lycfight/SWE-bench_oracle_5 · Datasets at Hugging Face

repo stringclasses 5 values	instance_id stringclasses 5 values	base_commit stringclasses 5 values	patch stringclasses 5 values	problem_statement stringclasses 5 values	hints_text stringclasses 4 values	created_at stringdate 2019-02-24 16:30:32 2023-08-02 16:35:14	FAIL_TO_PASS stringclasses 1 value	PASS_TO_PASS stringclasses 1 value
PrefectHQ/prefect	PrefectHQ__prefect-856	5739feb6eaac6a62e090e5c4590873d4474823bb	diff --git a/setup.py b/setup.py --- a/setup.py +++ b/setup.py @@ -17,6 +17,7 @@ "google-cloud-storage >= 1.13, < 2.0", ], "kubernetes": ["dask-kubernetes == 0.7.0", "kubernetes >= 8.0.1, < 9.0"], + "rss": ["feedparser >= 5.0.1, < 6.0"], "templates": ["jinja2 >= 2.0, < 3.0"], "viz": ["graphviz >= 0.8.3"], "twitter": ["tweepy >= 3.5, < 4.0"], diff --git a/src/prefect/tasks/rss/__init__.py b/src/prefect/tasks/rss/__init__.py new file mode 100644 --- /dev/null +++ b/src/prefect/tasks/rss/__init__.py @@ -0,0 +1,9 @@ +""" +Tasks for interacting with RSS feeds. +""" +try: + from prefect.tasks.rss.feed import ParseRSSFeed +except ImportError: + raise ImportError( + 'Using `prefect.tasks.rss` requires Prefect to be installed with the "rss" extra.' + ) diff --git a/src/prefect/tasks/rss/feed.py b/src/prefect/tasks/rss/feed.py new file mode 100644 --- /dev/null +++ b/src/prefect/tasks/rss/feed.py @@ -0,0 +1,42 @@ +from typing import Any + +import feedparser + +from prefect import Task +from prefect.utilities.tasks import defaults_from_attrs + + +class ParseRSSFeed(Task): + """ + Task for parsing RSS feeds. + + Args: + - feed_url (str): A remote URL pointing to an RSS feed + - kwargs (dict, optional): additional keyword arguments to pass to the Task + constructor + """ + + def __init__(self, feed_url: str = None, kwargs: Any): + self.feed_url = feed_url + + super().__init__(**kwargs) + + @defaults_from_attrs("feed_url") + def run(self, feed_url: str = None) -> "feedparser.FeedParserDict": + """ + Task run method. + + Args: + - feed_url (str): A remote URL pointing to an RSS feed + + Return: + - FeedParserDict: A dictionary representing the information from the + parsed feed. The object is accessable through indexing and attributes. + + Raises: + - ValueError: if `feed_url` is `None` + """ + if not feed_url: + raise ValueError("The feed_url must be provided.") + + return feedparser.parse(feed_url)	Create RSS Tasks for Task Library		2019-03-26T19:27:14Z	[]	[]
Qiskit/qiskit	Qiskit__qiskit-1856	240398505c6df0308992e697b4ad26c611faf248	diff --git a/examples/python/hello_quantum.py b/examples/python/hello_quantum.py --- a/examples/python/hello_quantum.py +++ b/examples/python/hello_quantum.py @@ -50,16 +50,17 @@ # Compile and run the Quantum Program on a real device backend try: least_busy_device = least_busy(IBMQ.backends(simulator=False)) - print("Running on current least busy device: ", least_busy_device) - - #running the job - job_exp = execute(qc, least_busy_device, shots=1024, max_credits=10) - result_exp = job_exp.result() - - # Show the results - print(result_exp.get_counts(qc)) except: print("All devices are currently unavailable.") + print("Running on current least busy device: ", least_busy_device) + + #running the job + job_exp = execute(qc, least_busy_device, shots=1024, max_credits=10) + result_exp = job_exp.result() + + # Show the results + print(result_exp.get_counts(qc)) + except QiskitError as ex: print('There was an error in the circuit!. Error = {}'.format(ex)) diff --git a/examples/python/rippleadd.py b/examples/python/rippleadd.py --- a/examples/python/rippleadd.py +++ b/examples/python/rippleadd.py @@ -13,7 +13,8 @@ """ from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit -from qiskit import compile, BasicAer +from qiskit import BasicAer +from qiskit import execute ############################################################### # Set the backend name and coupling map. @@ -76,14 +77,12 @@ def unmajority(p, a, b, c): ############################################################### # First version: not mapped -qobj = compile(qc, backend=backend, coupling_map=None, shots=1024) -job = backend.run(qobj) +job = execute(qc, backend=backend, coupling_map=None, shots=1024) result = job.result() print(result.get_counts(qc)) # Second version: mapped to 2x8 array coupling graph -qobj = compile(qc, backend=backend, coupling_map=coupling_map, shots=1024) -job = backend.run(qobj) +job = execute(qc, backend=backend, coupling_map=coupling_map, shots=1024) result = job.result() print(result.get_counts(qc)) diff --git a/examples/python/teleport.py b/examples/python/teleport.py --- a/examples/python/teleport.py +++ b/examples/python/teleport.py @@ -13,7 +13,8 @@ """ from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit -from qiskit import compile, BasicAer +from qiskit import BasicAer +from qiskit import execute ############################################################### # Set the backend name and coupling map. @@ -58,20 +59,14 @@ ############################################################### # First version: not mapped -initial_layout = {("q", 0): ("q", 0), ("q", 1): ("q", 1), - ("q", 2): ("q", 2)} -qobj = compile(qc, backend=backend, coupling_map=None, shots=1024, initial_layout=initial_layout) -job = backend.run(qobj) -qobj_exp = qobj.experiments[0] +initial_layout = [q[0], q[1], q[2]] +job = execute(qc, backend=backend, coupling_map=None, shots=1024, initial_layout=initial_layout) result = job.result() print(result.get_counts(qc)) # Second version: mapped to 2x8 array coupling graph -qobj = compile(qc, backend=backend, coupling_map=coupling_map, shots=1024,initial_layout=initial_layout) -qobj_exp = qobj.experiments[0] -qobj_exp.header.compiled_circuit_qasm = "" -job = backend.run(qobj) +job = execute(qc, backend=backend, coupling_map=coupling_map, shots=1024,initial_layout=initial_layout) result = job.result() print(result.get_counts(qc)) # Both versions should give the same distribution diff --git a/examples/python/using_qiskit_terra_level_0.py b/examples/python/using_qiskit_terra_level_0.py --- a/examples/python/using_qiskit_terra_level_0.py +++ b/examples/python/using_qiskit_terra_level_0.py @@ -68,18 +68,20 @@ try: # select least busy available device and execute. least_busy_device = least_busy(IBMQ.backends(simulator=False)) - print("Running on current least busy device: ", least_busy_device) + except: + print("All devices are currently unavailable.") - # running the job - job_exp = execute([qc1, qc2], backend=least_busy_device, shots=1024, max_credits=10) + print("Running on current least busy device: ", least_busy_device) - job_monitor(job_exp) - exp_result = job_exp.result() + # running the job + job_exp = execute([qc1, qc2], backend=least_busy_device, shots=1024, max_credits=10) + + job_monitor(job_exp) + exp_result = job_exp.result() + + # Show the results + print(exp_result.get_counts(qc1)) + print(exp_result.get_counts(qc2)) - # Show the results - print(exp_result.get_counts(qc1)) - print(exp_result.get_counts(qc2)) - except: - print("All devices are currently unavailable.") except QiskitError as ex: print('There was an error in the circuit!. Error = {}'.format(ex)) diff --git a/examples/python/using_qiskit_terra_level_1.py b/examples/python/using_qiskit_terra_level_1.py --- a/examples/python/using_qiskit_terra_level_1.py +++ b/examples/python/using_qiskit_terra_level_1.py @@ -8,20 +8,28 @@ """ Example showing how to use Qiskit at level 1 (intermediate). -This example shows how an intermediate user interacts with Terra. It builds some circuits -and compiles them from compile parameters. It makes a qobj object which is just and container to be -run on a backend. The same qobj can run on many backends (as shown). It is the -user responsibility to make sure it can be run. This is useful when you want to compare the same -circuits on different backends or change the compile parameters. - -To control the passes and we have a pass manager for level 2 user. +This example shows how an intermediate user interacts with Terra. +It builds some circuits and transpiles them with transpile options. +It then makes a qobj object which is just a container to be run on a backend. +The same qobj can be submitted to many backends (as shown). +It is the user's responsibility to make sure it can be run (i.e. it conforms +to the restrictions of the backend, if any). +This is useful when you want to compare the same +circuit on different backends without recompiling the whole circuit, +or just want to change some runtime parameters. + +To control the passes that transform the circuit, we have a pass manager +for the level 2 user. """ import pprint, time # Import the Qiskit modules -from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, QiskitError -from qiskit import compile, IBMQ, BasicAer +from qiskit import IBMQ, BasicAer +from qiskit import QiskitError +from qiskit.circuit import QuantumCircuit, ClassicalRegister, QuantumRegister +from qiskit.compiler import transpile, assemble_circuits +from qiskit.compiler import TranspileConfig, RunConfig from qiskit.providers.ibmq import least_busy from qiskit.tools.monitor import job_monitor @@ -33,7 +41,7 @@ For now, there's only access to local simulator backends...""") try: - # Create a Quantum and Classical Register and giving a name. + # Create a Quantum and Classical Register and give them names. qubit_reg = QuantumRegister(2, name='q') clbit_reg = ClassicalRegister(2, name='c') @@ -52,31 +60,48 @@ print("(Aer Backends)") for backend in BasicAer.backends(): print(backend.status()) - my_backend = BasicAer.get_backend('qasm_simulator') + qasm_simulator = BasicAer.get_backend('qasm_simulator') print("(QASM Simulator configuration) ") - pprint.pprint(my_backend.configuration()) + pprint.pprint(qasm_simulator.configuration()) print("(QASM Simulator properties) ") - pprint.pprint(my_backend.properties()) + pprint.pprint(qasm_simulator.properties()) - print("\n(IMQ Backends)") + # Compile and run the circuit on a real device backend + # See a list of available remote backends + print("\n(IBMQ Backends)") for backend in IBMQ.backends(): print(backend.status()) - # select least busy available device and execute. - least_busy_device = least_busy(IBMQ.backends(simulator=False)) + try: + # select least busy available device and execute. + least_busy_device = least_busy(IBMQ.backends(simulator=False)) + except: + print("All devices are currently unavailable.") + print("Running on current least busy device: ", least_busy_device) print("(with configuration) ") pprint.pprint(least_busy_device.configuration()) print("(with properties) ") pprint.pprint(least_busy_device.properties()) + # Transpile the circuits to make them compatible with the experimental backend + [qc1_new, qc2_new] = transpile(circuits=[qc1, qc2], + transpile_config=TranspileConfig(backend=least_busy_device)) + print("Bell circuit before transpile:") + print(qc1) + print("Bell circuit after transpile:") + print(qc1_new) + print("Superposition circuit before transpile:") + print(qc2) + print("Superposition circuit after transpile:") + print(qc2_new) - # Compiling the job for the experimental backend - qobj = compile([qc1, qc2], backend=least_busy_device, shots=1024, max_credits=10) + # Assemble the two circuits into a runnable qobj + qobj = assemble_circuits([qc1_new, qc2_new], run_config=RunConfig(shots=1000)) - # Running the job - sim_job = my_backend.run(qobj) + # Running qobj on the simulator + sim_job = qasm_simulator.run(qobj) # Getting the result sim_result=sim_job.result() @@ -85,20 +110,15 @@ print(sim_result.get_counts(qc1)) print(sim_result.get_counts(qc2)) - # Compile and run the Quantum Program on a real device backend - # See a list of available remote backends - try: - # Running the job. - exp_job = least_busy_device.run(qobj) + # Running the job. + exp_job = least_busy_device.run(qobj) - job_monitor(exp_job) - exp_result = exp_job.result() + job_monitor(exp_job) + exp_result = exp_job.result() - # Show the results - print(exp_result.get_counts(qc1)) - print(exp_result.get_counts(qc2)) - except: - print("All devices are currently unavailable.") + # Show the results + print(exp_result.get_counts(qc1)) + print(exp_result.get_counts(qc2)) except QiskitError as ex: print('There was an error in the circuit!. Error = {}'.format(ex)) diff --git a/qiskit/__init__.py b/qiskit/__init__.py --- a/qiskit/__init__.py +++ b/qiskit/__init__.py @@ -7,6 +7,7 @@ # pylint: disable=wrong-import-order + """Main Qiskit public functionality.""" import pkgutil @@ -21,7 +22,9 @@ from qiskit.circuit import ClassicalRegister from qiskit.circuit import QuantumRegister from qiskit.circuit import QuantumCircuit -from .tools.compiler import (compile, execute) +# pylint: disable=redefined-builtin +from qiskit.tools.compiler import compile # TODO remove after 0.8 +from qiskit.execute import (execute_circuits, execute) # The qiskit.extensions.x imports needs to be placed here due to the # mechanism for adding gates dynamically. @@ -38,12 +41,12 @@ # Please note these are global instances, not modules. from qiskit.providers.basicaer import BasicAer -# Try to import the Aer provider if the Aer element is installed. +# Try to import the Aer provider if installed. try: from qiskit.providers.aer import Aer except ImportError: pass -# Try to import the IBQM provider if the IBMQ element is installed. +# Try to import the IBMQ provider if installed. try: from qiskit.providers.ibmq import IBMQ except ImportError: diff --git a/qiskit/compiler/__init__.py b/qiskit/compiler/__init__.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/__init__.py @@ -0,0 +1,15 @@ +# -- coding: utf-8 -- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Helper module for Qiskit compiler. + +""" + +from .run_config import RunConfig +from .transpile_config import TranspileConfig +from .assembler import assemble_circuits +from .transpiler import transpile diff --git a/qiskit/compiler/assembler.py b/qiskit/compiler/assembler.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/assembler.py @@ -0,0 +1,122 @@ +# -- coding: utf-8 -- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Assemble function for converting a list of circuits into a qobj""" +import uuid +import sympy +import numpy + +from qiskit.circuit.quantumcircuit import QuantumCircuit +from qiskit.qobj import Qobj, QobjConfig, QobjExperiment, QobjInstruction, QobjHeader +from qiskit.qobj import QobjExperimentConfig, QobjExperimentHeader, QobjConditional +from qiskit.compiler.run_config import RunConfig +from qiskit.qobj.utils import QobjType + + +def assemble_circuits(circuits, run_config=None, qobj_header=None, qobj_id=None): + """Assembles a list of circuits into a qobj which can be run on the backend. + + Args: + circuits (list[QuantumCircuits] or QuantumCircuit): circuits to assemble + run_config (RunConfig): RunConfig object + qobj_header (QobjHeader): header to pass to the results + qobj_id (int): identifier for the generated qobj + + Returns: + Qobj: the Qobj to be run on the backends + """ + qobj_header = qobj_header or QobjHeader() + run_config = run_config or RunConfig() + if isinstance(circuits, QuantumCircuit): + circuits = [circuits] + + userconfig = QobjConfig(*run_config.to_dict()) + experiments = [] + max_n_qubits = 0 + max_memory_slots = 0 + for circuit in circuits: + # header stuff + n_qubits = 0 + memory_slots = 0 + qubit_labels = [] + clbit_labels = [] + + qreg_sizes = [] + creg_sizes = [] + for qreg in circuit.qregs: + qreg_sizes.append([qreg.name, qreg.size]) + for j in range(qreg.size): + qubit_labels.append([qreg.name, j]) + n_qubits += qreg.size + for creg in circuit.cregs: + creg_sizes.append([creg.name, creg.size]) + for j in range(creg.size): + clbit_labels.append([creg.name, j]) + memory_slots += creg.size + + # TODO: why do we need creq_sizes and qreg_sizes in header + # TODO: we need to rethink memory_slots as they are tied to classical bit + experimentheader = QobjExperimentHeader(qubit_labels=qubit_labels, + n_qubits=n_qubits, + qreg_sizes=qreg_sizes, + clbit_labels=clbit_labels, + memory_slots=memory_slots, + creg_sizes=creg_sizes, + name=circuit.name) + # TODO: why do we need n_qubits and memory_slots in both the header and the config + experimentconfig = QobjExperimentConfig(n_qubits=n_qubits, memory_slots=memory_slots) + + instructions = [] + for opt in circuit.data: + current_instruction = QobjInstruction(name=opt.name) + if opt.qargs: + qubit_indices = [qubit_labels.index([qubit[0].name, qubit[1]]) + for qubit in opt.qargs] + current_instruction.qubits = qubit_indices + if opt.cargs: + clbit_indices = [clbit_labels.index([clbit[0].name, clbit[1]]) + for clbit in opt.cargs] + current_instruction.memory = clbit_indices + + if opt.params: + params = list(map(lambda x: x.evalf(), opt.params)) + params = [sympy.matrix2numpy(x, dtype=complex) + if isinstance(x, sympy.Matrix) else x for x in params] + if len(params) == 1 and isinstance(params[0], numpy.ndarray): + # TODO: Aer expects list of rows for unitary instruction params; + # change to matrix in Aer. + params = params[0] + current_instruction.params = params + # TODO (jay): I really dont like this for snapshot. I also think we should change + # type to snap_type + if opt.name == "snapshot": + current_instruction.label = str(opt.params[0]) + current_instruction.type = str(opt.params[1]) + if opt.control: + mask = 0 + for clbit in clbit_labels: + if clbit[0] == opt.control[0].name: + mask \|= (1 << clbit_labels.index(clbit)) + + current_instruction.conditional = QobjConditional(mask="0x%X" % mask, + type='equals', + val="0x%X" % opt.control[1]) + + instructions.append(current_instruction) + experiments.append(QobjExperiment(instructions=instructions, header=experimentheader, + config=experimentconfig)) + if n_qubits > max_n_qubits: + max_n_qubits = n_qubits + if memory_slots > max_memory_slots: + max_memory_slots = memory_slots + + userconfig.memory_slots = max_memory_slots + userconfig.n_qubits = max_n_qubits + + return Qobj(qobj_id=qobj_id or str(uuid.uuid4()), config=userconfig, + experiments=experiments, header=qobj_header, + type=QobjType.QASM.value) diff --git a/qiskit/compiler/compile.py b/qiskit/compiler/compile.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/compile.py @@ -0,0 +1,75 @@ +# -- coding: utf-8 -- + +# Copyright 2018, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Helper module for simplified Qiskit usage.""" +import warnings +import logging + +from qiskit import transpiler +from qiskit.converters import circuits_to_qobj +from qiskit.compiler import RunConfig +from qiskit.qobj import QobjHeader +from qiskit.mapper import Layout + + +logger = logging.getLogger(__name__) + + +# pylint: disable=redefined-builtin +def compile(circuits, backend, + config=None, basis_gates=None, coupling_map=None, initial_layout=None, + shots=1024, max_credits=10, seed=None, qobj_id=None, seed_mapper=None, + pass_manager=None, memory=False): + """Compile a list of circuits into a qobj. + + Args: + circuits (QuantumCircuit or list[QuantumCircuit]): circuits to compile + backend (BaseBackend): a backend to compile for + config (dict): dictionary of parameters (e.g. noise) used by runner + basis_gates (list[str]): list of basis gates names supported by the + target. Default: ['u1','u2','u3','cx','id'] + coupling_map (list): coupling map (perhaps custom) to target in mapping + initial_layout (list): initial layout of qubits in mapping + shots (int): number of repetitions of each circuit, for sampling + max_credits (int): maximum credits to use + seed (int): random seed for simulators + seed_mapper (int): random seed for swapper mapper + qobj_id (int): identifier for the generated qobj + pass_manager (PassManager): a pass manger for the transpiler pipeline + memory (bool): if True, per-shot measurement bitstrings are returned as well + + Returns: + Qobj: the qobj to be run on the backends + + Raises: + QiskitError: if the desired options are not supported by backend + """ + if config: + warnings.warn('The `config` argument is deprecated and ' + 'does not do anything', DeprecationWarning) + + if initial_layout is not None and not isinstance(initial_layout, Layout): + initial_layout = Layout(initial_layout) + + circuits = transpiler.transpile(circuits, backend, basis_gates, coupling_map, initial_layout, + seed_mapper, pass_manager) + + # step 4: Making a qobj + run_config = RunConfig() + + if seed: + run_config.seed = seed + if shots: + run_config.shots = shots + if max_credits: + run_config.max_credits = max_credits + if memory: + run_config.memory = memory + qobj = circuits_to_qobj(circuits, qobj_header=QobjHeader(), run_config=run_config, + qobj_id=qobj_id) + + return qobj diff --git a/qiskit/compiler/models.py b/qiskit/compiler/models.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/models.py @@ -0,0 +1,35 @@ +# -- coding: utf-8 -- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Models for RunConfig and its related components.""" + +from marshmallow.validate import Range + +from qiskit.validation import BaseSchema +from qiskit.validation.fields import Boolean, Integer + + +class TranspileConfigSchema(BaseSchema): + """Schema for TranspileConfig.""" + + # Required properties. + # None + + # Optional properties. + + +class RunConfigSchema(BaseSchema): + """Schema for RunConfig.""" + + # Required properties. + # None + + # Optional properties. + shots = Integer(validate=Range(min=1)) + max_credits = Integer(validate=Range(min=3, max=10)) # TODO: can we check the range + seed = Integer() + memory = Boolean() # set default to be False diff --git a/qiskit/qobj/run_config.py b/qiskit/compiler/run_config.py similarity index 60% rename from qiskit/qobj/run_config.py rename to qiskit/compiler/run_config.py --- a/qiskit/qobj/run_config.py +++ b/qiskit/compiler/run_config.py @@ -7,23 +7,8 @@ """Models for RunConfig and its related components.""" -from marshmallow.validate import Range - -from qiskit.validation import BaseModel, BaseSchema, bind_schema -from qiskit.validation.fields import Boolean, Integer - - -class RunConfigSchema(BaseSchema): - """Schema for RunConfig.""" - - # Required properties. - # None - - # Optional properties. - shots = Integer(validate=Range(min=1)) - max_credits = Integer(validate=Range(min=3, max=10)) # TODO: can we check the range - seed = Integer() - memory = Boolean() # set default to be False +from qiskit.compiler.models import RunConfigSchema +from qiskit.validation import BaseModel, bind_schema @bind_schema(RunConfigSchema) diff --git a/qiskit/compiler/transpile_config.py b/qiskit/compiler/transpile_config.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/transpile_config.py @@ -0,0 +1,23 @@ +# -- coding: utf-8 -- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Models for TranspileConfig and its related components.""" + +from qiskit.compiler.models import TranspileConfigSchema +from qiskit.validation import BaseModel, bind_schema + + +@bind_schema(TranspileConfigSchema) +class TranspileConfig(BaseModel): + """Model for TranspileConfig. + + Please note that this class only describes the required fields. For the + full description of the model, please check ``TranspileConfigSchema``. + + Attributes: + + """ diff --git a/qiskit/compiler/transpiler.py b/qiskit/compiler/transpiler.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/transpiler.py @@ -0,0 +1,50 @@ +# -- coding: utf-8 -- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Circuit transpile function """ +import logging + +from qiskit import transpiler +from qiskit.mapper import Layout + + +logger = logging.getLogger(__name__) + + +def transpile(circuits, transpile_config=None): + """Compile a list of circuits into a list of optimized circuits. + + Args: + circuits (QuantumCircuit or list[QuantumCircuit]): circuits to compile + transpile_config (TranspileConfig): configuration for the transpiler + + Returns: + circuits: the optimized circuits + """ + + # ------------ + # TODO: This is a hack while we are still using the old transpiler. + initial_layout = getattr(transpile_config, 'initial_layout', None) + basis_gates = getattr(transpile_config, 'basis_gates', None) + coupling_map = getattr(transpile_config, 'coupling_map', None) + seed_mapper = getattr(transpile_config, 'seed_mapper', None) + + if initial_layout is not None and not isinstance(initial_layout, Layout): + initial_layout = Layout(initial_layout) + + pass_manager = None + backend = transpile_config.backend + new_circuits = transpiler.transpile(circuits, backend, basis_gates, coupling_map, + initial_layout, seed_mapper, pass_manager) + # --------- + + # THE IDEAL CODE HERE WILL BE. + # 1 set up the pass_manager using transpile_config options + # pass_manager = PassManager(TranspileConig) + # run the passes + # new_circuits = pass_manager.run(circuits) + return new_circuits diff --git a/qiskit/converters/circuits_to_qobj.py b/qiskit/converters/circuits_to_qobj.py --- a/qiskit/converters/circuits_to_qobj.py +++ b/qiskit/converters/circuits_to_qobj.py @@ -6,19 +6,14 @@ # the LICENSE.txt file in the root directory of this source tree. """Compile function for converting a list of circuits to the qobj""" -import uuid import warnings -import sympy -import numpy -from qiskit.circuit.quantumcircuit import QuantumCircuit -from qiskit.qobj import Qobj, QobjConfig, QobjExperiment, QobjInstruction, QobjHeader -from qiskit.qobj import QobjExperimentConfig, QobjExperimentHeader, QobjConditional -from qiskit.qobj.run_config import RunConfig -from qiskit.qobj.utils import QobjType +from qiskit.qobj import QobjHeader +from qiskit.compiler.run_config import RunConfig +from qiskit.compiler import assemble_circuits -def circuits_to_qobj(circuits, user_qobj_header=None, run_config=None, +def circuits_to_qobj(circuits, qobj_header=None, run_config=None, qobj_id=None, backend_name=None, config=None, shots=None, max_credits=None, basis_gates=None, @@ -27,10 +22,10 @@ def circuits_to_qobj(circuits, user_qobj_header=None, run_config=None, Args: circuits (list[QuantumCircuits] or QuantumCircuit): circuits to compile - user_qobj_header (QobjHeader): header to pass to the results + qobj_header (QobjHeader): header to pass to the results run_config (RunConfig): RunConfig object - qobj_id (int): identifier for the generated qobj + qobj_id (int): TODO: delete after qiskit-terra 0.8 backend_name (str): TODO: delete after qiskit-terra 0.8 config (dict): TODO: delete after qiskit-terra 0.8 shots (int): TODO: delete after qiskit-terra 0.8 @@ -43,14 +38,16 @@ def circuits_to_qobj(circuits, user_qobj_header=None, run_config=None, Returns: Qobj: the Qobj to be run on the backends """ - user_qobj_header = user_qobj_header or QobjHeader() + warnings.warn('circuits_to_qobj is deprecated and will be removed in Qiskit Terra 0.9. ' + 'Use qiskit.compiler.assemble_circuits() to serialize circuits into a qobj.', + DeprecationWarning) + + qobj_header = qobj_header or QobjHeader() run_config = run_config or RunConfig() - if isinstance(circuits, QuantumCircuit): - circuits = [circuits] if backend_name: warnings.warn('backend_name is not required anymore', DeprecationWarning) - user_qobj_header.backend_name = backend_name + qobj_header.backend_name = backend_name if config: warnings.warn('config is not used anymore. Set all configs in ' 'run_config.', DeprecationWarning) @@ -70,90 +67,9 @@ def circuits_to_qobj(circuits, user_qobj_header=None, run_config=None, if max_credits: warnings.warn('max_credits is not used anymore. Set it via run_config', DeprecationWarning) run_config.max_credits = max_credits + if qobj_id: + warnings.warn('qobj_id is not used anymore', DeprecationWarning) - userconfig = QobjConfig(run_config.to_dict()) - experiments = [] - max_n_qubits = 0 - max_memory_slots = 0 - for circuit in circuits: - # header stuff - n_qubits = 0 - memory_slots = 0 - qubit_labels = [] - clbit_labels = [] - - qreg_sizes = [] - creg_sizes = [] - for qreg in circuit.qregs: - qreg_sizes.append([qreg.name, qreg.size]) - for j in range(qreg.size): - qubit_labels.append([qreg.name, j]) - n_qubits += qreg.size - for creg in circuit.cregs: - creg_sizes.append([creg.name, creg.size]) - for j in range(creg.size): - clbit_labels.append([creg.name, j]) - memory_slots += creg.size - - # TODO: why do we need creq_sizes and qreg_sizes in header - # TODO: we need to rethink memory_slots as they are tied to classical bit - experimentheader = QobjExperimentHeader(qubit_labels=qubit_labels, - n_qubits=n_qubits, - qreg_sizes=qreg_sizes, - clbit_labels=clbit_labels, - memory_slots=memory_slots, - creg_sizes=creg_sizes, - name=circuit.name) - # TODO: why do we need n_qubits and memory_slots in both the header and the config - experimentconfig = QobjExperimentConfig(n_qubits=n_qubits, memory_slots=memory_slots) - - instructions = [] - for opt in circuit.data: - current_instruction = QobjInstruction(name=opt.name) - if opt.qargs: - qubit_indices = [qubit_labels.index([qubit[0].name, qubit[1]]) - for qubit in opt.qargs] - current_instruction.qubits = qubit_indices - if opt.cargs: - clbit_indices = [clbit_labels.index([clbit[0].name, clbit[1]]) - for clbit in opt.cargs] - current_instruction.memory = clbit_indices - - if opt.params: - params = list(map(lambda x: x.evalf(), opt.params)) - params = [sympy.matrix2numpy(x, dtype=complex) - if isinstance(x, sympy.Matrix) else x for x in params] - if len(params) == 1 and isinstance(params[0], numpy.ndarray): - # TODO: Aer expects list of rows for unitary instruction params; - # change to matrix in Aer. - params = params[0] - current_instruction.params = params - # TODO: I really dont like this for snapshot. I also think we should change - # type to snap_type - if opt.name == "snapshot": - current_instruction.label = str(opt.params[0]) - current_instruction.type = str(opt.params[1]) - if opt.control: - mask = 0 - for clbit in clbit_labels: - if clbit[0] == opt.control[0].name: - mask \|= (1 << clbit_labels.index(clbit)) - - current_instruction.conditional = QobjConditional(mask="0x%X" % mask, - type='equals', - val="0x%X" % opt.control[1]) - - instructions.append(current_instruction) - experiments.append(QobjExperiment(instructions=instructions, header=experimentheader, - config=experimentconfig)) - if n_qubits > max_n_qubits: - max_n_qubits = n_qubits - if memory_slots > max_memory_slots: - max_memory_slots = memory_slots - - userconfig.memory_slots = max_memory_slots - userconfig.n_qubits = max_n_qubits + qobj = assemble_circuits(circuits, qobj_header, run_config) - return Qobj(qobj_id=qobj_id or str(uuid.uuid4()), config=userconfig, - experiments=experiments, header=user_qobj_header, - type=QobjType.QASM.value) + return qobj diff --git a/qiskit/execute.py b/qiskit/execute.py new file mode 100644 --- /dev/null +++ b/qiskit/execute.py @@ -0,0 +1,133 @@ +# -- coding: utf-8 -- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +""" +Helper module for simplified Qiskit usage. + +This module includes + execute_circuits: compile and run a list of quantum circuits. + execute: simplified usage of either execute_circuits or execute_schedules + +In general we recommend using the SDK functions directly. However, to get something +running quickly we have provider this wrapper module. +""" + +import logging +import warnings + +from qiskit.compiler import assemble_circuits, transpile +from qiskit.compiler import RunConfig, TranspileConfig +from qiskit.qobj import QobjHeader + +logger = logging.getLogger(__name__) + + +def execute(circuits, backend, qobj_header=None, config=None, basis_gates=None, + coupling_map=None, initial_layout=None, shots=1024, max_credits=10, + seed=None, qobj_id=None, seed_mapper=None, pass_manager=None, + memory=False, kwargs): + """Executes a set of circuits. + + Args: + circuits (QuantumCircuit or list[QuantumCircuit]): circuits to execute + backend (BaseBackend): a backend to execute the circuits on + qobj_header (QobjHeader): user input to go into the header + config (dict): dictionary of parameters (e.g. noise) used by runner + basis_gates (list[str]): list of basis gate names supported by the + target. Default: ['u1','u2','u3','cx','id'] + coupling_map (list): coupling map (perhaps custom) to target in mapping + initial_layout (list): initial layout of qubits in mapping + shots (int): number of repetitions of each circuit, for sampling + max_credits (int): maximum credits to use + seed (int): random seed for simulators + seed_mapper (int): random seed for swapper mapper + qobj_id (int): identifier for the generated qobj + pass_manager (PassManager): a pass manger for the transpiler pipeline + memory (bool): if True, per-shot measurement bitstrings are returned as well. + kwargs: extra arguments used by AER for running configurable backends. + Refer to the backend documentation for details on these arguments + + Returns: + BaseJob: returns job instance derived from BaseJob + """ + + transpile_config = TranspileConfig() + run_config = RunConfig() + + if config: + warnings.warn('config is deprecated in terra 0.8', DeprecationWarning) + if qobj_id: + warnings.warn('qobj_id is deprecated in terra 0.8', DeprecationWarning) + if basis_gates: + transpile_config.basis_gate = basis_gates + if coupling_map: + transpile_config.coupling_map = coupling_map + if initial_layout: + transpile_config.initial_layout = initial_layout + if seed_mapper: + transpile_config.seed_mapper = seed_mapper + if shots: + run_config.shots = shots + if max_credits: + run_config.max_credits = max_credits + if seed: + run_config.seed = seed + if memory: + run_config.memory = memory + if pass_manager: + warnings.warn('pass_manager in the execute function is deprecated in terra 0.8.', + DeprecationWarning) + + job = execute_circuits(circuits, backend, qobj_header=qobj_header, + run_config=run_config, + transpile_config=transpile_config, kwargs) + + return job + + +def execute_circuits(circuits, backend, qobj_header=None, + transpile_config=None, run_config=None, kwargs): + """Executes a list of circuits. + + Args: + circuits (QuantumCircuit or list[QuantumCircuit]): circuits to execute + backend (BaseBackend): a backend to execute the circuits on + qobj_header (QobjHeader): User input to go in the header + transpile_config (TranspileConfig): Configurations for the transpiler + run_config (RunConfig): Run Configuration + kwargs: extra arguments used by AER for running configurable backends. + Refer to the backend documentation for details on these arguments + + Returns: + BaseJob: returns job instance derived from BaseJob + """ + + # TODO: a hack, remove when backend is not needed in transpile + # ------ + transpile_config = transpile_config or TranspileConfig() + transpile_config.backend = backend + # ------ + + # filling in the header with the backend name the qobj was run on + qobj_header = qobj_header or QobjHeader() + qobj_header.backend_name = backend.name() + + # default values + if not run_config: + # TODO remove max_credits from the default when it is not + # required by by the backend. + run_config = RunConfig(shots=1024, max_credits=10, memory=False) + + # transpiling the circuits using the transpiler_config + new_circuits = transpile(circuits, transpile_config=transpile_config) + + # assembling the circuits into a qobj to be run on the backend + qobj = assemble_circuits(new_circuits, qobj_header=qobj_header, + run_config=run_config) + + # executing the circuits on the backend and returning the job + return backend.run(qobj, kwargs) diff --git a/qiskit/qobj/__init__.py b/qiskit/qobj/__init__.py --- a/qiskit/qobj/__init__.py +++ b/qiskit/qobj/__init__.py @@ -11,6 +11,5 @@ from .models import (QobjConfig, QobjExperiment, QobjInstruction, QobjHeader, QobjExperimentHeader, QobjConditional, QobjExperimentConfig) from .exceptions import QobjValidationError -from .run_config import RunConfig from ._validation import validate_qobj_against_schema diff --git a/qiskit/qobj/qobj.py b/qiskit/qobj/qobj.py --- a/qiskit/qobj/qobj.py +++ b/qiskit/qobj/qobj.py @@ -20,9 +20,9 @@ """Current version of the Qobj schema. Qobj schema versions: - 1.1.0: Qiskit 0.8 -* 1.0.0: Qiskit 0.6 -* 0.0.1: Qiskit 0.5.x format (pre-schemas). +* 1.1.0: Qiskit Terra 0.8 +* 1.0.0: Qiskit Terra 0.6 +* 0.0.1: Qiskit Terra 0.5.x format (pre-schemas). """ diff --git a/qiskit/tools/__init__.py b/qiskit/tools/__init__.py --- a/qiskit/tools/__init__.py +++ b/qiskit/tools/__init__.py @@ -16,4 +16,4 @@ """ from .parallel import parallel_map -from .compiler import (compile, execute) +from .compiler import compile diff --git a/qiskit/tools/compiler.py b/qiskit/tools/compiler.py --- a/qiskit/tools/compiler.py +++ b/qiskit/tools/compiler.py @@ -5,17 +5,14 @@ # This source code is licensed under the Apache License, Version 2.0 found in # the LICENSE.txt file in the root directory of this source tree. -"""Helper module for simplified Qiskit usage.""" +"""Helper module for simplified Qiskit usage. THIS WILL BE REMOVED IN AFTER 0.8.""" import warnings import logging +from qiskit.compiler import assemble_circuits, RunConfig from qiskit import transpiler -from qiskit.converters import circuits_to_qobj -from qiskit.qobj import RunConfig -from qiskit.qobj import QobjHeader from qiskit.mapper import Layout - logger = logging.getLogger(__name__) @@ -48,64 +45,32 @@ def compile(circuits, backend, Raises: QiskitError: if the desired options are not supported by backend """ - if config: - warnings.warn('The `config` argument is deprecated and ' - 'does not do anything', DeprecationWarning) - - if initial_layout is not None and not isinstance(initial_layout, Layout): - initial_layout = Layout(initial_layout) - - circuits = transpiler.transpile(circuits, backend, basis_gates, coupling_map, initial_layout, - seed_mapper, pass_manager) + warnings.warn('qiskit.compile() is deprecated and will be removed in Qiskit Terra 0.9. ' + 'Please use qiskit.transpile() to transform circuits ' + 'and qiskit.assemble_circuits() to produce qobj.', + DeprecationWarning) - # step 4: Making a qobj run_config = RunConfig() - if seed: - run_config.seed = seed + if config: + warnings.warn('config is not used anymore. Set all configs in ' + 'run_config.', DeprecationWarning) if shots: run_config.shots = shots if max_credits: run_config.max_credits = max_credits + if seed: + run_config.seed = seed if memory: run_config.memory = memory - qobj = circuits_to_qobj(circuits, user_qobj_header=QobjHeader(), run_config=run_config, - qobj_id=qobj_id) - - return qobj - -def execute(circuits, backend, config=None, basis_gates=None, coupling_map=None, - initial_layout=None, shots=1024, max_credits=10, seed=None, - qobj_id=None, seed_mapper=None, pass_manager=None, - memory=False, kwargs): - """Executes a set of circuits. - - Args: - circuits (QuantumCircuit or list[QuantumCircuit]): circuits to execute - backend (BaseBackend): a backend to execute the circuits on - config (dict): dictionary of parameters (e.g. noise) used by runner - basis_gates (list[str]): list of basis gate names supported by the - target. Default: ['u1','u2','u3','cx','id'] - coupling_map (list): coupling map (perhaps custom) to target in mapping - initial_layout (list): initial layout of qubits in mapping - shots (int): number of repetitions of each circuit, for sampling - max_credits (int): maximum credits to use - seed (int): random seed for simulators - seed_mapper (int): random seed for swapper mapper - qobj_id (int): identifier for the generated qobj - pass_manager (PassManager): a pass manger for the transpiler pipeline - memory (bool): if True, per-shot measurement bitstrings are returned as well. - kwargs: extra arguments used by AER for running configurable backends. - Refer to the backend documentation for details on these arguments + if initial_layout is not None and not isinstance(initial_layout, Layout): + initial_layout = Layout(initial_layout) - Returns: - BaseJob: returns job instance derived from BaseJob - """ + new_circuits = transpiler.transpile(circuits, backend, basis_gates, coupling_map, + initial_layout, seed_mapper, pass_manager) - qobj = compile(circuits, backend, - config, basis_gates, coupling_map, initial_layout, - shots, max_credits, seed, qobj_id, seed_mapper, - pass_manager, memory) + qobj = assemble_circuits(new_circuits, qobj_header=None, run_config=run_config, + qobj_id=qobj_id) - return backend.run(qobj, kwargs) + return qobj	Allow for setting qobj header description in execute and propagate to job <!-- ⚠️ If you do not respect this template, your issue will be closed --> <!-- ⚠️ Make sure to browse the opened and closed issues to confirm this idea does not exist. --> ### What is the expected enhancement? The Qobj accepts a description in the header that allows the user to label the qobj with whatever message they want, e.g. `'cool_exp_v1'`. However, this can not be set unless one first does `compile` and then manually sets the description. Because the user never sees the qobj (and perhaps never should since it is just a fancy box) when using `execute` it is impossible to save this info. In addition, if I try to grab a job by the description (see #1710), or some substring in description, to the best of my knowledge I need to load all the jobs, and then make another API call for each job to load the qobj and then search the header description. It would be nice if the qobj header description could be set in execute, and then this description gets added to the resulting job, e.g. `job.description` so that I can search jobs by this value without needing to load qobjs.	This is good but it has to be done after the update to the compile interfaces. I will try and do it with that.	2019-02-24T16:30:32Z	[]	[]
apache/airflow	apache__airflow-33043	d989e9dba5899e87780df9a8c5994ed1e3f8a776	diff --git a/airflow/auth/managers/fab/views/roles_list.py b/airflow/auth/managers/fab/views/roles_list.py new file mode 100644 --- /dev/null +++ b/airflow/auth/managers/fab/views/roles_list.py @@ -0,0 +1,42 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +from __future__ import annotations + +from flask_appbuilder.security.views import RoleModelView + +from airflow.security import permissions + + +class CustomRoleModelView(RoleModelView): + """Customize permission names for FAB's builtin RoleModelView.""" + + class_permission_name = permissions.RESOURCE_ROLE + method_permission_name = { + "delete": "delete", + "download": "read", + "show": "read", + "list": "read", + "edit": "edit", + "add": "create", + "copy_role": "create", + } + base_permissions = [ + permissions.ACTION_CAN_CREATE, + permissions.ACTION_CAN_READ, + permissions.ACTION_CAN_EDIT, + permissions.ACTION_CAN_DELETE, + ] diff --git a/airflow/www/fab_security/manager.py b/airflow/www/fab_security/manager.py --- a/airflow/www/fab_security/manager.py +++ b/airflow/www/fab_security/manager.py @@ -258,7 +258,7 @@ def create_builtin_roles(self): """Returns FAB builtin roles.""" return self.appbuilder.get_app.config.get("FAB_ROLES", {}) - def get_roles_from_keys(self, role_keys: list[str]) -> set[RoleModelView]: + def get_roles_from_keys(self, role_keys: list[str]) -> set[Role]: """ Construct a list of FAB role objects, from a list of keys. @@ -267,7 +267,7 @@ def get_roles_from_keys(self, role_keys: list[str]) -> set[RoleModelView]: - we use AUTH_ROLES_MAPPING to map from keys, to FAB role names :param role_keys: the list of FAB role keys - :return: a list of RoleModelView + :return: a list of Role """ _roles = set() _role_keys = set(role_keys) diff --git a/airflow/www/fab_security/views.py b/airflow/www/fab_security/views.py --- a/airflow/www/fab_security/views.py +++ b/airflow/www/fab_security/views.py @@ -20,7 +20,6 @@ from flask_appbuilder.security.views import ( PermissionModelView, PermissionViewModelView, - RoleModelView, ViewMenuModelView, ) from flask_babel import lazy_gettext @@ -72,27 +71,6 @@ class PermissionPairModelView(PermissionViewModelView): list_columns = ["action", "resource"] -class CustomRoleModelView(RoleModelView): - """Customize permission names for FAB's builtin RoleModelView.""" - - class_permission_name = permissions.RESOURCE_ROLE - method_permission_name = { - "delete": "delete", - "download": "read", - "show": "read", - "list": "read", - "edit": "edit", - "add": "create", - "copy_role": "create", - } - base_permissions = [ - permissions.ACTION_CAN_CREATE, - permissions.ACTION_CAN_READ, - permissions.ACTION_CAN_EDIT, - permissions.ACTION_CAN_DELETE, - ] - - class ResourceModelView(ViewMenuModelView): """Customize permission names for FAB's builtin ViewMenuModelView.""" diff --git a/airflow/www/security.py b/airflow/www/security.py --- a/airflow/www/security.py +++ b/airflow/www/security.py @@ -25,6 +25,7 @@ from sqlalchemy.orm import Session, joinedload from airflow.auth.managers.fab.models import Permission, Resource, Role, User +from airflow.auth.managers.fab.views.roles_list import CustomRoleModelView from airflow.auth.managers.fab.views.user import ( CustomUserDBModelView, CustomUserLDAPModelView, @@ -47,7 +48,6 @@ from airflow.www.fab_security.sqla.manager import SecurityManager from airflow.www.fab_security.views import ( ActionModelView, - CustomRoleModelView, PermissionPairModelView, ResourceModelView, )	AIP-56 - FAB AM - Role views Move role related views to FAB Auth manager: - List roles - Edit role - Create role - View role		2023-08-02T16:35:14Z	[]	[]
celery/celery	celery__celery-6629	c7f2f141627de69645d1885b000b12def97152ec	diff --git a/celery/backends/base.py b/celery/backends/base.py --- a/celery/backends/base.py +++ b/celery/backends/base.py @@ -853,7 +853,11 @@ def _store_result(self, task_id, result, state, if current_meta['status'] == states.SUCCESS: return result - self._set_with_state(self.get_key_for_task(task_id), self.encode(meta), state) + try: + self._set_with_state(self.get_key_for_task(task_id), self.encode(meta), state) + except BackendStoreError as ex: + raise BackendStoreError(str(ex), state=state, task_id=task_id) from ex + return result def _save_group(self, group_id, result): diff --git a/celery/backends/redis.py b/celery/backends/redis.py --- a/celery/backends/redis.py +++ b/celery/backends/redis.py @@ -12,7 +12,7 @@ from celery import states from celery._state import task_join_will_block from celery.canvas import maybe_signature -from celery.exceptions import ChordError, ImproperlyConfigured +from celery.exceptions import BackendStoreError, ChordError, ImproperlyConfigured from celery.result import GroupResult, allow_join_result from celery.utils.functional import dictfilter from celery.utils.log import get_logger @@ -192,6 +192,10 @@ class RedisBackend(BaseKeyValueStoreBackend, AsyncBackendMixin): supports_autoexpire = True supports_native_join = True + #: Maximal length of string value in Redis. + #: 512 MB - https://redis.io/topics/data-types + _MAX_STR_VALUE_SIZE = 536870912 + def __init__(self, host=None, port=None, db=None, password=None, max_connections=None, url=None, connection_pool=None, kwargs): @@ -364,6 +368,9 @@ def on_connection_error(self, max_retries, exc, intervals, retries): return tts def set(self, key, value, retry_policy): + if len(value) > self._MAX_STR_VALUE_SIZE: + raise BackendStoreError('value too large for Redis backend') + return self.ensure(self._set, (key, value), *retry_policy) def _set(self, key, value): diff --git a/celery/exceptions.py b/celery/exceptions.py --- a/celery/exceptions.py +++ b/celery/exceptions.py @@ -288,7 +288,7 @@ def __repr__(self): class BackendStoreError(BackendError): - """An issue writing from the backend.""" + """An issue writing to the backend.""" def __init__(self, args, **kwargs): self.state = kwargs.get('state', "")	Workers retry Redis connection when task result is larger than 512MB - retry is useless as it never fits to Redis <!-- Please fill this template entirely and do not erase parts of it. We reserve the right to close without a response bug reports which are incomplete. --> # Checklist <!-- To check an item on the list replace [ ] with [x]. --> - [x] I have verified that the issue exists against the `master` branch of Celery. - [ ] This has already been asked to the [discussion group](https://groups.google.com/forum/#!forum/celery-users) first. - [x] I have read the relevant section in the [contribution guide](http://docs.celeryproject.org/en/latest/contributing.html#other-bugs) on reporting bugs. - [x] I have checked the [issues list](https://github.com/celery/celery/issues?q=is%3Aissue+label%3A%22Issue+Type%3A+Bug+Report%22+-label%3A%22Category%3A+Documentation%22) for similar or identical bug reports. - [x] I have checked the [pull requests list](https://github.com/celery/celery/pulls?q=is%3Apr+label%3A%22PR+Type%3A+Bugfix%22+-label%3A%22Category%3A+Documentation%22) for existing proposed fixes. - [x] I have checked the [commit log](https://github.com/celery/celery/commits/master) to find out if the bug was already fixed in the master branch. - [x] I have included all related issues and possible duplicate issues in this issue (If there are none, check this box anyway). ## Mandatory Debugging Information - [x] I have included the output of ``celery -A proj report`` in the issue. (if you are not able to do this, then at least specify the Celery version affected). - [x] I have verified that the issue exists against the `master` branch of Celery. - [x] I have included the contents of ``pip freeze`` in the issue. - [x] I have included all the versions of all the external dependencies required to reproduce this bug. ## Optional Debugging Information <!-- Try some of the below if you think they are relevant. It will help us figure out the scope of the bug and how many users it affects. --> - [ ] I have tried reproducing the issue on more than one Python version and/or implementation. - [ ] I have tried reproducing the issue on more than one message broker and/or result backend. - [ ] I have tried reproducing the issue on more than one version of the message broker and/or result backend. - [ ] I have tried reproducing the issue on more than one operating system. - [ ] I have tried reproducing the issue on more than one workers pool. - [ ] I have tried reproducing the issue with autoscaling, retries, ETA/Countdown & rate limits disabled. - [ ] I have tried reproducing the issue after downgrading and/or upgrading Celery and its dependencies. ## Related Issues and Possible Duplicates <!-- Please make sure to search and mention any related issues or possible duplicates to this issue as requested by the checklist above. This may or may not include issues in other repositories that the Celery project maintains or other repositories that are dependencies of Celery. If you don't know how to mention issues, please refer to Github's documentation on the subject: https://help.github.com/en/articles/autolinked-references-and-urls#issues-and-pull-requests --> #### Related Issues - None #### Possible Duplicates - None ## Environment & Settings <!-- Include the contents of celery --version below --> Celery version: 5.0.4 (singularity) <!-- Include the output of celery -A proj report below --> <details> <summary><b><code>celery report</code> Output:</b></summary> <p> ``` software -> celery:5.0.4 (singularity) kombu:5.0.2 py:3.9.0 billiard:3.6.3.0 py-amqp:5.0.2 platform -> system:Linux arch:64bit, ELF kernel version:5.9.12-arch1-1 imp:CPython loader -> celery.loaders.app.AppLoader settings -> transport:amqp results:redis://:@:6379/10 broker_url: 'amqp://:******@*:5672/' result_backend: 'redis://:******@**:6379/10' deprecated_settings: None ``` </p> </details> # Steps to Reproduce ## Required Dependencies <!-- Please fill the required dependencies to reproduce this issue --> Minimal Python Version: N/A or Unknown * Minimal Celery Version: N/A or Unknown * Minimal Kombu Version: N/A or Unknown * Minimal Broker Version: N/A or Unknown * Minimal Result Backend Version: N/A or Unknown * Minimal OS and/or Kernel Version: N/A or Unknown * Minimal Broker Client Version: N/A or Unknown * Minimal Result Backend Client Version: N/A or Unknown ### Python Packages <!-- Please fill the contents of pip freeze below --> <details> <summary><b><code>pip freeze</code> Output:</b></summary> <p> ``` amqp==5.0.2 billiard==3.6.3.0 celery @ git+https://github.com/celery/celery.git@420e3931a63538bd225ef57916deccf53cbcb57a // == master, tried also 5.0.4 click==7.1.2 click-didyoumean==0.0.3 click-plugins==1.1.1 click-repl==0.1.6 kombu==5.0.2 prompt-toolkit==3.0.8 pytz==2020.4 redis==3.5.3 six==1.15.0 vine==5.0.0 wcwidth==0.2.5 ``` </p> </details> ### Other Dependencies <!-- Please provide system dependencies, configuration files and other dependency information if applicable --> <details> <p> N/A </p> </details> ## Minimally Reproducible Test Case <!-- Please provide a reproducible test case. Refer to the Reporting Bugs section in our contribution guide. We prefer submitting test cases in the form of a PR to our integration test suite. If you can provide one, please mention the PR number below. If not, please attach the most minimal code example required to reproduce the issue below. If the test case is too large, please include a link to a gist or a repository below. --> <details> <p> ```python #!/usr/bin/env python3 from celery import Celery app = Celery( 'tasks', broker='amqp://user:*@:5672/', backend='redis://:@*:6379/1', ) @app.task(ignore_result=False) def test(args, *kwargs): return 'x' 536870911 # 512MB ``` </p> </details> # Expected Behavior <!-- Describe in detail what you expect to happen --> I'd except an exception or error. There is no point in retrying storing result to Redis when it simply never fits there. String limit is 512 MB. I could check size of the data I'm returning in task. However, Celery adds additional metadata to my result so I do not know to what size should I limit my result. # Actual Behavior <!-- Describe in detail what actually happened. Please include a backtrace and surround it with triple backticks (```). In addition, include the Celery daemon logs, the broker logs, the result backend logs and system logs below if they will help us debug the issue. --> ``` [2020-12-09 08:38:52,786: ERROR/ForkPoolWorker-8] Connection to Redis lost: Retry (0/20) now. [2020-12-09 08:38:53,328: ERROR/ForkPoolWorker-8] Connection to Redis lost: Retry (1/20) in 1.00 second. [2020-12-09 08:38:54,940: ERROR/ForkPoolWorker-8] Connection to Redis lost: Retry (2/20) in 1.00 second. [2020-12-09 08:38:56,547: ERROR/ForkPoolWorker-8] Connection to Redis lost: Retry (3/20) in 1.00 second. .... and so on ``` Redis fails with the following error when you try to set string larger than 512 MB: ``` raise ConnectionError("Error %s while writing to socket. %s." % redis.exceptions.ConnectionError: Error 32 while writing to socket. Broken pipe. ``` Tried via another script with direct Redis connection. This says nothing about input size therefore I think value size should be checked before sending data to Redis.	Pull requests are welcome.	2021-02-07T09:06:11Z	[]	[]
pandas-dev/pandas	pandas-dev__pandas-39217	b31c069ded78a67e0a6f54fc776a7920fd09beca	diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py --- a/pandas/io/json/_json.py +++ b/pandas/io/json/_json.py @@ -38,7 +38,7 @@ ) from pandas.io.json._normalize import convert_to_line_delimits from pandas.io.json._table_schema import build_table_schema, parse_table_schema -from pandas.io.parsers import validate_integer +from pandas.io.parsers.readers import validate_integer loads = json.loads dumps = json.dumps diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py deleted file mode 100644 --- a/pandas/io/parsers.py +++ /dev/null @@ -1,3997 +0,0 @@ -""" -Module contains tools for processing files into DataFrames or other objects -""" - -from collections import abc, defaultdict -import csv -import datetime -from io import StringIO -import itertools -import re -import sys -from textwrap import fill -from typing import ( - Any, - Dict, - Iterable, - Iterator, - List, - Optional, - Sequence, - Set, - Type, - cast, -) -import warnings - -import numpy as np - -import pandas._libs.lib as lib -import pandas._libs.ops as libops -import pandas._libs.parsers as parsers -from pandas._libs.parsers import STR_NA_VALUES -from pandas._libs.tslibs import parsing -from pandas._typing import DtypeArg, FilePathOrBuffer, StorageOptions, Union -from pandas.errors import ( - AbstractMethodError, - EmptyDataError, - ParserError, - ParserWarning, -) -from pandas.util._decorators import Appender - -from pandas.core.dtypes.cast import astype_nansafe -from pandas.core.dtypes.common import ( - ensure_object, - ensure_str, - is_bool_dtype, - is_categorical_dtype, - is_dict_like, - is_dtype_equal, - is_extension_array_dtype, - is_file_like, - is_float, - is_integer, - is_integer_dtype, - is_list_like, - is_object_dtype, - is_scalar, - is_string_dtype, - pandas_dtype, -) -from pandas.core.dtypes.dtypes import CategoricalDtype -from pandas.core.dtypes.missing import isna - -from pandas.core import algorithms, generic -from pandas.core.arrays import Categorical -from pandas.core.frame import DataFrame -from pandas.core.indexes.api import ( - Index, - MultiIndex, - RangeIndex, - ensure_index_from_sequences, -) -from pandas.core.series import Series -from pandas.core.tools import datetimes as tools - -from pandas.io.common import IOHandles, get_handle, validate_header_arg -from pandas.io.date_converters import generic_parser - -# BOM character (byte order mark) -# This exists at the beginning of a file to indicate endianness -# of a file (stream). Unfortunately, this marker screws up parsing, -# so we need to remove it if we see it. -_BOM = "\ufeff" - -_doc_read_csv_and_table = ( - r""" -{summary} - -Also supports optionally iterating or breaking of the file -into chunks. - -Additional help can be found in the online docs for -`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. - -Parameters ----------- -filepath_or_buffer : str, path object or file-like object - Any valid string path is acceptable. The string could be a URL. Valid - URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is - expected. A local file could be: file://localhost/path/to/table.csv. - - If you want to pass in a path object, pandas accepts any ``os.PathLike``. - - By file-like object, we refer to objects with a ``read()`` method, such as - a file handle (e.g. via builtin ``open`` function) or ``StringIO``. -sep : str, default {_default_sep} - Delimiter to use. If sep is None, the C engine cannot automatically detect - the separator, but the Python parsing engine can, meaning the latter will - be used and automatically detect the separator by Python's builtin sniffer - tool, ``csv.Sniffer``. In addition, separators longer than 1 character and - different from ``'\s+'`` will be interpreted as regular expressions and - will also force the use of the Python parsing engine. Note that regex - delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``. -delimiter : str, default ``None`` - Alias for sep. -header : int, list of int, default 'infer' - Row number(s) to use as the column names, and the start of the - data. Default behavior is to infer the column names: if no names - are passed the behavior is identical to ``header=0`` and column - names are inferred from the first line of the file, if column - names are passed explicitly then the behavior is identical to - ``header=None``. Explicitly pass ``header=0`` to be able to - replace existing names. The header can be a list of integers that - specify row locations for a multi-index on the columns - e.g. [0,1,3]. Intervening rows that are not specified will be - skipped (e.g. 2 in this example is skipped). Note that this - parameter ignores commented lines and empty lines if - ``skip_blank_lines=True``, so ``header=0`` denotes the first line of - data rather than the first line of the file. -names : array-like, optional - List of column names to use. If the file contains a header row, - then you should explicitly pass ``header=0`` to override the column names. - Duplicates in this list are not allowed. -index_col : int, str, sequence of int / str, or False, default ``None`` - Column(s) to use as the row labels of the ``DataFrame``, either given as - string name or column index. If a sequence of int / str is given, a - MultiIndex is used. - - Note: ``index_col=False`` can be used to force pandas to not use the first - column as the index, e.g. when you have a malformed file with delimiters at - the end of each line. -usecols : list-like or callable, optional - Return a subset of the columns. If list-like, all elements must either - be positional (i.e. integer indices into the document columns) or strings - that correspond to column names provided either by the user in `names` or - inferred from the document header row(s). For example, a valid list-like - `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``. - Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``. - To instantiate a DataFrame from ``data`` with element order preserved use - ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns - in ``['foo', 'bar']`` order or - ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]`` - for ``['bar', 'foo']`` order. - - If callable, the callable function will be evaluated against the column - names, returning names where the callable function evaluates to True. An - example of a valid callable argument would be ``lambda x: x.upper() in - ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster - parsing time and lower memory usage. -squeeze : bool, default False - If the parsed data only contains one column then return a Series. -prefix : str, optional - Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... -mangle_dupe_cols : bool, default True - Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than - 'X'...'X'. Passing in False will cause data to be overwritten if there - are duplicate names in the columns. -dtype : Type name or dict of column -> type, optional - Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32, - 'c': 'Int64'}} - Use `str` or `object` together with suitable `na_values` settings - to preserve and not interpret dtype. - If converters are specified, they will be applied INSTEAD - of dtype conversion. -engine : {{'c', 'python'}}, optional - Parser engine to use. The C engine is faster while the python engine is - currently more feature-complete. -converters : dict, optional - Dict of functions for converting values in certain columns. Keys can either - be integers or column labels. -true_values : list, optional - Values to consider as True. -false_values : list, optional - Values to consider as False. -skipinitialspace : bool, default False - Skip spaces after delimiter. -skiprows : list-like, int or callable, optional - Line numbers to skip (0-indexed) or number of lines to skip (int) - at the start of the file. - - If callable, the callable function will be evaluated against the row - indices, returning True if the row should be skipped and False otherwise. - An example of a valid callable argument would be ``lambda x: x in [0, 2]``. -skipfooter : int, default 0 - Number of lines at bottom of file to skip (Unsupported with engine='c'). -nrows : int, optional - Number of rows of file to read. Useful for reading pieces of large files. -na_values : scalar, str, list-like, or dict, optional - Additional strings to recognize as NA/NaN. If dict passed, specific - per-column NA values. By default the following values are interpreted as - NaN: '""" - + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ") - + """'. -keep_default_na : bool, default True - Whether or not to include the default NaN values when parsing the data. - Depending on whether `na_values` is passed in, the behavior is as follows: - - * If `keep_default_na` is True, and `na_values` are specified, `na_values` - is appended to the default NaN values used for parsing. - * If `keep_default_na` is True, and `na_values` are not specified, only - the default NaN values are used for parsing. - * If `keep_default_na` is False, and `na_values` are specified, only - the NaN values specified `na_values` are used for parsing. - * If `keep_default_na` is False, and `na_values` are not specified, no - strings will be parsed as NaN. - - Note that if `na_filter` is passed in as False, the `keep_default_na` and - `na_values` parameters will be ignored. -na_filter : bool, default True - Detect missing value markers (empty strings and the value of na_values). In - data without any NAs, passing na_filter=False can improve the performance - of reading a large file. -verbose : bool, default False - Indicate number of NA values placed in non-numeric columns. -skip_blank_lines : bool, default True - If True, skip over blank lines rather than interpreting as NaN values. -parse_dates : bool or list of int or names or list of lists or dict, \ -default False - The behavior is as follows: - - * boolean. If True -> try parsing the index. - * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 - each as a separate date column. - * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as - a single date column. - * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call - result 'foo' - - If a column or index cannot be represented as an array of datetimes, - say because of an unparsable value or a mixture of timezones, the column - or index will be returned unaltered as an object data type. For - non-standard datetime parsing, use ``pd.to_datetime`` after - ``pd.read_csv``. To parse an index or column with a mixture of timezones, - specify ``date_parser`` to be a partially-applied - :func:`pandas.to_datetime` with ``utc=True``. See - :ref:`io.csv.mixed_timezones` for more. - - Note: A fast-path exists for iso8601-formatted dates. -infer_datetime_format : bool, default False - If True and `parse_dates` is enabled, pandas will attempt to infer the - format of the datetime strings in the columns, and if it can be inferred, - switch to a faster method of parsing them. In some cases this can increase - the parsing speed by 5-10x. -keep_date_col : bool, default False - If True and `parse_dates` specifies combining multiple columns then - keep the original columns. -date_parser : function, optional - Function to use for converting a sequence of string columns to an array of - datetime instances. The default uses ``dateutil.parser.parser`` to do the - conversion. Pandas will try to call `date_parser` in three different ways, - advancing to the next if an exception occurs: 1) Pass one or more arrays - (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the - string values from the columns defined by `parse_dates` into a single array - and pass that; and 3) call `date_parser` once for each row using one or - more strings (corresponding to the columns defined by `parse_dates`) as - arguments. -dayfirst : bool, default False - DD/MM format dates, international and European format. -cache_dates : bool, default True - If True, use a cache of unique, converted dates to apply the datetime - conversion. May produce significant speed-up when parsing duplicate - date strings, especially ones with timezone offsets. - - .. versionadded:: 0.25.0 -iterator : bool, default False - Return TextFileReader object for iteration or getting chunks with - ``get_chunk()``. - - .. versionchanged:: 1.2 - - ``TextFileReader`` is a context manager. -chunksize : int, optional - Return TextFileReader object for iteration. - See the `IO Tools docs - <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ - for more information on ``iterator`` and ``chunksize``. - - .. versionchanged:: 1.2 - - ``TextFileReader`` is a context manager. -compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' - For on-the-fly decompression of on-disk data. If 'infer' and - `filepath_or_buffer` is path-like, then detect compression from the - following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no - decompression). If using 'zip', the ZIP file must contain only one data - file to be read in. Set to None for no decompression. -thousands : str, optional - Thousands separator. -decimal : str, default '.' - Character to recognize as decimal point (e.g. use ',' for European data). -lineterminator : str (length 1), optional - Character to break file into lines. Only valid with C parser. -quotechar : str (length 1), optional - The character used to denote the start and end of a quoted item. Quoted - items can include the delimiter and it will be ignored. -quoting : int or csv.QUOTE_* instance, default 0 - Control field quoting behavior per ``csv.QUOTE_`` constants. Use one of - QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). -doublequote : bool, default ``True`` - When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate - whether or not to interpret two consecutive quotechar elements INSIDE a - field as a single ``quotechar`` element. -escapechar : str (length 1), optional - One-character string used to escape other characters. -comment : str, optional - Indicates remainder of line should not be parsed. If found at the beginning - of a line, the line will be ignored altogether. This parameter must be a - single character. Like empty lines (as long as ``skip_blank_lines=True``), - fully commented lines are ignored by the parameter `header` but not by - `skiprows`. For example, if ``comment='#'``, parsing - ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being - treated as the header. -encoding : str, optional - Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python - standard encodings - <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ . -dialect : str or csv.Dialect, optional - If provided, this parameter will override values (default or not) for the - following parameters: `delimiter`, `doublequote`, `escapechar`, - `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to - override values, a ParserWarning will be issued. See csv.Dialect - documentation for more details. -error_bad_lines : bool, default True - Lines with too many fields (e.g. a csv line with too many commas) will by - default cause an exception to be raised, and no DataFrame will be returned. - If False, then these "bad lines" will dropped from the DataFrame that is - returned. -warn_bad_lines : bool, default True - If error_bad_lines is False, and warn_bad_lines is True, a warning for each - "bad line" will be output. -delim_whitespace : bool, default False - Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be - used as the sep. Equivalent to setting ``sep='\\s+'``. If this option - is set to True, nothing should be passed in for the ``delimiter`` - parameter. -low_memory : bool, default True - Internally process the file in chunks, resulting in lower memory use - while parsing, but possibly mixed type inference. To ensure no mixed - types either set False, or specify the type with the `dtype` parameter. - Note that the entire file is read into a single DataFrame regardless, - use the `chunksize` or `iterator` parameter to return the data in chunks. - (Only valid with C parser). -memory_map : bool, default False - If a filepath is provided for `filepath_or_buffer`, map the file object - directly onto memory and access the data directly from there. Using this - option can improve performance because there is no longer any I/O overhead. -float_precision : str, optional - Specifies which converter the C engine should use for floating-point - values. The options are ``None`` or 'high' for the ordinary converter, - 'legacy' for the original lower precision pandas converter, and - 'round_trip' for the round-trip converter. - - .. versionchanged:: 1.2 - -{storage_options} - - .. versionadded:: 1.2 - -Returns -------- -DataFrame or TextParser - A comma-separated values (csv) file is returned as two-dimensional - data structure with labeled axes. - -See Also --------- -DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. -read_csv : Read a comma-separated values (csv) file into DataFrame. -read_fwf : Read a table of fixed-width formatted lines into DataFrame. - -Examples --------- ->>> pd.{func_name}('data.csv') # doctest: +SKIP -""" -) - - -def validate_integer(name, val, min_val=0): - """ - Checks whether the 'name' parameter for parsing is either - an integer OR float that can SAFELY be cast to an integer - without losing accuracy. Raises a ValueError if that is - not the case. - - Parameters - ---------- - name : string - Parameter name (used for error reporting) - val : int or float - The value to check - min_val : int - Minimum allowed value (val < min_val will result in a ValueError) - """ - msg = f"'{name:s}' must be an integer >={min_val:d}" - - if val is not None: - if is_float(val): - if int(val) != val: - raise ValueError(msg) - val = int(val) - elif not (is_integer(val) and val >= min_val): - raise ValueError(msg) - - return val - - -def _validate_names(names): - """ - Raise ValueError if the `names` parameter contains duplicates or has an - invalid data type. - - Parameters - ---------- - names : array-like or None - An array containing a list of the names used for the output DataFrame. - - Raises - ------ - ValueError - If names are not unique or are not ordered (e.g. set). - """ - if names is not None: - if len(names) != len(set(names)): - raise ValueError("Duplicate names are not allowed.") - if not ( - is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView) - ): - raise ValueError("Names should be an ordered collection.") - - -def _read(filepath_or_buffer: FilePathOrBuffer, kwds): - """Generic reader of line files.""" - if kwds.get("date_parser", None) is not None: - if isinstance(kwds["parse_dates"], bool): - kwds["parse_dates"] = True - - # Extract some of the arguments (pass chunksize on). - iterator = kwds.get("iterator", False) - chunksize = validate_integer("chunksize", kwds.get("chunksize", None), 1) - nrows = kwds.get("nrows", None) - - # Check for duplicates in names. - _validate_names(kwds.get("names", None)) - - # Create the parser. - parser = TextFileReader(filepath_or_buffer, kwds) - - if chunksize or iterator: - return parser - - with parser: - return parser.read(nrows) - - -_parser_defaults = { - "delimiter": None, - "escapechar": None, - "quotechar": '"', - "quoting": csv.QUOTE_MINIMAL, - "doublequote": True, - "skipinitialspace": False, - "lineterminator": None, - "header": "infer", - "index_col": None, - "names": None, - "prefix": None, - "skiprows": None, - "skipfooter": 0, - "nrows": None, - "na_values": None, - "keep_default_na": True, - "true_values": None, - "false_values": None, - "converters": None, - "dtype": None, - "cache_dates": True, - "thousands": None, - "comment": None, - "decimal": ".", - # 'engine': 'c', - "parse_dates": False, - "keep_date_col": False, - "dayfirst": False, - "date_parser": None, - "usecols": None, - # 'iterator': False, - "chunksize": None, - "verbose": False, - "encoding": None, - "squeeze": False, - "compression": None, - "mangle_dupe_cols": True, - "infer_datetime_format": False, - "skip_blank_lines": True, -} - - -_c_parser_defaults = { - "delim_whitespace": False, - "na_filter": True, - "low_memory": True, - "memory_map": False, - "error_bad_lines": True, - "warn_bad_lines": True, - "float_precision": None, -} - -_fwf_defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None} - -_c_unsupported = {"skipfooter"} -_python_unsupported = {"low_memory", "float_precision"} - -_deprecated_defaults: Dict[str, Any] = {} -_deprecated_args: Set[str] = set() - - -@Appender( - _doc_read_csv_and_table.format( - func_name="read_csv", - summary="Read a comma-separated values (csv) file into DataFrame.", - _default_sep="','", - storage_options=generic._shared_docs["storage_options"], - ) -) -def read_csv( - filepath_or_buffer: FilePathOrBuffer, - sep=lib.no_default, - delimiter=None, - # Column and Index Locations and Names - header="infer", - names=None, - index_col=None, - usecols=None, - squeeze=False, - prefix=None, - mangle_dupe_cols=True, - # General Parsing Configuration - dtype: Optional[DtypeArg] = None, - engine=None, - converters=None, - true_values=None, - false_values=None, - skipinitialspace=False, - skiprows=None, - skipfooter=0, - nrows=None, - # NA and Missing Data Handling - na_values=None, - keep_default_na=True, - na_filter=True, - verbose=False, - skip_blank_lines=True, - # Datetime Handling - parse_dates=False, - infer_datetime_format=False, - keep_date_col=False, - date_parser=None, - dayfirst=False, - cache_dates=True, - # Iteration - iterator=False, - chunksize=None, - # Quoting, Compression, and File Format - compression="infer", - thousands=None, - decimal: str = ".", - lineterminator=None, - quotechar='"', - quoting=csv.QUOTE_MINIMAL, - doublequote=True, - escapechar=None, - comment=None, - encoding=None, - dialect=None, - # Error Handling - error_bad_lines=True, - warn_bad_lines=True, - # Internal - delim_whitespace=False, - low_memory=_c_parser_defaults["low_memory"], - memory_map=False, - float_precision=None, - storage_options: StorageOptions = None, -): - kwds = locals() - del kwds["filepath_or_buffer"] - del kwds["sep"] - - kwds_defaults = _refine_defaults_read( - dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": ","} - ) - kwds.update(kwds_defaults) - - return _read(filepath_or_buffer, kwds) - - -@Appender( - _doc_read_csv_and_table.format( - func_name="read_table", - summary="Read general delimited file into DataFrame.", - _default_sep=r"'\\t' (tab-stop)", - storage_options=generic._shared_docs["storage_options"], - ) -) -def read_table( - filepath_or_buffer: FilePathOrBuffer, - sep=lib.no_default, - delimiter=None, - # Column and Index Locations and Names - header="infer", - names=None, - index_col=None, - usecols=None, - squeeze=False, - prefix=None, - mangle_dupe_cols=True, - # General Parsing Configuration - dtype: Optional[DtypeArg] = None, - engine=None, - converters=None, - true_values=None, - false_values=None, - skipinitialspace=False, - skiprows=None, - skipfooter=0, - nrows=None, - # NA and Missing Data Handling - na_values=None, - keep_default_na=True, - na_filter=True, - verbose=False, - skip_blank_lines=True, - # Datetime Handling - parse_dates=False, - infer_datetime_format=False, - keep_date_col=False, - date_parser=None, - dayfirst=False, - cache_dates=True, - # Iteration - iterator=False, - chunksize=None, - # Quoting, Compression, and File Format - compression="infer", - thousands=None, - decimal: str = ".", - lineterminator=None, - quotechar='"', - quoting=csv.QUOTE_MINIMAL, - doublequote=True, - escapechar=None, - comment=None, - encoding=None, - dialect=None, - # Error Handling - error_bad_lines=True, - warn_bad_lines=True, - # Internal - delim_whitespace=False, - low_memory=_c_parser_defaults["low_memory"], - memory_map=False, - float_precision=None, -): - kwds = locals() - del kwds["filepath_or_buffer"] - del kwds["sep"] - - kwds_defaults = _refine_defaults_read( - dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": "\t"} - ) - kwds.update(kwds_defaults) - - return _read(filepath_or_buffer, kwds) - - -def read_fwf( - filepath_or_buffer: FilePathOrBuffer, - colspecs="infer", - widths=None, - infer_nrows=100, - kwds, -): - r""" - Read a table of fixed-width formatted lines into DataFrame. - - Also supports optionally iterating or breaking of the file - into chunks. - - Additional help can be found in the `online docs for IO Tools - <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. - - Parameters - ---------- - filepath_or_buffer : str, path object or file-like object - Any valid string path is acceptable. The string could be a URL. Valid - URL schemes include http, ftp, s3, and file. For file URLs, a host is - expected. A local file could be: - ``file://localhost/path/to/table.csv``. - - If you want to pass in a path object, pandas accepts any - ``os.PathLike``. - - By file-like object, we refer to objects with a ``read()`` method, - such as a file handle (e.g. via builtin ``open`` function) - or ``StringIO``. - colspecs : list of tuple (int, int) or 'infer'. optional - A list of tuples giving the extents of the fixed-width - fields of each line as half-open intervals (i.e., [from, to[ ). - String value 'infer' can be used to instruct the parser to try - detecting the column specifications from the first 100 rows of - the data which are not being skipped via skiprows (default='infer'). - widths : list of int, optional - A list of field widths which can be used instead of 'colspecs' if - the intervals are contiguous. - infer_nrows : int, default 100 - The number of rows to consider when letting the parser determine the - `colspecs`. - - .. versionadded:: 0.24.0 - kwds : optional - Optional keyword arguments can be passed to ``TextFileReader``. - - Returns - ------- - DataFrame or TextParser - A comma-separated values (csv) file is returned as two-dimensional - data structure with labeled axes. - - See Also - -------- - DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. - read_csv : Read a comma-separated values (csv) file into DataFrame. - - Examples - -------- - >>> pd.read_fwf('data.csv') # doctest: +SKIP - """ - # Check input arguments. - if colspecs is None and widths is None: - raise ValueError("Must specify either colspecs or widths") - elif colspecs not in (None, "infer") and widths is not None: - raise ValueError("You must specify only one of 'widths' and 'colspecs'") - - # Compute 'colspecs' from 'widths', if specified. - if widths is not None: - colspecs, col = [], 0 - for w in widths: - colspecs.append((col, col + w)) - col += w - - kwds["colspecs"] = colspecs - kwds["infer_nrows"] = infer_nrows - kwds["engine"] = "python-fwf" - return _read(filepath_or_buffer, kwds) - - -class TextFileReader(abc.Iterator): - """ - - Passed dialect overrides any of the related parser options - - """ - - def __init__(self, f, engine=None, kwds): - - self.f = f - - if engine is not None: - engine_specified = True - else: - engine = "python" - engine_specified = False - self.engine = engine - self._engine_specified = kwds.get("engine_specified", engine_specified) - - _validate_skipfooter(kwds) - - dialect = _extract_dialect(kwds) - if dialect is not None: - kwds = _merge_with_dialect_properties(dialect, kwds) - - if kwds.get("header", "infer") == "infer": - kwds["header"] = 0 if kwds.get("names") is None else None - - self.orig_options = kwds - - # miscellanea - self._currow = 0 - - options = self._get_options_with_defaults(engine) - options["storage_options"] = kwds.get("storage_options", None) - - self.chunksize = options.pop("chunksize", None) - self.nrows = options.pop("nrows", None) - self.squeeze = options.pop("squeeze", False) - - self._check_file_or_buffer(f, engine) - self.options, self.engine = self._clean_options(options, engine) - - if "has_index_names" in kwds: - self.options["has_index_names"] = kwds["has_index_names"] - - self._engine = self._make_engine(self.engine) - - def close(self): - self._engine.close() - - def _get_options_with_defaults(self, engine): - kwds = self.orig_options - - options = {} - - for argname, default in _parser_defaults.items(): - value = kwds.get(argname, default) - - # see gh-12935 - if argname == "mangle_dupe_cols" and not value: - raise ValueError("Setting mangle_dupe_cols=False is not supported yet") - else: - options[argname] = value - - for argname, default in _c_parser_defaults.items(): - if argname in kwds: - value = kwds[argname] - - if engine != "c" and value != default: - if "python" in engine and argname not in _python_unsupported: - pass - elif value == _deprecated_defaults.get(argname, default): - pass - else: - raise ValueError( - f"The {repr(argname)} option is not supported with the " - f"{repr(engine)} engine" - ) - else: - value = _deprecated_defaults.get(argname, default) - options[argname] = value - - if engine == "python-fwf": - # pandas\io\parsers.py:907: error: Incompatible types in assignment - # (expression has type "object", variable has type "Union[int, str, - # None]") [assignment] - for argname, default in _fwf_defaults.items(): # type: ignore[assignment] - options[argname] = kwds.get(argname, default) - - return options - - def _check_file_or_buffer(self, f, engine): - # see gh-16530 - if is_file_like(f) and engine != "c" and not hasattr(f, "__next__"): - # The C engine doesn't need the file-like to have the "__next__" - # attribute. However, the Python engine explicitly calls - # "__next__(...)" when iterating through such an object, meaning it - # needs to have that attribute - raise ValueError( - "The 'python' engine cannot iterate through this file buffer." - ) - - def _clean_options(self, options, engine): - result = options.copy() - - fallback_reason = None - - # C engine not supported yet - if engine == "c": - if options["skipfooter"] > 0: - fallback_reason = "the 'c' engine does not support skipfooter" - engine = "python" - - sep = options["delimiter"] - delim_whitespace = options["delim_whitespace"] - - if sep is None and not delim_whitespace: - if engine == "c": - fallback_reason = ( - "the 'c' engine does not support " - "sep=None with delim_whitespace=False" - ) - engine = "python" - elif sep is not None and len(sep) > 1: - if engine == "c" and sep == r"\s+": - result["delim_whitespace"] = True - del result["delimiter"] - elif engine not in ("python", "python-fwf"): - # wait until regex engine integrated - fallback_reason = ( - "the 'c' engine does not support " - "regex separators (separators > 1 char and " - r"different from '\s+' are interpreted as regex)" - ) - engine = "python" - elif delim_whitespace: - if "python" in engine: - result["delimiter"] = r"\s+" - elif sep is not None: - encodeable = True - encoding = sys.getfilesystemencoding() or "utf-8" - try: - if len(sep.encode(encoding)) > 1: - encodeable = False - except UnicodeDecodeError: - encodeable = False - if not encodeable and engine not in ("python", "python-fwf"): - fallback_reason = ( - f"the separator encoded in {encoding} " - "is > 1 char long, and the 'c' engine " - "does not support such separators" - ) - engine = "python" - - quotechar = options["quotechar"] - if quotechar is not None and isinstance(quotechar, (str, bytes)): - if ( - len(quotechar) == 1 - and ord(quotechar) > 127 - and engine not in ("python", "python-fwf") - ): - fallback_reason = ( - "ord(quotechar) > 127, meaning the " - "quotechar is larger than one byte, " - "and the 'c' engine does not support such quotechars" - ) - engine = "python" - - if fallback_reason and self._engine_specified: - raise ValueError(fallback_reason) - - if engine == "c": - for arg in _c_unsupported: - del result[arg] - - if "python" in engine: - for arg in _python_unsupported: - if fallback_reason and result[arg] != _c_parser_defaults[arg]: - raise ValueError( - "Falling back to the 'python' engine because " - f"{fallback_reason}, but this causes {repr(arg)} to be " - "ignored as it is not supported by the 'python' engine." - ) - del result[arg] - - if fallback_reason: - warnings.warn( - ( - "Falling back to the 'python' engine because " - f"{fallback_reason}; you can avoid this warning by specifying " - "engine='python'." - ), - ParserWarning, - stacklevel=5, - ) - - index_col = options["index_col"] - names = options["names"] - converters = options["converters"] - na_values = options["na_values"] - skiprows = options["skiprows"] - - validate_header_arg(options["header"]) - - for arg in _deprecated_args: - parser_default = _c_parser_defaults[arg] - depr_default = _deprecated_defaults[arg] - if result.get(arg, depr_default) != depr_default: - msg = ( - f"The {arg} argument has been deprecated and will be " - "removed in a future version.\n\n" - ) - warnings.warn(msg, FutureWarning, stacklevel=2) - else: - result[arg] = parser_default - - if index_col is True: - raise ValueError("The value of index_col couldn't be 'True'") - if _is_index_col(index_col): - if not isinstance(index_col, (list, tuple, np.ndarray)): - index_col = [index_col] - result["index_col"] = index_col - - names = list(names) if names is not None else names - - # type conversion-related - if converters is not None: - if not isinstance(converters, dict): - raise TypeError( - "Type converters must be a dict or subclass, " - f"input was a {type(converters).__name__}" - ) - else: - converters = {} - - # Converting values to NA - keep_default_na = options["keep_default_na"] - na_values, na_fvalues = _clean_na_values(na_values, keep_default_na) - - # handle skiprows; this is internally handled by the - # c-engine, so only need for python parsers - if engine != "c": - if is_integer(skiprows): - skiprows = list(range(skiprows)) - if skiprows is None: - skiprows = set() - elif not callable(skiprows): - skiprows = set(skiprows) - - # put stuff back - result["names"] = names - result["converters"] = converters - result["na_values"] = na_values - result["na_fvalues"] = na_fvalues - result["skiprows"] = skiprows - - return result, engine - - def __next__(self): - try: - return self.get_chunk() - except StopIteration: - self.close() - raise - - def _make_engine(self, engine="c"): - mapping: Dict[str, Type[ParserBase]] = { - "c": CParserWrapper, - "python": PythonParser, - "python-fwf": FixedWidthFieldParser, - } - if engine not in mapping: - raise ValueError( - f"Unknown engine: {engine} (valid options are {mapping.keys()})" - ) - # error: Too many arguments for "ParserBase" - return mapping[engine](self.f, self.options) # type: ignore[call-arg] - - def _failover_to_python(self): - raise AbstractMethodError(self) - - def read(self, nrows=None): - nrows = validate_integer("nrows", nrows) - index, columns, col_dict = self._engine.read(nrows) - - if index is None: - if col_dict: - # Any column is actually fine: - new_rows = len(next(iter(col_dict.values()))) - index = RangeIndex(self._currow, self._currow + new_rows) - else: - new_rows = 0 - else: - new_rows = len(index) - - df = DataFrame(col_dict, columns=columns, index=index) - - self._currow += new_rows - - if self.squeeze and len(df.columns) == 1: - return df[df.columns[0]].copy() - return df - - def get_chunk(self, size=None): - if size is None: - size = self.chunksize - if self.nrows is not None: - if self._currow >= self.nrows: - raise StopIteration - size = min(size, self.nrows - self._currow) - return self.read(nrows=size) - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, traceback): - self.close() - - -def _is_index_col(col): - return col is not None and col is not False - - -def _is_potential_multi_index( - columns, index_col: Optional[Union[bool, Sequence[int]]] = None -): - """ - Check whether or not the `columns` parameter - could be converted into a MultiIndex. - - Parameters - ---------- - columns : array-like - Object which may or may not be convertible into a MultiIndex - index_col : None, bool or list, optional - Column or columns to use as the (possibly hierarchical) index - - Returns - ------- - boolean : Whether or not columns could become a MultiIndex - """ - if index_col is None or isinstance(index_col, bool): - index_col = [] - - return ( - len(columns) - and not isinstance(columns, MultiIndex) - and all(isinstance(c, tuple) for c in columns if c not in list(index_col)) - ) - - -def _evaluate_usecols(usecols, names): - """ - Check whether or not the 'usecols' parameter - is a callable. If so, enumerates the 'names' - parameter and returns a set of indices for - each entry in 'names' that evaluates to True. - If not a callable, returns 'usecols'. - """ - if callable(usecols): - return {i for i, name in enumerate(names) if usecols(name)} - return usecols - - -def _validate_usecols_names(usecols, names): - """ - Validates that all usecols are present in a given - list of names. If not, raise a ValueError that - shows what usecols are missing. - - Parameters - ---------- - usecols : iterable of usecols - The columns to validate are present in names. - names : iterable of names - The column names to check against. - - Returns - ------- - usecols : iterable of usecols - The `usecols` parameter if the validation succeeds. - - Raises - ------ - ValueError : Columns were missing. Error message will list them. - """ - missing = [c for c in usecols if c not in names] - if len(missing) > 0: - raise ValueError( - f"Usecols do not match columns, columns expected but not found: {missing}" - ) - - return usecols - - -def _validate_skipfooter_arg(skipfooter): - """ - Validate the 'skipfooter' parameter. - - Checks whether 'skipfooter' is a non-negative integer. - Raises a ValueError if that is not the case. - - Parameters - ---------- - skipfooter : non-negative integer - The number of rows to skip at the end of the file. - - Returns - ------- - validated_skipfooter : non-negative integer - The original input if the validation succeeds. - - Raises - ------ - ValueError : 'skipfooter' was not a non-negative integer. - """ - if not is_integer(skipfooter): - raise ValueError("skipfooter must be an integer") - - if skipfooter < 0: - raise ValueError("skipfooter cannot be negative") - - return skipfooter - - -def _validate_usecols_arg(usecols): - """ - Validate the 'usecols' parameter. - - Checks whether or not the 'usecols' parameter contains all integers - (column selection by index), strings (column by name) or is a callable. - Raises a ValueError if that is not the case. - - Parameters - ---------- - usecols : list-like, callable, or None - List of columns to use when parsing or a callable that can be used - to filter a list of table columns. - - Returns - ------- - usecols_tuple : tuple - A tuple of (verified_usecols, usecols_dtype). - - 'verified_usecols' is either a set if an array-like is passed in or - 'usecols' if a callable or None is passed in. - - 'usecols_dtype` is the inferred dtype of 'usecols' if an array-like - is passed in or None if a callable or None is passed in. - """ - msg = ( - "'usecols' must either be list-like of all strings, all unicode, " - "all integers or a callable." - ) - if usecols is not None: - if callable(usecols): - return usecols, None - - if not is_list_like(usecols): - # see gh-20529 - # - # Ensure it is iterable container but not string. - raise ValueError(msg) - - usecols_dtype = lib.infer_dtype(usecols, skipna=False) - - if usecols_dtype not in ("empty", "integer", "string"): - raise ValueError(msg) - - usecols = set(usecols) - - return usecols, usecols_dtype - return usecols, None - - -def _validate_parse_dates_arg(parse_dates): - """ - Check whether or not the 'parse_dates' parameter - is a non-boolean scalar. Raises a ValueError if - that is the case. - """ - msg = ( - "Only booleans, lists, and dictionaries are accepted " - "for the 'parse_dates' parameter" - ) - - if parse_dates is not None: - if is_scalar(parse_dates): - if not lib.is_bool(parse_dates): - raise TypeError(msg) - - elif not isinstance(parse_dates, (list, dict)): - raise TypeError(msg) - - return parse_dates - - -class ParserBase: - def __init__(self, kwds): - - self.names = kwds.get("names") - self.orig_names: Optional[List] = None - self.prefix = kwds.pop("prefix", None) - - self.index_col = kwds.get("index_col", None) - self.unnamed_cols: Set = set() - self.index_names: Optional[List] = None - self.col_names = None - - self.parse_dates = _validate_parse_dates_arg(kwds.pop("parse_dates", False)) - self.date_parser = kwds.pop("date_parser", None) - self.dayfirst = kwds.pop("dayfirst", False) - self.keep_date_col = kwds.pop("keep_date_col", False) - - self.na_values = kwds.get("na_values") - self.na_fvalues = kwds.get("na_fvalues") - self.na_filter = kwds.get("na_filter", False) - self.keep_default_na = kwds.get("keep_default_na", True) - - self.true_values = kwds.get("true_values") - self.false_values = kwds.get("false_values") - self.mangle_dupe_cols = kwds.get("mangle_dupe_cols", True) - self.infer_datetime_format = kwds.pop("infer_datetime_format", False) - self.cache_dates = kwds.pop("cache_dates", True) - - self._date_conv = _make_date_converter( - date_parser=self.date_parser, - dayfirst=self.dayfirst, - infer_datetime_format=self.infer_datetime_format, - cache_dates=self.cache_dates, - ) - - # validate header options for mi - self.header = kwds.get("header") - if isinstance(self.header, (list, tuple, np.ndarray)): - if not all(map(is_integer, self.header)): - raise ValueError("header must be integer or list of integers") - if any(i < 0 for i in self.header): - raise ValueError( - "cannot specify multi-index header with negative integers" - ) - if kwds.get("usecols"): - raise ValueError( - "cannot specify usecols when specifying a multi-index header" - ) - if kwds.get("names"): - raise ValueError( - "cannot specify names when specifying a multi-index header" - ) - - # validate index_col that only contains integers - if self.index_col is not None: - is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray)) - if not ( - is_sequence - and all(map(is_integer, self.index_col)) - or is_integer(self.index_col) - ): - raise ValueError( - "index_col must only contain row numbers " - "when specifying a multi-index header" - ) - elif self.header is not None: - # GH 27394 - if self.prefix is not None: - raise ValueError( - "Argument prefix must be None if argument header is not None" - ) - # GH 16338 - elif not is_integer(self.header): - raise ValueError("header must be integer or list of integers") - # GH 27779 - elif self.header < 0: - raise ValueError( - "Passing negative integer to header is invalid. " - "For no header, use header=None instead" - ) - - self._name_processed = False - - self._first_chunk = True - - self.handles: Optional[IOHandles] = None - - def _open_handles(self, src: FilePathOrBuffer, kwds: Dict[str, Any]) -> None: - """ - Let the readers open IOHanldes after they are done with their potential raises. - """ - self.handles = get_handle( - src, - "r", - encoding=kwds.get("encoding", None), - compression=kwds.get("compression", None), - memory_map=kwds.get("memory_map", False), - storage_options=kwds.get("storage_options", None), - ) - - def _validate_parse_dates_presence(self, columns: List[str]) -> None: - """ - Check if parse_dates are in columns. - - If user has provided names for parse_dates, check if those columns - are available. - - Parameters - ---------- - columns : list - List of names of the dataframe. - - Raises - ------ - ValueError - If column to parse_date is not in dataframe. - - """ - cols_needed: Iterable - if is_dict_like(self.parse_dates): - cols_needed = itertools.chain(self.parse_dates.values()) - elif is_list_like(self.parse_dates): - # a column in parse_dates could be represented - # ColReference = Union[int, str] - # DateGroups = List[ColReference] - # ParseDates = Union[DateGroups, List[DateGroups], - # Dict[ColReference, DateGroups]] - cols_needed = itertools.chain.from_iterable( - col if is_list_like(col) else [col] for col in self.parse_dates - ) - else: - cols_needed = [] - - # get only columns that are references using names (str), not by index - missing_cols = ", ".join( - sorted( - { - col - for col in cols_needed - if isinstance(col, str) and col not in columns - } - ) - ) - if missing_cols: - raise ValueError( - f"Missing column provided to 'parse_dates': '{missing_cols}'" - ) - - def close(self): - if self.handles is not None: - self.handles.close() - - @property - def _has_complex_date_col(self): - return isinstance(self.parse_dates, dict) or ( - isinstance(self.parse_dates, list) - and len(self.parse_dates) > 0 - and isinstance(self.parse_dates[0], list) - ) - - def _should_parse_dates(self, i): - if isinstance(self.parse_dates, bool): - return self.parse_dates - else: - if self.index_names is not None: - name = self.index_names[i] - else: - name = None - j = i if self.index_col is None else self.index_col[i] - - if is_scalar(self.parse_dates): - return (j == self.parse_dates) or ( - name is not None and name == self.parse_dates - ) - else: - return (j in self.parse_dates) or ( - name is not None and name in self.parse_dates - ) - - def _extract_multi_indexer_columns( - self, header, index_names, col_names, passed_names=False - ): - """ - extract and return the names, index_names, col_names - header is a list-of-lists returned from the parsers - """ - if len(header) < 2: - return header[0], index_names, col_names, passed_names - - # the names are the tuples of the header that are not the index cols - # 0 is the name of the index, assuming index_col is a list of column - # numbers - ic = self.index_col - if ic is None: - ic = [] - - if not isinstance(ic, (list, tuple, np.ndarray)): - ic = [ic] - sic = set(ic) - - # clean the index_names - index_names = header.pop(-1) - index_names, _, _ = _clean_index_names( - index_names, self.index_col, self.unnamed_cols - ) - - # extract the columns - field_count = len(header[0]) - - def extract(r): - return tuple(r[i] for i in range(field_count) if i not in sic) - - columns = list(zip((extract(r) for r in header))) - names = ic + columns - - # If we find unnamed columns all in a single - # level, then our header was too long. - for n in range(len(columns[0])): - if all(ensure_str(col[n]) in self.unnamed_cols for col in columns): - header = ",".join(str(x) for x in self.header) - raise ParserError( - f"Passed header=[{header}] are too many rows " - "for this multi_index of columns" - ) - - # Clean the column names (if we have an index_col). - if len(ic): - col_names = [ - r[0] if ((r[0] is not None) and r[0] not in self.unnamed_cols) else None - for r in header - ] - else: - col_names = [None] len(header) - - passed_names = True - - return names, index_names, col_names, passed_names - - def _maybe_dedup_names(self, names): - # see gh-7160 and gh-9424: this helps to provide - # immediate alleviation of the duplicate names - # issue and appears to be satisfactory to users, - # but ultimately, not needing to butcher the names - # would be nice! - if self.mangle_dupe_cols: - names = list(names) # so we can index - # pandas\io\parsers.py:1559: error: Need type annotation for - # 'counts' [var-annotated] - counts = defaultdict(int) # type: ignore[var-annotated] - is_potential_mi = _is_potential_multi_index(names, self.index_col) - - for i, col in enumerate(names): - cur_count = counts[col] - - while cur_count > 0: - counts[col] = cur_count + 1 - - if is_potential_mi: - col = col[:-1] + (f"{col[-1]}.{cur_count}",) - else: - col = f"{col}.{cur_count}" - cur_count = counts[col] - - names[i] = col - counts[col] = cur_count + 1 - - return names - - def _maybe_make_multi_index_columns(self, columns, col_names=None): - # possibly create a column mi here - if _is_potential_multi_index(columns): - columns = MultiIndex.from_tuples(columns, names=col_names) - return columns - - def _make_index(self, data, alldata, columns, indexnamerow=False): - if not _is_index_col(self.index_col) or not self.index_col: - index = None - - elif not self._has_complex_date_col: - index = self._get_simple_index(alldata, columns) - index = self._agg_index(index) - elif self._has_complex_date_col: - if not self._name_processed: - (self.index_names, _, self.index_col) = _clean_index_names( - list(columns), self.index_col, self.unnamed_cols - ) - self._name_processed = True - index = self._get_complex_date_index(data, columns) - index = self._agg_index(index, try_parse_dates=False) - - # add names for the index - if indexnamerow: - coffset = len(indexnamerow) - len(columns) - # pandas\io\parsers.py:1604: error: Item "None" of "Optional[Any]" - # has no attribute "set_names" [union-attr] - index = index.set_names(indexnamerow[:coffset]) # type: ignore[union-attr] - - # maybe create a mi on the columns - columns = self._maybe_make_multi_index_columns(columns, self.col_names) - - return index, columns - - _implicit_index = False - - def _get_simple_index(self, data, columns): - def ix(col): - if not isinstance(col, str): - return col - raise ValueError(f"Index {col} invalid") - - to_remove = [] - index = [] - for idx in self.index_col: - i = ix(idx) - to_remove.append(i) - index.append(data[i]) - - # remove index items from content and columns, don't pop in - # loop - for i in sorted(to_remove, reverse=True): - data.pop(i) - if not self._implicit_index: - columns.pop(i) - - return index - - def _get_complex_date_index(self, data, col_names): - def _get_name(icol): - if isinstance(icol, str): - return icol - - if col_names is None: - raise ValueError(f"Must supply column order to use {icol!s} as index") - - for i, c in enumerate(col_names): - if i == icol: - return c - - to_remove = [] - index = [] - for idx in self.index_col: - name = _get_name(idx) - to_remove.append(name) - index.append(data[name]) - - # remove index items from content and columns, don't pop in - # loop - for c in sorted(to_remove, reverse=True): - data.pop(c) - col_names.remove(c) - - return index - - def _agg_index(self, index, try_parse_dates=True) -> Index: - arrays = [] - - for i, arr in enumerate(index): - - if try_parse_dates and self._should_parse_dates(i): - arr = self._date_conv(arr) - - if self.na_filter: - col_na_values = self.na_values - col_na_fvalues = self.na_fvalues - else: - col_na_values = set() - col_na_fvalues = set() - - if isinstance(self.na_values, dict): - # pandas\io\parsers.py:1678: error: Value of type - # "Optional[Any]" is not indexable [index] - col_name = self.index_names[i] # type: ignore[index] - if col_name is not None: - col_na_values, col_na_fvalues = _get_na_values( - col_name, self.na_values, self.na_fvalues, self.keep_default_na - ) - - arr, _ = self._infer_types(arr, col_na_values \| col_na_fvalues) - arrays.append(arr) - - names = self.index_names - index = ensure_index_from_sequences(arrays, names) - - return index - - def _convert_to_ndarrays( - self, dct, na_values, na_fvalues, verbose=False, converters=None, dtypes=None - ): - result = {} - for c, values in dct.items(): - conv_f = None if converters is None else converters.get(c, None) - if isinstance(dtypes, dict): - cast_type = dtypes.get(c, None) - else: - # single dtype or None - cast_type = dtypes - - if self.na_filter: - col_na_values, col_na_fvalues = _get_na_values( - c, na_values, na_fvalues, self.keep_default_na - ) - else: - col_na_values, col_na_fvalues = set(), set() - - if conv_f is not None: - # conv_f applied to data before inference - if cast_type is not None: - warnings.warn( - ( - "Both a converter and dtype were specified " - f"for column {c} - only the converter will be used" - ), - ParserWarning, - stacklevel=7, - ) - - try: - values = lib.map_infer(values, conv_f) - except ValueError: - mask = algorithms.isin(values, list(na_values)).view(np.uint8) - values = lib.map_infer_mask(values, conv_f, mask) - - cvals, na_count = self._infer_types( - values, set(col_na_values) \| col_na_fvalues, try_num_bool=False - ) - else: - is_ea = is_extension_array_dtype(cast_type) - is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type) - # skip inference if specified dtype is object - # or casting to an EA - try_num_bool = not (cast_type and is_str_or_ea_dtype) - - # general type inference and conversion - cvals, na_count = self._infer_types( - values, set(col_na_values) \| col_na_fvalues, try_num_bool - ) - - # type specified in dtype param or cast_type is an EA - if cast_type and ( - not is_dtype_equal(cvals, cast_type) - or is_extension_array_dtype(cast_type) - ): - if not is_ea and na_count > 0: - try: - if is_bool_dtype(cast_type): - raise ValueError( - f"Bool column has NA values in column {c}" - ) - except (AttributeError, TypeError): - # invalid input to is_bool_dtype - pass - cast_type = pandas_dtype(cast_type) - cvals = self._cast_types(cvals, cast_type, c) - - result[c] = cvals - if verbose and na_count: - print(f"Filled {na_count} NA values in column {c!s}") - return result - - def _infer_types(self, values, na_values, try_num_bool=True): - """ - Infer types of values, possibly casting - - Parameters - ---------- - values : ndarray - na_values : set - try_num_bool : bool, default try - try to cast values to numeric (first preference) or boolean - - Returns - ------- - converted : ndarray - na_count : int - """ - na_count = 0 - if issubclass(values.dtype.type, (np.number, np.bool_)): - mask = algorithms.isin(values, list(na_values)) - na_count = mask.sum() - if na_count > 0: - if is_integer_dtype(values): - values = values.astype(np.float64) - np.putmask(values, mask, np.nan) - return values, na_count - - if try_num_bool and is_object_dtype(values.dtype): - # exclude e.g DatetimeIndex here - try: - result = lib.maybe_convert_numeric(values, na_values, False) - except (ValueError, TypeError): - # e.g. encountering datetime string gets ValueError - # TypeError can be raised in floatify - result = values - na_count = parsers.sanitize_objects(result, na_values, False) - else: - na_count = isna(result).sum() - else: - result = values - if values.dtype == np.object_: - na_count = parsers.sanitize_objects(values, na_values, False) - - if result.dtype == np.object_ and try_num_bool: - result = libops.maybe_convert_bool( - np.asarray(values), - true_values=self.true_values, - false_values=self.false_values, - ) - - return result, na_count - - def _cast_types(self, values, cast_type, column): - """ - Cast values to specified type - - Parameters - ---------- - values : ndarray - cast_type : string or np.dtype - dtype to cast values to - column : string - column name - used only for error reporting - - Returns - ------- - converted : ndarray - """ - if is_categorical_dtype(cast_type): - known_cats = ( - isinstance(cast_type, CategoricalDtype) - and cast_type.categories is not None - ) - - if not is_object_dtype(values) and not known_cats: - # TODO: this is for consistency with - # c-parser which parses all categories - # as strings - values = astype_nansafe(values, str) - - cats = Index(values).unique().dropna() - values = Categorical._from_inferred_categories( - cats, cats.get_indexer(values), cast_type, true_values=self.true_values - ) - - # use the EA's implementation of casting - elif is_extension_array_dtype(cast_type): - # ensure cast_type is an actual dtype and not a string - cast_type = pandas_dtype(cast_type) - array_type = cast_type.construct_array_type() - try: - if is_bool_dtype(cast_type): - return array_type._from_sequence_of_strings( - values, - dtype=cast_type, - true_values=self.true_values, - false_values=self.false_values, - ) - else: - return array_type._from_sequence_of_strings(values, dtype=cast_type) - except NotImplementedError as err: - raise NotImplementedError( - f"Extension Array: {array_type} must implement " - "_from_sequence_of_strings in order to be used in parser methods" - ) from err - - else: - try: - values = astype_nansafe(values, cast_type, copy=True, skipna=True) - except ValueError as err: - raise ValueError( - f"Unable to convert column {column} to type {cast_type}" - ) from err - return values - - def _do_date_conversions(self, names, data): - # returns data, columns - - if self.parse_dates is not None: - data, names = _process_date_conversion( - data, - self._date_conv, - self.parse_dates, - self.index_col, - self.index_names, - names, - keep_date_col=self.keep_date_col, - ) - - return names, data - - -class CParserWrapper(ParserBase): - def __init__(self, src: FilePathOrBuffer, kwds): - self.kwds = kwds - kwds = kwds.copy() - - ParserBase.__init__(self, kwds) - - # #2442 - kwds["allow_leading_cols"] = self.index_col is not False - - # GH20529, validate usecol arg before TextReader - self.usecols, self.usecols_dtype = _validate_usecols_arg(kwds["usecols"]) - kwds["usecols"] = self.usecols - - # open handles - self._open_handles(src, kwds) - assert self.handles is not None - for key in ("storage_options", "encoding", "memory_map", "compression"): - kwds.pop(key, None) - if self.handles.is_mmap and hasattr(self.handles.handle, "mmap"): - # pandas\io\parsers.py:1861: error: Item "IO[Any]" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "RawIOBase" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "BufferedIOBase" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "TextIOBase" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "TextIOWrapper" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "mmap" of "Union[IO[Any], - # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap]" has - # no attribute "mmap" [union-attr] - self.handles.handle = self.handles.handle.mmap # type: ignore[union-attr] - - try: - self._reader = parsers.TextReader(self.handles.handle, kwds) - except Exception: - self.handles.close() - raise - self.unnamed_cols = self._reader.unnamed_cols - - passed_names = self.names is None - - if self._reader.header is None: - self.names = None - else: - if len(self._reader.header) > 1: - # we have a multi index in the columns - ( - self.names, - self.index_names, - self.col_names, - passed_names, - ) = self._extract_multi_indexer_columns( - self._reader.header, self.index_names, self.col_names, passed_names - ) - else: - self.names = list(self._reader.header[0]) - - if self.names is None: - if self.prefix: - self.names = [ - f"{self.prefix}{i}" for i in range(self._reader.table_width) - ] - else: - self.names = list(range(self._reader.table_width)) - - # gh-9755 - # - # need to set orig_names here first - # so that proper indexing can be done - # with _set_noconvert_columns - # - # once names has been filtered, we will - # then set orig_names again to names - self.orig_names = self.names[:] - - if self.usecols: - usecols = _evaluate_usecols(self.usecols, self.orig_names) - - # GH 14671 - # assert for mypy, orig_names is List or None, None would error in issubset - assert self.orig_names is not None - if self.usecols_dtype == "string" and not set(usecols).issubset( - self.orig_names - ): - _validate_usecols_names(usecols, self.orig_names) - - if len(self.names) > len(usecols): - self.names = [ - n - for i, n in enumerate(self.names) - if (i in usecols or n in usecols) - ] - - if len(self.names) < len(usecols): - _validate_usecols_names(usecols, self.names) - - self._validate_parse_dates_presence(self.names) - self._set_noconvert_columns() - - self.orig_names = self.names - - if not self._has_complex_date_col: - if self._reader.leading_cols == 0 and _is_index_col(self.index_col): - - self._name_processed = True - (index_names, self.names, self.index_col) = _clean_index_names( - self.names, self.index_col, self.unnamed_cols - ) - - if self.index_names is None: - self.index_names = index_names - - if self._reader.header is None and not passed_names: - # pandas\io\parsers.py:1997: error: Argument 1 to "len" has - # incompatible type "Optional[Any]"; expected "Sized" - # [arg-type] - self.index_names = [None] * len( - self.index_names # type: ignore[arg-type] - ) - - self._implicit_index = self._reader.leading_cols > 0 - - def close(self) -> None: - super().close() - - # close additional handles opened by C parser - try: - self._reader.close() - except ValueError: - pass - - def _set_noconvert_columns(self): - """ - Set the columns that should not undergo dtype conversions. - - Currently, any column that is involved with date parsing will not - undergo such conversions. - """ - names = self.orig_names - if self.usecols_dtype == "integer": - # A set of integers will be converted to a list in - # the correct order every single time. - usecols = list(self.usecols) - usecols.sort() - elif callable(self.usecols) or self.usecols_dtype not in ("empty", None): - # The names attribute should have the correct columns - # in the proper order for indexing with parse_dates. - usecols = self.names[:] - else: - # Usecols is empty. - - # pandas\io\parsers.py:2030: error: Incompatible types in - # assignment (expression has type "None", variable has type - # "List[Any]") [assignment] - usecols = None # type: ignore[assignment] - - def _set(x): - if usecols is not None and is_integer(x): - x = usecols[x] - - if not is_integer(x): - # assert for mypy, names is List or None, None would error when calling - # .index() - assert names is not None - x = names.index(x) - - self._reader.set_noconvert(x) - - if isinstance(self.parse_dates, list): - for val in self.parse_dates: - if isinstance(val, list): - for k in val: - _set(k) - else: - _set(val) - - elif isinstance(self.parse_dates, dict): - for val in self.parse_dates.values(): - if isinstance(val, list): - for k in val: - _set(k) - else: - _set(val) - - elif self.parse_dates: - if isinstance(self.index_col, list): - for k in self.index_col: - _set(k) - elif self.index_col is not None: - _set(self.index_col) - - def set_error_bad_lines(self, status): - self._reader.set_error_bad_lines(int(status)) - - def read(self, nrows=None): - try: - data = self._reader.read(nrows) - except StopIteration: - if self._first_chunk: - self._first_chunk = False - names = self._maybe_dedup_names(self.orig_names) - index, columns, col_dict = _get_empty_meta( - names, - self.index_col, - self.index_names, - dtype=self.kwds.get("dtype"), - ) - columns = self._maybe_make_multi_index_columns(columns, self.col_names) - - if self.usecols is not None: - columns = self._filter_usecols(columns) - - col_dict = {k: v for k, v in col_dict.items() if k in columns} - - return index, columns, col_dict - - else: - self.close() - raise - - # Done with first read, next time raise StopIteration - self._first_chunk = False - - names = self.names - - if self._reader.leading_cols: - if self._has_complex_date_col: - raise NotImplementedError("file structure not yet supported") - - # implicit index, no index names - arrays = [] - - for i in range(self._reader.leading_cols): - if self.index_col is None: - values = data.pop(i) - else: - values = data.pop(self.index_col[i]) - - values = self._maybe_parse_dates(values, i, try_parse_dates=True) - arrays.append(values) - - index = ensure_index_from_sequences(arrays) - - if self.usecols is not None: - names = self._filter_usecols(names) - - names = self._maybe_dedup_names(names) - - # rename dict keys - data = sorted(data.items()) - data = {k: v for k, (i, v) in zip(names, data)} - - names, data = self._do_date_conversions(names, data) - - else: - # rename dict keys - data = sorted(data.items()) - - # ugh, mutation - - # assert for mypy, orig_names is List or None, None would error in list(...) - assert self.orig_names is not None - names = list(self.orig_names) - names = self._maybe_dedup_names(names) - - if self.usecols is not None: - names = self._filter_usecols(names) - - # columns as list - alldata = [x[1] for x in data] - - data = {k: v for k, (i, v) in zip(names, data)} - - names, data = self._do_date_conversions(names, data) - index, names = self._make_index(data, alldata, names) - - # maybe create a mi on the columns - names = self._maybe_make_multi_index_columns(names, self.col_names) - - return index, names, data - - def _filter_usecols(self, names): - # hackish - usecols = _evaluate_usecols(self.usecols, names) - if usecols is not None and len(names) != len(usecols): - names = [ - name for i, name in enumerate(names) if i in usecols or name in usecols - ] - return names - - def _get_index_names(self): - names = list(self._reader.header[0]) - idx_names = None - - if self._reader.leading_cols == 0 and self.index_col is not None: - (idx_names, names, self.index_col) = _clean_index_names( - names, self.index_col, self.unnamed_cols - ) - - return names, idx_names - - def _maybe_parse_dates(self, values, index, try_parse_dates=True): - if try_parse_dates and self._should_parse_dates(index): - values = self._date_conv(values) - return values - - -def TextParser(args, kwds): - """ - Converts lists of lists/tuples into DataFrames with proper type inference - and optional (e.g. string to datetime) conversion. Also enables iterating - lazily over chunks of large files - - Parameters - ---------- - data : file-like object or list - delimiter : separator character to use - dialect : str or csv.Dialect instance, optional - Ignored if delimiter is longer than 1 character - names : sequence, default - header : int, default 0 - Row to use to parse column labels. Defaults to the first row. Prior - rows will be discarded - index_col : int or list, optional - Column or columns to use as the (possibly hierarchical) index - has_index_names: bool, default False - True if the cols defined in index_col have an index name and are - not in the header. - na_values : scalar, str, list-like, or dict, optional - Additional strings to recognize as NA/NaN. - keep_default_na : bool, default True - thousands : str, optional - Thousands separator - comment : str, optional - Comment out remainder of line - parse_dates : bool, default False - keep_date_col : bool, default False - date_parser : function, optional - skiprows : list of integers - Row numbers to skip - skipfooter : int - Number of line at bottom of file to skip - converters : dict, optional - Dict of functions for converting values in certain columns. Keys can - either be integers or column labels, values are functions that take one - input argument, the cell (not column) content, and return the - transformed content. - encoding : str, optional - Encoding to use for UTF when reading/writing (ex. 'utf-8') - squeeze : bool, default False - returns Series if only one column. - infer_datetime_format: bool, default False - If True and `parse_dates` is True for a column, try to infer the - datetime format based on the first datetime string. If the format - can be inferred, there often will be a large parsing speed-up. - float_precision : str, optional - Specifies which converter the C engine should use for floating-point - values. The options are `None` or `high` for the ordinary converter, - `legacy` for the original lower precision pandas converter, and - `round_trip` for the round-trip converter. - - .. versionchanged:: 1.2 - """ - kwds["engine"] = "python" - return TextFileReader(args, kwds) - - -def count_empty_vals(vals) -> int: - return sum(1 for v in vals if v == "" or v is None) - - -class PythonParser(ParserBase): - def __init__(self, f: Union[FilePathOrBuffer, List], kwds): - """ - Workhorse function for processing nested list into DataFrame - """ - ParserBase.__init__(self, kwds) - - self.data: Optional[Iterator[str]] = None - self.buf: List = [] - self.pos = 0 - self.line_pos = 0 - - self.skiprows = kwds["skiprows"] - - if callable(self.skiprows): - self.skipfunc = self.skiprows - else: - self.skipfunc = lambda x: x in self.skiprows - - self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"]) - self.delimiter = kwds["delimiter"] - - self.quotechar = kwds["quotechar"] - if isinstance(self.quotechar, str): - self.quotechar = str(self.quotechar) - - self.escapechar = kwds["escapechar"] - self.doublequote = kwds["doublequote"] - self.skipinitialspace = kwds["skipinitialspace"] - self.lineterminator = kwds["lineterminator"] - self.quoting = kwds["quoting"] - self.usecols, _ = _validate_usecols_arg(kwds["usecols"]) - self.skip_blank_lines = kwds["skip_blank_lines"] - - self.warn_bad_lines = kwds["warn_bad_lines"] - self.error_bad_lines = kwds["error_bad_lines"] - - self.names_passed = kwds["names"] or None - - self.has_index_names = False - if "has_index_names" in kwds: - self.has_index_names = kwds["has_index_names"] - - self.verbose = kwds["verbose"] - self.converters = kwds["converters"] - - self.dtype = kwds["dtype"] - self.thousands = kwds["thousands"] - self.decimal = kwds["decimal"] - - self.comment = kwds["comment"] - - # Set self.data to something that can read lines. - if isinstance(f, list): - # read_excel: f is a list - self.data = cast(Iterator[str], f) - else: - self._open_handles(f, kwds) - assert self.handles is not None - assert hasattr(self.handles.handle, "readline") - try: - self._make_reader(self.handles.handle) - except (csv.Error, UnicodeDecodeError): - self.close() - raise - - # Get columns in two steps: infer from data, then - # infer column indices from self.usecols if it is specified. - self._col_indices: Optional[List[int]] = None - try: - ( - self.columns, - self.num_original_columns, - self.unnamed_cols, - ) = self._infer_columns() - except (TypeError, ValueError): - self.close() - raise - - # Now self.columns has the set of columns that we will process. - # The original set is stored in self.original_columns. - if len(self.columns) > 1: - # we are processing a multi index column - ( - self.columns, - self.index_names, - self.col_names, - _, - ) = self._extract_multi_indexer_columns( - self.columns, self.index_names, self.col_names - ) - # Update list of original names to include all indices. - self.num_original_columns = len(self.columns) - else: - self.columns = self.columns[0] - - # get popped off for index - self.orig_names = list(self.columns) - - # needs to be cleaned/refactored - # multiple date column thing turning into a real spaghetti factory - - if not self._has_complex_date_col: - (index_names, self.orig_names, self.columns) = self._get_index_name( - self.columns - ) - self._name_processed = True - if self.index_names is None: - self.index_names = index_names - - if self._col_indices is None: - self._col_indices = list(range(len(self.columns))) - - self._validate_parse_dates_presence(self.columns) - if self.parse_dates: - self._no_thousands_columns = self._set_no_thousands_columns() - else: - self._no_thousands_columns = None - - if len(self.decimal) != 1: - raise ValueError("Only length-1 decimal markers supported") - - decimal = re.escape(self.decimal) - if self.thousands is None: - regex = fr"^[\-\+]?[0-9]({decimal}[0-9])?([0-9]?(E\|e)\-?[0-9]+)?$" - else: - thousands = re.escape(self.thousands) - regex = ( - fr"^[\-\+]?([0-9]+{thousands}\|[0-9])({decimal}[0-9])?" - fr"([0-9]?(E\|e)\-?[0-9]+)?$" - ) - self.num = re.compile(regex) - - def _set_no_thousands_columns(self): - # Create a set of column ids that are not to be stripped of thousands - # operators. - noconvert_columns = set() - - def _set(x): - if is_integer(x): - noconvert_columns.add(x) - else: - assert self._col_indices is not None - col_indices = self._col_indices - noconvert_columns.add(col_indices[self.columns.index(x)]) - - if isinstance(self.parse_dates, list): - for val in self.parse_dates: - if isinstance(val, list): - for k in val: - _set(k) - else: - _set(val) - - elif isinstance(self.parse_dates, dict): - for val in self.parse_dates.values(): - if isinstance(val, list): - for k in val: - _set(k) - else: - _set(val) - - elif self.parse_dates: - if isinstance(self.index_col, list): - for k in self.index_col: - _set(k) - elif self.index_col is not None: - _set(self.index_col) - - return noconvert_columns - - def _make_reader(self, f): - sep = self.delimiter - - if sep is None or len(sep) == 1: - if self.lineterminator: - raise ValueError( - "Custom line terminators not supported in python parser (yet)" - ) - - class MyDialect(csv.Dialect): - delimiter = self.delimiter - quotechar = self.quotechar - escapechar = self.escapechar - doublequote = self.doublequote - skipinitialspace = self.skipinitialspace - quoting = self.quoting - lineterminator = "\n" - - dia = MyDialect - - if sep is not None: - dia.delimiter = sep - else: - # attempt to sniff the delimiter from the first valid line, - # i.e. no comment line and not in skiprows - line = f.readline() - lines = self._check_comments([[line]])[0] - while self.skipfunc(self.pos) or not lines: - self.pos += 1 - line = f.readline() - lines = self._check_comments([[line]])[0] - - # since `line` was a string, lines will be a list containing - # only a single string - line = lines[0] - - self.pos += 1 - self.line_pos += 1 - sniffed = csv.Sniffer().sniff(line) - dia.delimiter = sniffed.delimiter - - # Note: encoding is irrelevant here - line_rdr = csv.reader(StringIO(line), dialect=dia) - self.buf.extend(list(line_rdr)) - - # Note: encoding is irrelevant here - reader = csv.reader(f, dialect=dia, strict=True) - - else: - - def _read(): - line = f.readline() - pat = re.compile(sep) - - yield pat.split(line.strip()) - - for line in f: - yield pat.split(line.strip()) - - reader = _read() - - # pandas\io\parsers.py:2427: error: Incompatible types in assignment - # (expression has type "_reader", variable has type "Union[IO[Any], - # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap, None]") - # [assignment] - self.data = reader # type: ignore[assignment] - - def read(self, rows=None): - try: - content = self._get_lines(rows) - except StopIteration: - if self._first_chunk: - content = [] - else: - self.close() - raise - - # done with first read, next time raise StopIteration - self._first_chunk = False - - # pandas\io\parsers.py:2480: error: Argument 1 to "list" has - # incompatible type "Optional[Any]"; expected "Iterable[Any]" - # [arg-type] - columns = list(self.orig_names) # type: ignore[arg-type] - if not len(content): # pragma: no cover - # DataFrame with the right metadata, even though it's length 0 - names = self._maybe_dedup_names(self.orig_names) - index, columns, col_dict = _get_empty_meta( - names, self.index_col, self.index_names, self.dtype - ) - columns = self._maybe_make_multi_index_columns(columns, self.col_names) - return index, columns, col_dict - - # handle new style for names in index - count_empty_content_vals = count_empty_vals(content[0]) - indexnamerow = None - if self.has_index_names and count_empty_content_vals == len(columns): - indexnamerow = content[0] - content = content[1:] - - alldata = self._rows_to_cols(content) - data, columns = self._exclude_implicit_index(alldata) - - columns, data = self._do_date_conversions(columns, data) - - data = self._convert_data(data) - index, columns = self._make_index(data, alldata, columns, indexnamerow) - - return index, columns, data - - def _exclude_implicit_index(self, alldata): - names = self._maybe_dedup_names(self.orig_names) - - offset = 0 - if self._implicit_index: - offset = len(self.index_col) - - if self._col_indices is not None and len(names) != len(self._col_indices): - names = [names[i] for i in sorted(self._col_indices)] - - return {name: alldata[i + offset] for i, name in enumerate(names)}, names - - # legacy - def get_chunk(self, size=None): - if size is None: - # pandas\io\parsers.py:2528: error: "PythonParser" has no attribute - # "chunksize" [attr-defined] - size = self.chunksize # type: ignore[attr-defined] - return self.read(rows=size) - - def _convert_data(self, data): - # apply converters - def _clean_mapping(mapping): - """converts col numbers to names""" - clean = {} - for col, v in mapping.items(): - # pandas\io\parsers.py:2537: error: Unsupported right operand - # type for in ("Optional[Any]") [operator] - if ( - isinstance(col, int) - and col not in self.orig_names # type: ignore[operator] - ): - # pandas\io\parsers.py:2538: error: Value of type - # "Optional[Any]" is not indexable [index] - col = self.orig_names[col] # type: ignore[index] - clean[col] = v - return clean - - clean_conv = _clean_mapping(self.converters) - if not isinstance(self.dtype, dict): - # handles single dtype applied to all columns - clean_dtypes = self.dtype - else: - clean_dtypes = _clean_mapping(self.dtype) - - # Apply NA values. - clean_na_values = {} - clean_na_fvalues = {} - - if isinstance(self.na_values, dict): - for col in self.na_values: - na_value = self.na_values[col] - na_fvalue = self.na_fvalues[col] - - # pandas\io\parsers.py:2558: error: Unsupported right operand - # type for in ("Optional[Any]") [operator] - if ( - isinstance(col, int) - and col not in self.orig_names # type: ignore[operator] - ): - # pandas\io\parsers.py:2559: error: Value of type - # "Optional[Any]" is not indexable [index] - col = self.orig_names[col] # type: ignore[index] - - clean_na_values[col] = na_value - clean_na_fvalues[col] = na_fvalue - else: - clean_na_values = self.na_values - clean_na_fvalues = self.na_fvalues - - return self._convert_to_ndarrays( - data, - clean_na_values, - clean_na_fvalues, - self.verbose, - clean_conv, - clean_dtypes, - ) - - def _infer_columns(self): - names = self.names - num_original_columns = 0 - clear_buffer = True - # pandas\io\parsers.py:2580: error: Need type annotation for - # 'unnamed_cols' (hint: "unnamed_cols: Set[<type>] = ...") - # [var-annotated] - unnamed_cols = set() # type: ignore[var-annotated] - - if self.header is not None: - header = self.header - - if isinstance(header, (list, tuple, np.ndarray)): - have_mi_columns = len(header) > 1 - # we have a mi columns, so read an extra line - if have_mi_columns: - header = list(header) + [header[-1] + 1] - else: - have_mi_columns = False - header = [header] - - # pandas\io\parsers.py:2594: error: Need type annotation for - # 'columns' (hint: "columns: List[<type>] = ...") [var-annotated] - columns = [] # type: ignore[var-annotated] - for level, hr in enumerate(header): - try: - line = self._buffered_line() - - while self.line_pos <= hr: - line = self._next_line() - - except StopIteration as err: - if self.line_pos < hr: - raise ValueError( - f"Passed header={hr} but only {self.line_pos + 1} lines in " - "file" - ) from err - - # We have an empty file, so check - # if columns are provided. That will - # serve as the 'line' for parsing - if have_mi_columns and hr > 0: - if clear_buffer: - self._clear_buffer() - columns.append([None] * len(columns[-1])) - return columns, num_original_columns, unnamed_cols - - if not self.names: - raise EmptyDataError("No columns to parse from file") from err - - line = self.names[:] - - this_columns = [] - this_unnamed_cols = [] - - for i, c in enumerate(line): - if c == "": - if have_mi_columns: - col_name = f"Unnamed: {i}_level_{level}" - else: - col_name = f"Unnamed: {i}" - - this_unnamed_cols.append(i) - this_columns.append(col_name) - else: - this_columns.append(c) - - if not have_mi_columns and self.mangle_dupe_cols: - # pandas\io\parsers.py:2639: error: Need type annotation - # for 'counts' [var-annotated] - counts = defaultdict(int) # type: ignore[var-annotated] - - for i, col in enumerate(this_columns): - cur_count = counts[col] - - while cur_count > 0: - counts[col] = cur_count + 1 - col = f"{col}.{cur_count}" - cur_count = counts[col] - - this_columns[i] = col - counts[col] = cur_count + 1 - elif have_mi_columns: - - # if we have grabbed an extra line, but its not in our - # format so save in the buffer, and create an blank extra - # line for the rest of the parsing code - if hr == header[-1]: - lc = len(this_columns) - ic = len(self.index_col) if self.index_col is not None else 0 - unnamed_count = len(this_unnamed_cols) - - if lc != unnamed_count and lc - ic > unnamed_count: - clear_buffer = False - # pandas\io\parsers.py:2663: error: List item 0 has - # incompatible type "None"; expected "str" - # [list-item] - this_columns = [None] * lc # type: ignore[list-item] - self.buf = [self.buf[-1]] - - # pandas\io\parsers.py:2666: error: Argument 1 to "append" of - # "list" has incompatible type "List[str]"; expected - # "List[None]" [arg-type] - columns.append(this_columns) # type: ignore[arg-type] - unnamed_cols.update({this_columns[i] for i in this_unnamed_cols}) - - if len(columns) == 1: - num_original_columns = len(this_columns) - - if clear_buffer: - self._clear_buffer() - - if names is not None: - if len(names) > len(columns[0]): - raise ValueError( - "Number of passed names did not match " - "number of header fields in the file" - ) - if len(columns) > 1: - raise TypeError("Cannot pass names with multi-index columns") - - if self.usecols is not None: - # Set _use_cols. We don't store columns because they are - # overwritten. - self._handle_usecols(columns, names) - else: - num_original_columns = len(names) - columns = [names] - else: - columns = self._handle_usecols(columns, columns[0]) - else: - try: - line = self._buffered_line() - - except StopIteration as err: - if not names: - raise EmptyDataError("No columns to parse from file") from err - - line = names[:] - - ncols = len(line) - num_original_columns = ncols - - if not names: - if self.prefix: - # pandas\io\parsers.py:2711: error: List comprehension has - # incompatible type List[str]; expected List[None] [misc] - columns = [ - [ - f"{self.prefix}{i}" # type: ignore[misc] - for i in range(ncols) - ] - ] - else: - # pandas\io\parsers.py:2713: error: Argument 1 to "list" - # has incompatible type "range"; expected "Iterable[None]" - # [arg-type] - columns = [list(range(ncols))] # type: ignore[arg-type] - columns = self._handle_usecols(columns, columns[0]) - else: - if self.usecols is None or len(names) >= num_original_columns: - columns = self._handle_usecols([names], names) - num_original_columns = len(names) - else: - if not callable(self.usecols) and len(names) != len(self.usecols): - raise ValueError( - "Number of passed names did not match number of " - "header fields in the file" - ) - # Ignore output but set used columns. - self._handle_usecols([names], names) - columns = [names] - num_original_columns = ncols - - return columns, num_original_columns, unnamed_cols - - def _handle_usecols(self, columns, usecols_key): - """ - Sets self._col_indices - - usecols_key is used if there are string usecols. - """ - if self.usecols is not None: - if callable(self.usecols): - col_indices = _evaluate_usecols(self.usecols, usecols_key) - elif any(isinstance(u, str) for u in self.usecols): - if len(columns) > 1: - raise ValueError( - "If using multiple headers, usecols must be integers." - ) - col_indices = [] - - for col in self.usecols: - if isinstance(col, str): - try: - col_indices.append(usecols_key.index(col)) - except ValueError: - _validate_usecols_names(self.usecols, usecols_key) - else: - col_indices.append(col) - else: - col_indices = self.usecols - - columns = [ - [n for i, n in enumerate(column) if i in col_indices] - for column in columns - ] - self._col_indices = sorted(col_indices) - return columns - - def _buffered_line(self): - """ - Return a line from buffer, filling buffer if required. - """ - if len(self.buf) > 0: - return self.buf[0] - else: - return self._next_line() - - def _check_for_bom(self, first_row): - """ - Checks whether the file begins with the BOM character. - If it does, remove it. In addition, if there is quoting - in the field subsequent to the BOM, remove it as well - because it technically takes place at the beginning of - the name, not the middle of it. - """ - # first_row will be a list, so we need to check - # that that list is not empty before proceeding. - if not first_row: - return first_row - - # The first element of this row is the one that could have the - # BOM that we want to remove. Check that the first element is a - # string before proceeding. - if not isinstance(first_row[0], str): - return first_row - - # Check that the string is not empty, as that would - # obviously not have a BOM at the start of it. - if not first_row[0]: - return first_row - - # Since the string is non-empty, check that it does - # in fact begin with a BOM. - first_elt = first_row[0][0] - if first_elt != _BOM: - return first_row - - first_row_bom = first_row[0] - - if len(first_row_bom) > 1 and first_row_bom[1] == self.quotechar: - start = 2 - quote = first_row_bom[1] - end = first_row_bom[2:].index(quote) + 2 - - # Extract the data between the quotation marks - new_row = first_row_bom[start:end] - - # Extract any remaining data after the second - # quotation mark. - if len(first_row_bom) > end + 1: - new_row += first_row_bom[end + 1 :] - - else: - - # No quotation so just remove BOM from first element - new_row = first_row_bom[1:] - return [new_row] + first_row[1:] - - def _is_line_empty(self, line): - """ - Check if a line is empty or not. - - Parameters - ---------- - line : str, array-like - The line of data to check. - - Returns - ------- - boolean : Whether or not the line is empty. - """ - return not line or all(not x for x in line) - - def _next_line(self): - if isinstance(self.data, list): - while self.skipfunc(self.pos): - self.pos += 1 - - while True: - try: - line = self._check_comments([self.data[self.pos]])[0] - self.pos += 1 - # either uncommented or blank to begin with - if not self.skip_blank_lines and ( - self._is_line_empty(self.data[self.pos - 1]) or line - ): - break - elif self.skip_blank_lines: - ret = self._remove_empty_lines([line]) - if ret: - line = ret[0] - break - except IndexError: - raise StopIteration - else: - while self.skipfunc(self.pos): - self.pos += 1 - # assert for mypy, data is Iterator[str] or None, would error in next - assert self.data is not None - next(self.data) - - while True: - orig_line = self._next_iter_line(row_num=self.pos + 1) - self.pos += 1 - - if orig_line is not None: - line = self._check_comments([orig_line])[0] - - if self.skip_blank_lines: - ret = self._remove_empty_lines([line]) - - if ret: - line = ret[0] - break - elif self._is_line_empty(orig_line) or line: - break - - # This was the first line of the file, - # which could contain the BOM at the - # beginning of it. - if self.pos == 1: - line = self._check_for_bom(line) - - self.line_pos += 1 - self.buf.append(line) - return line - - def _alert_malformed(self, msg, row_num): - """ - Alert a user about a malformed row. - - If `self.error_bad_lines` is True, the alert will be `ParserError`. - If `self.warn_bad_lines` is True, the alert will be printed out. - - Parameters - ---------- - msg : The error message to display. - row_num : The row number where the parsing error occurred. - Because this row number is displayed, we 1-index, - even though we 0-index internally. - """ - if self.error_bad_lines: - raise ParserError(msg) - elif self.warn_bad_lines: - base = f"Skipping line {row_num}: " - sys.stderr.write(base + msg + "\n") - - def _next_iter_line(self, row_num): - """ - Wrapper around iterating through `self.data` (CSV source). - - When a CSV error is raised, we check for specific - error messages that allow us to customize the - error message displayed to the user. - - Parameters - ---------- - row_num : The row number of the line being parsed. - """ - try: - # assert for mypy, data is Iterator[str] or None, would error in next - assert self.data is not None - return next(self.data) - except csv.Error as e: - if self.warn_bad_lines or self.error_bad_lines: - msg = str(e) - - if "NULL byte" in msg or "line contains NUL" in msg: - msg = ( - "NULL byte detected. This byte " - "cannot be processed in Python's " - "native csv library at the moment, " - "so please pass in engine='c' instead" - ) - - if self.skipfooter > 0: - reason = ( - "Error could possibly be due to " - "parsing errors in the skipped footer rows " - "(the skipfooter keyword is only applied " - "after Python's csv library has parsed " - "all rows)." - ) - msg += ". " + reason - - self._alert_malformed(msg, row_num) - return None - - def _check_comments(self, lines): - if self.comment is None: - return lines - ret = [] - for line in lines: - rl = [] - for x in line: - if ( - not isinstance(x, str) - or self.comment not in x - or x in self.na_values - ): - rl.append(x) - else: - x = x[: x.find(self.comment)] - if len(x) > 0: - rl.append(x) - break - ret.append(rl) - return ret - - def _remove_empty_lines(self, lines): - """ - Iterate through the lines and remove any that are - either empty or contain only one whitespace value - - Parameters - ---------- - lines : array-like - The array of lines that we are to filter. - - Returns - ------- - filtered_lines : array-like - The same array of lines with the "empty" ones removed. - """ - ret = [] - for line in lines: - # Remove empty lines and lines with only one whitespace value - if ( - len(line) > 1 - or len(line) == 1 - and (not isinstance(line[0], str) or line[0].strip()) - ): - ret.append(line) - return ret - - def _check_thousands(self, lines): - if self.thousands is None: - return lines - - return self._search_replace_num_columns( - lines=lines, search=self.thousands, replace="" - ) - - def _search_replace_num_columns(self, lines, search, replace): - ret = [] - for line in lines: - rl = [] - for i, x in enumerate(line): - if ( - not isinstance(x, str) - or search not in x - or (self._no_thousands_columns and i in self._no_thousands_columns) - or not self.num.search(x.strip()) - ): - rl.append(x) - else: - rl.append(x.replace(search, replace)) - ret.append(rl) - return ret - - def _check_decimal(self, lines): - if self.decimal == _parser_defaults["decimal"]: - return lines - - return self._search_replace_num_columns( - lines=lines, search=self.decimal, replace="." - ) - - def _clear_buffer(self): - self.buf = [] - - _implicit_index = False - - def _get_index_name(self, columns): - """ - Try several cases to get lines: - - 0) There are headers on row 0 and row 1 and their - total summed lengths equals the length of the next line. - Treat row 0 as columns and row 1 as indices - 1) Look for implicit index: there are more columns - on row 1 than row 0. If this is true, assume that row - 1 lists index columns and row 0 lists normal columns. - 2) Get index from the columns if it was listed. - """ - orig_names = list(columns) - columns = list(columns) - - try: - line = self._next_line() - except StopIteration: - line = None - - try: - next_line = self._next_line() - except StopIteration: - next_line = None - - # implicitly index_col=0 b/c 1 fewer column names - implicit_first_cols = 0 - if line is not None: - # leave it 0, #2442 - # Case 1 - if self.index_col is not False: - implicit_first_cols = len(line) - self.num_original_columns - - # Case 0 - if next_line is not None: - if len(next_line) == len(line) + self.num_original_columns: - # column and index names on diff rows - self.index_col = list(range(len(line))) - self.buf = self.buf[1:] - - for c in reversed(line): - columns.insert(0, c) - - # Update list of original names to include all indices. - orig_names = list(columns) - self.num_original_columns = len(columns) - return line, orig_names, columns - - if implicit_first_cols > 0: - # Case 1 - self._implicit_index = True - if self.index_col is None: - self.index_col = list(range(implicit_first_cols)) - - index_name = None - - else: - # Case 2 - (index_name, columns_, self.index_col) = _clean_index_names( - columns, self.index_col, self.unnamed_cols - ) - - return index_name, orig_names, columns - - def _rows_to_cols(self, content): - col_len = self.num_original_columns - - if self._implicit_index: - col_len += len(self.index_col) - - max_len = max(len(row) for row in content) - - # Check that there are no rows with too many - # elements in their row (rows with too few - # elements are padded with NaN). - if max_len > col_len and self.index_col is not False and self.usecols is None: - - footers = self.skipfooter if self.skipfooter else 0 - bad_lines = [] - - iter_content = enumerate(content) - content_len = len(content) - content = [] - - for (i, l) in iter_content: - actual_len = len(l) - - if actual_len > col_len: - if self.error_bad_lines or self.warn_bad_lines: - row_num = self.pos - (content_len - i + footers) - bad_lines.append((row_num, actual_len)) - - if self.error_bad_lines: - break - else: - content.append(l) - - for row_num, actual_len in bad_lines: - msg = ( - f"Expected {col_len} fields in line {row_num + 1}, saw " - f"{actual_len}" - ) - if ( - self.delimiter - and len(self.delimiter) > 1 - and self.quoting != csv.QUOTE_NONE - ): - # see gh-13374 - reason = ( - "Error could possibly be due to quotes being " - "ignored when a multi-char delimiter is used." - ) - msg += ". " + reason - - self._alert_malformed(msg, row_num + 1) - - # see gh-13320 - zipped_content = list(lib.to_object_array(content, min_width=col_len).T) - - if self.usecols: - assert self._col_indices is not None - col_indices = self._col_indices - - if self._implicit_index: - zipped_content = [ - a - for i, a in enumerate(zipped_content) - if ( - i < len(self.index_col) - or i - len(self.index_col) in col_indices - ) - ] - else: - zipped_content = [ - a for i, a in enumerate(zipped_content) if i in col_indices - ] - return zipped_content - - def _get_lines(self, rows=None): - lines = self.buf - new_rows = None - - # already fetched some number - if rows is not None: - # we already have the lines in the buffer - if len(self.buf) >= rows: - new_rows, self.buf = self.buf[:rows], self.buf[rows:] - - # need some lines - else: - rows -= len(self.buf) - - if new_rows is None: - if isinstance(self.data, list): - if self.pos > len(self.data): - raise StopIteration - if rows is None: - new_rows = self.data[self.pos :] - new_pos = len(self.data) - else: - new_rows = self.data[self.pos : self.pos + rows] - new_pos = self.pos + rows - - # Check for stop rows. n.b.: self.skiprows is a set. - if self.skiprows: - new_rows = [ - row - for i, row in enumerate(new_rows) - if not self.skipfunc(i + self.pos) - ] - - lines.extend(new_rows) - self.pos = new_pos - - else: - new_rows = [] - try: - if rows is not None: - for _ in range(rows): - # assert for mypy, data is Iterator[str] or None, would - # error in next - assert self.data is not None - new_rows.append(next(self.data)) - lines.extend(new_rows) - else: - rows = 0 - - while True: - new_row = self._next_iter_line(row_num=self.pos + rows + 1) - rows += 1 - - if new_row is not None: - new_rows.append(new_row) - - except StopIteration: - if self.skiprows: - new_rows = [ - row - for i, row in enumerate(new_rows) - if not self.skipfunc(i + self.pos) - ] - lines.extend(new_rows) - if len(lines) == 0: - raise - self.pos += len(new_rows) - - self.buf = [] - else: - lines = new_rows - - if self.skipfooter: - lines = lines[: -self.skipfooter] - - lines = self._check_comments(lines) - if self.skip_blank_lines: - lines = self._remove_empty_lines(lines) - lines = self._check_thousands(lines) - return self._check_decimal(lines) - - -def _make_date_converter( - date_parser=None, dayfirst=False, infer_datetime_format=False, cache_dates=True -): - def converter(date_cols): - if date_parser is None: - strs = parsing.concat_date_cols(date_cols) - - try: - return tools.to_datetime( - ensure_object(strs), - utc=None, - dayfirst=dayfirst, - errors="ignore", - infer_datetime_format=infer_datetime_format, - cache=cache_dates, - ).to_numpy() - - except ValueError: - return tools.to_datetime( - parsing.try_parse_dates(strs, dayfirst=dayfirst), cache=cache_dates - ) - else: - try: - result = tools.to_datetime( - date_parser(date_cols), errors="ignore", cache=cache_dates - ) - if isinstance(result, datetime.datetime): - raise Exception("scalar parser") - return result - except Exception: - try: - return tools.to_datetime( - parsing.try_parse_dates( - parsing.concat_date_cols(date_cols), - parser=date_parser, - dayfirst=dayfirst, - ), - errors="ignore", - ) - except Exception: - return generic_parser(date_parser, date_cols) - - return converter - - -def _process_date_conversion( - data_dict, - converter, - parse_spec, - index_col, - index_names, - columns, - keep_date_col=False, -): - def _isindex(colspec): - return (isinstance(index_col, list) and colspec in index_col) or ( - isinstance(index_names, list) and colspec in index_names - ) - - new_cols = [] - new_data = {} - - orig_names = columns - columns = list(columns) - - date_cols = set() - - if parse_spec is None or isinstance(parse_spec, bool): - return data_dict, columns - - if isinstance(parse_spec, list): - # list of column lists - for colspec in parse_spec: - if is_scalar(colspec): - if isinstance(colspec, int) and colspec not in data_dict: - colspec = orig_names[colspec] - if _isindex(colspec): - continue - data_dict[colspec] = converter(data_dict[colspec]) - else: - new_name, col, old_names = _try_convert_dates( - converter, colspec, data_dict, orig_names - ) - if new_name in data_dict: - raise ValueError(f"New date column already in dict {new_name}") - new_data[new_name] = col - new_cols.append(new_name) - date_cols.update(old_names) - - elif isinstance(parse_spec, dict): - # dict of new name to column list - for new_name, colspec in parse_spec.items(): - if new_name in data_dict: - raise ValueError(f"Date column {new_name} already in dict") - - _, col, old_names = _try_convert_dates( - converter, colspec, data_dict, orig_names - ) - - new_data[new_name] = col - new_cols.append(new_name) - date_cols.update(old_names) - - data_dict.update(new_data) - new_cols.extend(columns) - - if not keep_date_col: - for c in list(date_cols): - data_dict.pop(c) - new_cols.remove(c) - - return data_dict, new_cols - - -def _try_convert_dates(parser, colspec, data_dict, columns): - colset = set(columns) - colnames = [] - - for c in colspec: - if c in colset: - colnames.append(c) - elif isinstance(c, int) and c not in columns: - colnames.append(columns[c]) - else: - colnames.append(c) - - new_name = "_".join(str(x) for x in colnames) - to_parse = [data_dict[c] for c in colnames if c in data_dict] - - new_col = parser(to_parse) - return new_name, new_col, colnames - - -def _clean_na_values(na_values, keep_default_na=True): - - if na_values is None: - if keep_default_na: - na_values = STR_NA_VALUES - else: - na_values = set() - # pandas\io\parsers.py:3387: error: Need type annotation for - # 'na_fvalues' (hint: "na_fvalues: Set[<type>] = ...") [var-annotated] - na_fvalues = set() # type: ignore[var-annotated] - elif isinstance(na_values, dict): - old_na_values = na_values.copy() - na_values = {} # Prevent aliasing. - - # Convert the values in the na_values dictionary - # into array-likes for further use. This is also - # where we append the default NaN values, provided - # that `keep_default_na=True`. - for k, v in old_na_values.items(): - if not is_list_like(v): - v = [v] - - if keep_default_na: - v = set(v) \| STR_NA_VALUES - - na_values[k] = v - # pandas\io\parsers.py:3404: error: Incompatible types in assignment - # (expression has type "Dict[Any, Any]", variable has type "Set[Any]") - # [assignment] - na_fvalues = { # type: ignore[assignment] - k: _floatify_na_values(v) for k, v in na_values.items() - } - else: - if not is_list_like(na_values): - na_values = [na_values] - na_values = _stringify_na_values(na_values) - if keep_default_na: - na_values = na_values \| STR_NA_VALUES - - na_fvalues = _floatify_na_values(na_values) - - return na_values, na_fvalues - - -def _clean_index_names(columns, index_col, unnamed_cols): - if not _is_index_col(index_col): - return None, columns, index_col - - columns = list(columns) - - # In case of no rows and multiindex columns we have to set index_names to - # list of Nones GH#38292 - if not columns: - return [None] * len(index_col), columns, index_col - - cp_cols = list(columns) - index_names = [] - - # don't mutate - index_col = list(index_col) - - for i, c in enumerate(index_col): - if isinstance(c, str): - index_names.append(c) - for j, name in enumerate(cp_cols): - if name == c: - index_col[i] = j - columns.remove(name) - break - else: - name = cp_cols[c] - columns.remove(name) - index_names.append(name) - - # Only clean index names that were placeholders. - for i, name in enumerate(index_names): - if isinstance(name, str) and name in unnamed_cols: - # pandas\io\parsers.py:3445: error: No overload variant of - # "__setitem__" of "list" matches argument types "int", "None" - # [call-overload] - index_names[i] = None # type: ignore[call-overload] - - return index_names, columns, index_col - - -def _get_empty_meta(columns, index_col, index_names, dtype: Optional[DtypeArg] = None): - columns = list(columns) - - # Convert `dtype` to a defaultdict of some kind. - # This will enable us to write `dtype[col_name]` - # without worrying about KeyError issues later on. - if not is_dict_like(dtype): - # if dtype == None, default will be object. - default_dtype = dtype or object - dtype = defaultdict(lambda: default_dtype) - else: - dtype = cast(dict, dtype) - dtype = defaultdict( - lambda: object, - {columns[k] if is_integer(k) else k: v for k, v in dtype.items()}, - ) - - # Even though we have no data, the "index" of the empty DataFrame - # could for example still be an empty MultiIndex. Thus, we need to - # check whether we have any index columns specified, via either: - # - # 1) index_col (column indices) - # 2) index_names (column names) - # - # Both must be non-null to ensure a successful construction. Otherwise, - # we have to create a generic empty Index. - if (index_col is None or index_col is False) or index_names is None: - index = Index([]) - else: - data = [Series([], dtype=dtype[name]) for name in index_names] - index = ensure_index_from_sequences(data, names=index_names) - index_col.sort() - - for i, n in enumerate(index_col): - columns.pop(n - i) - - col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns} - - return index, columns, col_dict - - -def _floatify_na_values(na_values): - # create float versions of the na_values - result = set() - for v in na_values: - try: - v = float(v) - if not np.isnan(v): - result.add(v) - except (TypeError, ValueError, OverflowError): - pass - return result - - -def _stringify_na_values(na_values): - """ return a stringified and numeric for these values """ - result = [] - for x in na_values: - result.append(str(x)) - result.append(x) - try: - v = float(x) - - # we are like 999 here - if v == int(v): - v = int(v) - result.append(f"{v}.0") - result.append(str(v)) - - # pandas\io\parsers.py:3522: error: Argument 1 to "append" of - # "list" has incompatible type "float"; expected "str" [arg-type] - result.append(v) # type: ignore[arg-type] - except (TypeError, ValueError, OverflowError): - pass - try: - # pandas\io\parsers.py:3526: error: Argument 1 to "append" of - # "list" has incompatible type "int"; expected "str" [arg-type] - result.append(int(x)) # type: ignore[arg-type] - except (TypeError, ValueError, OverflowError): - pass - return set(result) - - -def _get_na_values(col, na_values, na_fvalues, keep_default_na): - """ - Get the NaN values for a given column. - - Parameters - ---------- - col : str - The name of the column. - na_values : array-like, dict - The object listing the NaN values as strings. - na_fvalues : array-like, dict - The object listing the NaN values as floats. - keep_default_na : bool - If `na_values` is a dict, and the column is not mapped in the - dictionary, whether to return the default NaN values or the empty set. - - Returns - ------- - nan_tuple : A length-two tuple composed of - - 1) na_values : the string NaN values for that column. - 2) na_fvalues : the float NaN values for that column. - """ - if isinstance(na_values, dict): - if col in na_values: - return na_values[col], na_fvalues[col] - else: - if keep_default_na: - return STR_NA_VALUES, set() - - return set(), set() - else: - return na_values, na_fvalues - - -def _get_col_names(colspec, columns): - colset = set(columns) - colnames = [] - for c in colspec: - if c in colset: - colnames.append(c) - elif isinstance(c, int): - colnames.append(columns[c]) - return colnames - - -class FixedWidthReader(abc.Iterator): - """ - A reader of fixed-width lines. - """ - - def __init__(self, f, colspecs, delimiter, comment, skiprows=None, infer_nrows=100): - self.f = f - self.buffer = None - self.delimiter = "\r\n" + delimiter if delimiter else "\n\r\t " - self.comment = comment - if colspecs == "infer": - self.colspecs = self.detect_colspecs( - infer_nrows=infer_nrows, skiprows=skiprows - ) - else: - self.colspecs = colspecs - - if not isinstance(self.colspecs, (tuple, list)): - raise TypeError( - "column specifications must be a list or tuple, " - f"input was a {type(colspecs).__name__}" - ) - - for colspec in self.colspecs: - if not ( - isinstance(colspec, (tuple, list)) - and len(colspec) == 2 - and isinstance(colspec[0], (int, np.integer, type(None))) - and isinstance(colspec[1], (int, np.integer, type(None))) - ): - raise TypeError( - "Each column specification must be " - "2 element tuple or list of integers" - ) - - def get_rows(self, infer_nrows, skiprows=None): - """ - Read rows from self.f, skipping as specified. - - We distinguish buffer_rows (the first <= infer_nrows - lines) from the rows returned to detect_colspecs - because it's simpler to leave the other locations - with skiprows logic alone than to modify them to - deal with the fact we skipped some rows here as - well. - - Parameters - ---------- - infer_nrows : int - Number of rows to read from self.f, not counting - rows that are skipped. - skiprows: set, optional - Indices of rows to skip. - - Returns - ------- - detect_rows : list of str - A list containing the rows to read. - - """ - if skiprows is None: - skiprows = set() - buffer_rows = [] - detect_rows = [] - for i, row in enumerate(self.f): - if i not in skiprows: - detect_rows.append(row) - buffer_rows.append(row) - if len(detect_rows) >= infer_nrows: - break - self.buffer = iter(buffer_rows) - return detect_rows - - def detect_colspecs(self, infer_nrows=100, skiprows=None): - # Regex escape the delimiters - delimiters = "".join(fr"\{x}" for x in self.delimiter) - pattern = re.compile(f"([^{delimiters}]+)") - rows = self.get_rows(infer_nrows, skiprows) - if not rows: - raise EmptyDataError("No rows from which to infer column width") - max_len = max(map(len, rows)) - mask = np.zeros(max_len + 1, dtype=int) - if self.comment is not None: - rows = [row.partition(self.comment)[0] for row in rows] - for row in rows: - for m in pattern.finditer(row): - mask[m.start() : m.end()] = 1 - shifted = np.roll(mask, 1) - shifted[0] = 0 - edges = np.where((mask ^ shifted) == 1)[0] - edge_pairs = list(zip(edges[::2], edges[1::2])) - return edge_pairs - - def __next__(self): - if self.buffer is not None: - try: - line = next(self.buffer) - except StopIteration: - self.buffer = None - line = next(self.f) - else: - line = next(self.f) - # Note: 'colspecs' is a sequence of half-open intervals. - return [line[fromm:to].strip(self.delimiter) for (fromm, to) in self.colspecs] - - -class FixedWidthFieldParser(PythonParser): - """ - Specialization that Converts fixed-width fields into DataFrames. - See PythonParser for details. - """ - - def __init__(self, f, kwds): - # Support iterators, convert to a list. - self.colspecs = kwds.pop("colspecs") - self.infer_nrows = kwds.pop("infer_nrows") - PythonParser.__init__(self, f, kwds) - - def _make_reader(self, f): - self.data = FixedWidthReader( - f, - self.colspecs, - self.delimiter, - self.comment, - self.skiprows, - self.infer_nrows, - ) - - def _remove_empty_lines(self, lines) -> List: - """ - Returns the list of lines without the empty ones. With fixed-width - fields, empty lines become arrays of empty strings. - - See PythonParser._remove_empty_lines. - """ - return [ - line - for line in lines - if any(not isinstance(e, str) or e.strip() for e in line) - ] - - -def _refine_defaults_read( - dialect: Union[str, csv.Dialect], - delimiter: Union[str, object], - delim_whitespace: bool, - engine: str, - sep: Union[str, object], - defaults: Dict[str, Any], -): - """Validate/refine default values of input parameters of read_csv, read_table. - - Parameters - ---------- - dialect : str or csv.Dialect - If provided, this parameter will override values (default or not) for the - following parameters: `delimiter`, `doublequote`, `escapechar`, - `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to - override values, a ParserWarning will be issued. See csv.Dialect - documentation for more details. - delimiter : str or object - Alias for sep. - delim_whitespace : bool - Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be - used as the sep. Equivalent to setting ``sep='\\s+'``. If this option - is set to True, nothing should be passed in for the ``delimiter`` - parameter. - engine : {{'c', 'python'}} - Parser engine to use. The C engine is faster while the python engine is - currently more feature-complete. - sep : str or object - A delimiter provided by the user (str) or a sentinel value, i.e. - pandas._libs.lib.no_default. - defaults: dict - Default values of input parameters. - - Returns - ------- - kwds : dict - Input parameters with correct values. - - Raises - ------ - ValueError : If a delimiter was specified with ``sep`` (or ``delimiter``) and - ``delim_whitespace=True``. - """ - # fix types for sep, delimiter to Union(str, Any) - delim_default = defaults["delimiter"] - kwds: Dict[str, Any] = {} - # gh-23761 - # - # When a dialect is passed, it overrides any of the overlapping - # parameters passed in directly. We don't want to warn if the - # default parameters were passed in (since it probably means - # that the user didn't pass them in explicitly in the first place). - # - # "delimiter" is the annoying corner case because we alias it to - # "sep" before doing comparison to the dialect values later on. - # Thus, we need a flag to indicate that we need to "override" - # the comparison to dialect values by checking if default values - # for BOTH "delimiter" and "sep" were provided. - if dialect is not None: - kwds["sep_override"] = delimiter is None and ( - sep is lib.no_default or sep == delim_default - ) - - # Alias sep -> delimiter. - if delimiter is None: - delimiter = sep - - if delim_whitespace and (delimiter is not lib.no_default): - raise ValueError( - "Specified a delimiter with both sep and " - "delim_whitespace=True; you can only specify one." - ) - - if delimiter is lib.no_default: - # assign default separator value - kwds["delimiter"] = delim_default - else: - kwds["delimiter"] = delimiter - - if engine is not None: - kwds["engine_specified"] = True - else: - kwds["engine"] = "c" - kwds["engine_specified"] = False - - return kwds - - -def _extract_dialect(kwds: Dict[str, Any]) -> Optional[csv.Dialect]: - """ - Extract concrete csv dialect instance. - - Returns - ------- - csv.Dialect or None - """ - if kwds.get("dialect") is None: - return None - - dialect = kwds["dialect"] - if dialect in csv.list_dialects(): - dialect = csv.get_dialect(dialect) - - _validate_dialect(dialect) - - return dialect - - -MANDATORY_DIALECT_ATTRS = ( - "delimiter", - "doublequote", - "escapechar", - "skipinitialspace", - "quotechar", - "quoting", -) - - -def _validate_dialect(dialect: csv.Dialect) -> None: - """ - Validate csv dialect instance. - - Raises - ------ - ValueError - If incorrect dialect is provided. - """ - for param in MANDATORY_DIALECT_ATTRS: - if not hasattr(dialect, param): - raise ValueError(f"Invalid dialect {dialect} provided") - - -def _merge_with_dialect_properties( - dialect: csv.Dialect, - defaults: Dict[str, Any], -) -> Dict[str, Any]: - """ - Merge default kwargs in TextFileReader with dialect parameters. - - Parameters - ---------- - dialect : csv.Dialect - Concrete csv dialect. See csv.Dialect documentation for more details. - defaults : dict - Keyword arguments passed to TextFileReader. - - Returns - ------- - kwds : dict - Updated keyword arguments, merged with dialect parameters. - """ - kwds = defaults.copy() - - for param in MANDATORY_DIALECT_ATTRS: - dialect_val = getattr(dialect, param) - - parser_default = _parser_defaults[param] - provided = kwds.get(param, parser_default) - - # Messages for conflicting values between the dialect - # instance and the actual parameters provided. - conflict_msgs = [] - - # Don't warn if the default parameter was passed in, - # even if it conflicts with the dialect (gh-23761). - if provided != parser_default and provided != dialect_val: - msg = ( - f"Conflicting values for '{param}': '{provided}' was " - f"provided, but the dialect specifies '{dialect_val}'. " - "Using the dialect-specified value." - ) - - # Annoying corner case for not warning about - # conflicts between dialect and delimiter parameter. - # Refer to the outer "_read_" function for more info. - if not (param == "delimiter" and kwds.pop("sep_override", False)): - conflict_msgs.append(msg) - - if conflict_msgs: - warnings.warn("\n\n".join(conflict_msgs), ParserWarning, stacklevel=2) - kwds[param] = dialect_val - return kwds - - -def _validate_skipfooter(kwds: Dict[str, Any]) -> None: - """ - Check whether skipfooter is compatible with other kwargs in TextFileReader. - - Parameters - ---------- - kwds : dict - Keyword arguments passed to TextFileReader. - - Raises - ------ - ValueError - If skipfooter is not compatible with other parameters. - """ - if kwds.get("skipfooter"): - if kwds.get("iterator") or kwds.get("chunksize"): - raise ValueError("'skipfooter' not supported for iteration") - if kwds.get("nrows"): - raise ValueError("'skipfooter' not supported with 'nrows'") diff --git a/pandas/io/parsers/__init__.py b/pandas/io/parsers/__init__.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/__init__.py @@ -0,0 +1,9 @@ +from pandas.io.parsers.readers import ( + TextFileReader, + TextParser, + read_csv, + read_fwf, + read_table, +) + +__all__ = ["TextFileReader", "TextParser", "read_csv", "read_fwf", "read_table"] diff --git a/pandas/io/parsers/base_parser.py b/pandas/io/parsers/base_parser.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/base_parser.py @@ -0,0 +1,1085 @@ +from collections import defaultdict +import csv +import datetime +import itertools +from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Union, cast +import warnings + +import numpy as np + +import pandas._libs.lib as lib +import pandas._libs.ops as libops +import pandas._libs.parsers as parsers +from pandas._libs.parsers import STR_NA_VALUES +from pandas._libs.tslibs import parsing +from pandas._typing import DtypeArg, FilePathOrBuffer +from pandas.errors import ParserError, ParserWarning + +from pandas.core.dtypes.cast import astype_nansafe +from pandas.core.dtypes.common import ( + ensure_object, + ensure_str, + is_bool_dtype, + is_categorical_dtype, + is_dict_like, + is_dtype_equal, + is_extension_array_dtype, + is_integer, + is_integer_dtype, + is_list_like, + is_object_dtype, + is_scalar, + is_string_dtype, + pandas_dtype, +) +from pandas.core.dtypes.dtypes import CategoricalDtype +from pandas.core.dtypes.missing import isna + +from pandas.core import algorithms +from pandas.core.arrays import Categorical +from pandas.core.indexes.api import Index, MultiIndex, ensure_index_from_sequences +from pandas.core.series import Series +from pandas.core.tools import datetimes as tools + +from pandas.io.common import IOHandles, get_handle +from pandas.io.date_converters import generic_parser + +parser_defaults = { + "delimiter": None, + "escapechar": None, + "quotechar": '"', + "quoting": csv.QUOTE_MINIMAL, + "doublequote": True, + "skipinitialspace": False, + "lineterminator": None, + "header": "infer", + "index_col": None, + "names": None, + "prefix": None, + "skiprows": None, + "skipfooter": 0, + "nrows": None, + "na_values": None, + "keep_default_na": True, + "true_values": None, + "false_values": None, + "converters": None, + "dtype": None, + "cache_dates": True, + "thousands": None, + "comment": None, + "decimal": ".", + # 'engine': 'c', + "parse_dates": False, + "keep_date_col": False, + "dayfirst": False, + "date_parser": None, + "usecols": None, + # 'iterator': False, + "chunksize": None, + "verbose": False, + "encoding": None, + "squeeze": False, + "compression": None, + "mangle_dupe_cols": True, + "infer_datetime_format": False, + "skip_blank_lines": True, +} + + +class ParserBase: + def __init__(self, kwds): + + self.names = kwds.get("names") + self.orig_names: Optional[List] = None + self.prefix = kwds.pop("prefix", None) + + self.index_col = kwds.get("index_col", None) + self.unnamed_cols: Set = set() + self.index_names: Optional[List] = None + self.col_names = None + + self.parse_dates = _validate_parse_dates_arg(kwds.pop("parse_dates", False)) + self.date_parser = kwds.pop("date_parser", None) + self.dayfirst = kwds.pop("dayfirst", False) + self.keep_date_col = kwds.pop("keep_date_col", False) + + self.na_values = kwds.get("na_values") + self.na_fvalues = kwds.get("na_fvalues") + self.na_filter = kwds.get("na_filter", False) + self.keep_default_na = kwds.get("keep_default_na", True) + + self.true_values = kwds.get("true_values") + self.false_values = kwds.get("false_values") + self.mangle_dupe_cols = kwds.get("mangle_dupe_cols", True) + self.infer_datetime_format = kwds.pop("infer_datetime_format", False) + self.cache_dates = kwds.pop("cache_dates", True) + + self._date_conv = _make_date_converter( + date_parser=self.date_parser, + dayfirst=self.dayfirst, + infer_datetime_format=self.infer_datetime_format, + cache_dates=self.cache_dates, + ) + + # validate header options for mi + self.header = kwds.get("header") + if isinstance(self.header, (list, tuple, np.ndarray)): + if not all(map(is_integer, self.header)): + raise ValueError("header must be integer or list of integers") + if any(i < 0 for i in self.header): + raise ValueError( + "cannot specify multi-index header with negative integers" + ) + if kwds.get("usecols"): + raise ValueError( + "cannot specify usecols when specifying a multi-index header" + ) + if kwds.get("names"): + raise ValueError( + "cannot specify names when specifying a multi-index header" + ) + + # validate index_col that only contains integers + if self.index_col is not None: + is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray)) + if not ( + is_sequence + and all(map(is_integer, self.index_col)) + or is_integer(self.index_col) + ): + raise ValueError( + "index_col must only contain row numbers " + "when specifying a multi-index header" + ) + elif self.header is not None: + # GH 27394 + if self.prefix is not None: + raise ValueError( + "Argument prefix must be None if argument header is not None" + ) + # GH 16338 + elif not is_integer(self.header): + raise ValueError("header must be integer or list of integers") + # GH 27779 + elif self.header < 0: + raise ValueError( + "Passing negative integer to header is invalid. " + "For no header, use header=None instead" + ) + + self._name_processed = False + + self._first_chunk = True + + self.handles: Optional[IOHandles] = None + + def _open_handles(self, src: FilePathOrBuffer, kwds: Dict[str, Any]) -> None: + """ + Let the readers open IOHanldes after they are done with their potential raises. + """ + self.handles = get_handle( + src, + "r", + encoding=kwds.get("encoding", None), + compression=kwds.get("compression", None), + memory_map=kwds.get("memory_map", False), + storage_options=kwds.get("storage_options", None), + ) + + def _validate_parse_dates_presence(self, columns: List[str]) -> None: + """ + Check if parse_dates are in columns. + + If user has provided names for parse_dates, check if those columns + are available. + + Parameters + ---------- + columns : list + List of names of the dataframe. + + Raises + ------ + ValueError + If column to parse_date is not in dataframe. + + """ + cols_needed: Iterable + if is_dict_like(self.parse_dates): + cols_needed = itertools.chain(self.parse_dates.values()) + elif is_list_like(self.parse_dates): + # a column in parse_dates could be represented + # ColReference = Union[int, str] + # DateGroups = List[ColReference] + # ParseDates = Union[DateGroups, List[DateGroups], + # Dict[ColReference, DateGroups]] + cols_needed = itertools.chain.from_iterable( + col if is_list_like(col) else [col] for col in self.parse_dates + ) + else: + cols_needed = [] + + # get only columns that are references using names (str), not by index + missing_cols = ", ".join( + sorted( + { + col + for col in cols_needed + if isinstance(col, str) and col not in columns + } + ) + ) + if missing_cols: + raise ValueError( + f"Missing column provided to 'parse_dates': '{missing_cols}'" + ) + + def close(self): + if self.handles is not None: + self.handles.close() + + @property + def _has_complex_date_col(self): + return isinstance(self.parse_dates, dict) or ( + isinstance(self.parse_dates, list) + and len(self.parse_dates) > 0 + and isinstance(self.parse_dates[0], list) + ) + + def _should_parse_dates(self, i): + if isinstance(self.parse_dates, bool): + return self.parse_dates + else: + if self.index_names is not None: + name = self.index_names[i] + else: + name = None + j = i if self.index_col is None else self.index_col[i] + + if is_scalar(self.parse_dates): + return (j == self.parse_dates) or ( + name is not None and name == self.parse_dates + ) + else: + return (j in self.parse_dates) or ( + name is not None and name in self.parse_dates + ) + + def _extract_multi_indexer_columns( + self, header, index_names, col_names, passed_names=False + ): + """ + extract and return the names, index_names, col_names + header is a list-of-lists returned from the parsers + """ + if len(header) < 2: + return header[0], index_names, col_names, passed_names + + # the names are the tuples of the header that are not the index cols + # 0 is the name of the index, assuming index_col is a list of column + # numbers + ic = self.index_col + if ic is None: + ic = [] + + if not isinstance(ic, (list, tuple, np.ndarray)): + ic = [ic] + sic = set(ic) + + # clean the index_names + index_names = header.pop(-1) + index_names, _, _ = self._clean_index_names( + index_names, self.index_col, self.unnamed_cols + ) + + # extract the columns + field_count = len(header[0]) + + def extract(r): + return tuple(r[i] for i in range(field_count) if i not in sic) + + columns = list(zip((extract(r) for r in header))) + names = ic + columns + + # If we find unnamed columns all in a single + # level, then our header was too long. + for n in range(len(columns[0])): + if all(ensure_str(col[n]) in self.unnamed_cols for col in columns): + header = ",".join(str(x) for x in self.header) + raise ParserError( + f"Passed header=[{header}] are too many rows " + "for this multi_index of columns" + ) + + # Clean the column names (if we have an index_col). + if len(ic): + col_names = [ + r[0] if ((r[0] is not None) and r[0] not in self.unnamed_cols) else None + for r in header + ] + else: + col_names = [None] * len(header) + + passed_names = True + + return names, index_names, col_names, passed_names + + def _maybe_dedup_names(self, names): + # see gh-7160 and gh-9424: this helps to provide + # immediate alleviation of the duplicate names + # issue and appears to be satisfactory to users, + # but ultimately, not needing to butcher the names + # would be nice! + if self.mangle_dupe_cols: + names = list(names) # so we can index + # pandas\io\parsers.py:1559: error: Need type annotation for + # 'counts' [var-annotated] + counts = defaultdict(int) # type: ignore[var-annotated] + is_potential_mi = _is_potential_multi_index(names, self.index_col) + + for i, col in enumerate(names): + cur_count = counts[col] + + while cur_count > 0: + counts[col] = cur_count + 1 + + if is_potential_mi: + col = col[:-1] + (f"{col[-1]}.{cur_count}",) + else: + col = f"{col}.{cur_count}" + cur_count = counts[col] + + names[i] = col + counts[col] = cur_count + 1 + + return names + + def _maybe_make_multi_index_columns(self, columns, col_names=None): + # possibly create a column mi here + if _is_potential_multi_index(columns): + columns = MultiIndex.from_tuples(columns, names=col_names) + return columns + + def _make_index(self, data, alldata, columns, indexnamerow=False): + if not is_index_col(self.index_col) or not self.index_col: + index = None + + elif not self._has_complex_date_col: + index = self._get_simple_index(alldata, columns) + index = self._agg_index(index) + elif self._has_complex_date_col: + if not self._name_processed: + (self.index_names, _, self.index_col) = self._clean_index_names( + list(columns), self.index_col, self.unnamed_cols + ) + self._name_processed = True + index = self._get_complex_date_index(data, columns) + index = self._agg_index(index, try_parse_dates=False) + + # add names for the index + if indexnamerow: + coffset = len(indexnamerow) - len(columns) + # pandas\io\parsers.py:1604: error: Item "None" of "Optional[Any]" + # has no attribute "set_names" [union-attr] + index = index.set_names(indexnamerow[:coffset]) # type: ignore[union-attr] + + # maybe create a mi on the columns + columns = self._maybe_make_multi_index_columns(columns, self.col_names) + + return index, columns + + _implicit_index = False + + def _get_simple_index(self, data, columns): + def ix(col): + if not isinstance(col, str): + return col + raise ValueError(f"Index {col} invalid") + + to_remove = [] + index = [] + for idx in self.index_col: + i = ix(idx) + to_remove.append(i) + index.append(data[i]) + + # remove index items from content and columns, don't pop in + # loop + for i in sorted(to_remove, reverse=True): + data.pop(i) + if not self._implicit_index: + columns.pop(i) + + return index + + def _get_complex_date_index(self, data, col_names): + def _get_name(icol): + if isinstance(icol, str): + return icol + + if col_names is None: + raise ValueError(f"Must supply column order to use {icol!s} as index") + + for i, c in enumerate(col_names): + if i == icol: + return c + + to_remove = [] + index = [] + for idx in self.index_col: + name = _get_name(idx) + to_remove.append(name) + index.append(data[name]) + + # remove index items from content and columns, don't pop in + # loop + for c in sorted(to_remove, reverse=True): + data.pop(c) + col_names.remove(c) + + return index + + def _agg_index(self, index, try_parse_dates=True) -> Index: + arrays = [] + + for i, arr in enumerate(index): + + if try_parse_dates and self._should_parse_dates(i): + arr = self._date_conv(arr) + + if self.na_filter: + col_na_values = self.na_values + col_na_fvalues = self.na_fvalues + else: + col_na_values = set() + col_na_fvalues = set() + + if isinstance(self.na_values, dict): + # pandas\io\parsers.py:1678: error: Value of type + # "Optional[Any]" is not indexable [index] + col_name = self.index_names[i] # type: ignore[index] + if col_name is not None: + col_na_values, col_na_fvalues = _get_na_values( + col_name, self.na_values, self.na_fvalues, self.keep_default_na + ) + + arr, _ = self._infer_types(arr, col_na_values \| col_na_fvalues) + arrays.append(arr) + + names = self.index_names + index = ensure_index_from_sequences(arrays, names) + + return index + + def _convert_to_ndarrays( + self, dct, na_values, na_fvalues, verbose=False, converters=None, dtypes=None + ): + result = {} + for c, values in dct.items(): + conv_f = None if converters is None else converters.get(c, None) + if isinstance(dtypes, dict): + cast_type = dtypes.get(c, None) + else: + # single dtype or None + cast_type = dtypes + + if self.na_filter: + col_na_values, col_na_fvalues = _get_na_values( + c, na_values, na_fvalues, self.keep_default_na + ) + else: + col_na_values, col_na_fvalues = set(), set() + + if conv_f is not None: + # conv_f applied to data before inference + if cast_type is not None: + warnings.warn( + ( + "Both a converter and dtype were specified " + f"for column {c} - only the converter will be used" + ), + ParserWarning, + stacklevel=7, + ) + + try: + values = lib.map_infer(values, conv_f) + except ValueError: + mask = algorithms.isin(values, list(na_values)).view(np.uint8) + values = lib.map_infer_mask(values, conv_f, mask) + + cvals, na_count = self._infer_types( + values, set(col_na_values) \| col_na_fvalues, try_num_bool=False + ) + else: + is_ea = is_extension_array_dtype(cast_type) + is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type) + # skip inference if specified dtype is object + # or casting to an EA + try_num_bool = not (cast_type and is_str_or_ea_dtype) + + # general type inference and conversion + cvals, na_count = self._infer_types( + values, set(col_na_values) \| col_na_fvalues, try_num_bool + ) + + # type specified in dtype param or cast_type is an EA + if cast_type and ( + not is_dtype_equal(cvals, cast_type) + or is_extension_array_dtype(cast_type) + ): + if not is_ea and na_count > 0: + try: + if is_bool_dtype(cast_type): + raise ValueError( + f"Bool column has NA values in column {c}" + ) + except (AttributeError, TypeError): + # invalid input to is_bool_dtype + pass + cast_type = pandas_dtype(cast_type) + cvals = self._cast_types(cvals, cast_type, c) + + result[c] = cvals + if verbose and na_count: + print(f"Filled {na_count} NA values in column {c!s}") + return result + + def _infer_types(self, values, na_values, try_num_bool=True): + """ + Infer types of values, possibly casting + + Parameters + ---------- + values : ndarray + na_values : set + try_num_bool : bool, default try + try to cast values to numeric (first preference) or boolean + + Returns + ------- + converted : ndarray + na_count : int + """ + na_count = 0 + if issubclass(values.dtype.type, (np.number, np.bool_)): + mask = algorithms.isin(values, list(na_values)) + na_count = mask.sum() + if na_count > 0: + if is_integer_dtype(values): + values = values.astype(np.float64) + np.putmask(values, mask, np.nan) + return values, na_count + + if try_num_bool and is_object_dtype(values.dtype): + # exclude e.g DatetimeIndex here + try: + result = lib.maybe_convert_numeric(values, na_values, False) + except (ValueError, TypeError): + # e.g. encountering datetime string gets ValueError + # TypeError can be raised in floatify + result = values + na_count = parsers.sanitize_objects(result, na_values, False) + else: + na_count = isna(result).sum() + else: + result = values + if values.dtype == np.object_: + na_count = parsers.sanitize_objects(values, na_values, False) + + if result.dtype == np.object_ and try_num_bool: + result = libops.maybe_convert_bool( + np.asarray(values), + true_values=self.true_values, + false_values=self.false_values, + ) + + return result, na_count + + def _cast_types(self, values, cast_type, column): + """ + Cast values to specified type + + Parameters + ---------- + values : ndarray + cast_type : string or np.dtype + dtype to cast values to + column : string + column name - used only for error reporting + + Returns + ------- + converted : ndarray + """ + if is_categorical_dtype(cast_type): + known_cats = ( + isinstance(cast_type, CategoricalDtype) + and cast_type.categories is not None + ) + + if not is_object_dtype(values) and not known_cats: + # TODO: this is for consistency with + # c-parser which parses all categories + # as strings + values = astype_nansafe(values, str) + + cats = Index(values).unique().dropna() + values = Categorical._from_inferred_categories( + cats, cats.get_indexer(values), cast_type, true_values=self.true_values + ) + + # use the EA's implementation of casting + elif is_extension_array_dtype(cast_type): + # ensure cast_type is an actual dtype and not a string + cast_type = pandas_dtype(cast_type) + array_type = cast_type.construct_array_type() + try: + if is_bool_dtype(cast_type): + return array_type._from_sequence_of_strings( + values, + dtype=cast_type, + true_values=self.true_values, + false_values=self.false_values, + ) + else: + return array_type._from_sequence_of_strings(values, dtype=cast_type) + except NotImplementedError as err: + raise NotImplementedError( + f"Extension Array: {array_type} must implement " + "_from_sequence_of_strings in order to be used in parser methods" + ) from err + + else: + try: + values = astype_nansafe(values, cast_type, copy=True, skipna=True) + except ValueError as err: + raise ValueError( + f"Unable to convert column {column} to type {cast_type}" + ) from err + return values + + def _do_date_conversions(self, names, data): + # returns data, columns + + if self.parse_dates is not None: + data, names = _process_date_conversion( + data, + self._date_conv, + self.parse_dates, + self.index_col, + self.index_names, + names, + keep_date_col=self.keep_date_col, + ) + + return names, data + + def _evaluate_usecols(self, usecols, names): + """ + Check whether or not the 'usecols' parameter + is a callable. If so, enumerates the 'names' + parameter and returns a set of indices for + each entry in 'names' that evaluates to True. + If not a callable, returns 'usecols'. + """ + if callable(usecols): + return {i for i, name in enumerate(names) if usecols(name)} + return usecols + + def _validate_usecols_names(self, usecols, names): + """ + Validates that all usecols are present in a given + list of names. If not, raise a ValueError that + shows what usecols are missing. + + Parameters + ---------- + usecols : iterable of usecols + The columns to validate are present in names. + names : iterable of names + The column names to check against. + + Returns + ------- + usecols : iterable of usecols + The `usecols` parameter if the validation succeeds. + + Raises + ------ + ValueError : Columns were missing. Error message will list them. + """ + missing = [c for c in usecols if c not in names] + if len(missing) > 0: + raise ValueError( + f"Usecols do not match columns, columns expected but not found: " + f"{missing}" + ) + + return usecols + + def _validate_usecols_arg(self, usecols): + """ + Validate the 'usecols' parameter. + + Checks whether or not the 'usecols' parameter contains all integers + (column selection by index), strings (column by name) or is a callable. + Raises a ValueError if that is not the case. + + Parameters + ---------- + usecols : list-like, callable, or None + List of columns to use when parsing or a callable that can be used + to filter a list of table columns. + + Returns + ------- + usecols_tuple : tuple + A tuple of (verified_usecols, usecols_dtype). + + 'verified_usecols' is either a set if an array-like is passed in or + 'usecols' if a callable or None is passed in. + + 'usecols_dtype` is the inferred dtype of 'usecols' if an array-like + is passed in or None if a callable or None is passed in. + """ + msg = ( + "'usecols' must either be list-like of all strings, all unicode, " + "all integers or a callable." + ) + if usecols is not None: + if callable(usecols): + return usecols, None + + if not is_list_like(usecols): + # see gh-20529 + # + # Ensure it is iterable container but not string. + raise ValueError(msg) + + usecols_dtype = lib.infer_dtype(usecols, skipna=False) + + if usecols_dtype not in ("empty", "integer", "string"): + raise ValueError(msg) + + usecols = set(usecols) + + return usecols, usecols_dtype + return usecols, None + + def _clean_index_names(self, columns, index_col, unnamed_cols): + if not is_index_col(index_col): + return None, columns, index_col + + columns = list(columns) + + # In case of no rows and multiindex columns we have to set index_names to + # list of Nones GH#38292 + if not columns: + return [None] * len(index_col), columns, index_col + + cp_cols = list(columns) + index_names = [] + + # don't mutate + index_col = list(index_col) + + for i, c in enumerate(index_col): + if isinstance(c, str): + index_names.append(c) + for j, name in enumerate(cp_cols): + if name == c: + index_col[i] = j + columns.remove(name) + break + else: + name = cp_cols[c] + columns.remove(name) + index_names.append(name) + + # Only clean index names that were placeholders. + for i, name in enumerate(index_names): + if isinstance(name, str) and name in unnamed_cols: + # pandas\io\parsers.py:3445: error: No overload variant of + # "__setitem__" of "list" matches argument types "int", "None" + # [call-overload] + index_names[i] = None # type: ignore[call-overload] + + return index_names, columns, index_col + + def _get_empty_meta( + self, columns, index_col, index_names, dtype: Optional[DtypeArg] = None + ): + columns = list(columns) + + # Convert `dtype` to a defaultdict of some kind. + # This will enable us to write `dtype[col_name]` + # without worrying about KeyError issues later on. + if not is_dict_like(dtype): + # if dtype == None, default will be object. + default_dtype = dtype or object + dtype = defaultdict(lambda: default_dtype) + else: + dtype = cast(dict, dtype) + dtype = defaultdict( + lambda: object, + {columns[k] if is_integer(k) else k: v for k, v in dtype.items()}, + ) + + # Even though we have no data, the "index" of the empty DataFrame + # could for example still be an empty MultiIndex. Thus, we need to + # check whether we have any index columns specified, via either: + # + # 1) index_col (column indices) + # 2) index_names (column names) + # + # Both must be non-null to ensure a successful construction. Otherwise, + # we have to create a generic empty Index. + if (index_col is None or index_col is False) or index_names is None: + index = Index([]) + else: + data = [Series([], dtype=dtype[name]) for name in index_names] + index = ensure_index_from_sequences(data, names=index_names) + index_col.sort() + + for i, n in enumerate(index_col): + columns.pop(n - i) + + col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns} + + return index, columns, col_dict + + +def _make_date_converter( + date_parser=None, dayfirst=False, infer_datetime_format=False, cache_dates=True +): + def converter(date_cols): + if date_parser is None: + strs = parsing.concat_date_cols(date_cols) + + try: + return tools.to_datetime( + ensure_object(strs), + utc=None, + dayfirst=dayfirst, + errors="ignore", + infer_datetime_format=infer_datetime_format, + cache=cache_dates, + ).to_numpy() + + except ValueError: + return tools.to_datetime( + parsing.try_parse_dates(strs, dayfirst=dayfirst), cache=cache_dates + ) + else: + try: + result = tools.to_datetime( + date_parser(date_cols), errors="ignore", cache=cache_dates + ) + if isinstance(result, datetime.datetime): + raise Exception("scalar parser") + return result + except Exception: + try: + return tools.to_datetime( + parsing.try_parse_dates( + parsing.concat_date_cols(date_cols), + parser=date_parser, + dayfirst=dayfirst, + ), + errors="ignore", + ) + except Exception: + return generic_parser(date_parser, date_cols) + + return converter + + +def _process_date_conversion( + data_dict, + converter, + parse_spec, + index_col, + index_names, + columns, + keep_date_col=False, +): + def _isindex(colspec): + return (isinstance(index_col, list) and colspec in index_col) or ( + isinstance(index_names, list) and colspec in index_names + ) + + new_cols = [] + new_data = {} + + orig_names = columns + columns = list(columns) + + date_cols = set() + + if parse_spec is None or isinstance(parse_spec, bool): + return data_dict, columns + + if isinstance(parse_spec, list): + # list of column lists + for colspec in parse_spec: + if is_scalar(colspec): + if isinstance(colspec, int) and colspec not in data_dict: + colspec = orig_names[colspec] + if _isindex(colspec): + continue + data_dict[colspec] = converter(data_dict[colspec]) + else: + new_name, col, old_names = _try_convert_dates( + converter, colspec, data_dict, orig_names + ) + if new_name in data_dict: + raise ValueError(f"New date column already in dict {new_name}") + new_data[new_name] = col + new_cols.append(new_name) + date_cols.update(old_names) + + elif isinstance(parse_spec, dict): + # dict of new name to column list + for new_name, colspec in parse_spec.items(): + if new_name in data_dict: + raise ValueError(f"Date column {new_name} already in dict") + + _, col, old_names = _try_convert_dates( + converter, colspec, data_dict, orig_names + ) + + new_data[new_name] = col + new_cols.append(new_name) + date_cols.update(old_names) + + data_dict.update(new_data) + new_cols.extend(columns) + + if not keep_date_col: + for c in list(date_cols): + data_dict.pop(c) + new_cols.remove(c) + + return data_dict, new_cols + + +def _try_convert_dates(parser, colspec, data_dict, columns): + colset = set(columns) + colnames = [] + + for c in colspec: + if c in colset: + colnames.append(c) + elif isinstance(c, int) and c not in columns: + colnames.append(columns[c]) + else: + colnames.append(c) + + new_name = "_".join(str(x) for x in colnames) + to_parse = [data_dict[c] for c in colnames if c in data_dict] + + new_col = parser(to_parse) + return new_name, new_col, colnames + + +def _get_na_values(col, na_values, na_fvalues, keep_default_na): + """ + Get the NaN values for a given column. + + Parameters + ---------- + col : str + The name of the column. + na_values : array-like, dict + The object listing the NaN values as strings. + na_fvalues : array-like, dict + The object listing the NaN values as floats. + keep_default_na : bool + If `na_values` is a dict, and the column is not mapped in the + dictionary, whether to return the default NaN values or the empty set. + + Returns + ------- + nan_tuple : A length-two tuple composed of + + 1) na_values : the string NaN values for that column. + 2) na_fvalues : the float NaN values for that column. + """ + if isinstance(na_values, dict): + if col in na_values: + return na_values[col], na_fvalues[col] + else: + if keep_default_na: + return STR_NA_VALUES, set() + + return set(), set() + else: + return na_values, na_fvalues + + +# Seems to be unused +def _get_col_names(colspec, columns): + colset = set(columns) + colnames = [] + for c in colspec: + if c in colset: + colnames.append(c) + elif isinstance(c, int): + colnames.append(columns[c]) + return colnames + + +def _is_potential_multi_index( + columns, index_col: Optional[Union[bool, Sequence[int]]] = None +): + """ + Check whether or not the `columns` parameter + could be converted into a MultiIndex. + + Parameters + ---------- + columns : array-like + Object which may or may not be convertible into a MultiIndex + index_col : None, bool or list, optional + Column or columns to use as the (possibly hierarchical) index + + Returns + ------- + boolean : Whether or not columns could become a MultiIndex + """ + if index_col is None or isinstance(index_col, bool): + index_col = [] + + return ( + len(columns) + and not isinstance(columns, MultiIndex) + and all(isinstance(c, tuple) for c in columns if c not in list(index_col)) + ) + + +def _validate_parse_dates_arg(parse_dates): + """ + Check whether or not the 'parse_dates' parameter + is a non-boolean scalar. Raises a ValueError if + that is the case. + """ + msg = ( + "Only booleans, lists, and dictionaries are accepted " + "for the 'parse_dates' parameter" + ) + + if parse_dates is not None: + if is_scalar(parse_dates): + if not lib.is_bool(parse_dates): + raise TypeError(msg) + + elif not isinstance(parse_dates, (list, dict)): + raise TypeError(msg) + + return parse_dates + + +def is_index_col(col): + return col is not None and col is not False diff --git a/pandas/io/parsers/c_parser_wrapper.py b/pandas/io/parsers/c_parser_wrapper.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/c_parser_wrapper.py @@ -0,0 +1,328 @@ +import pandas._libs.parsers as parsers +from pandas._typing import FilePathOrBuffer + +from pandas.core.dtypes.common import is_integer + +from pandas.core.indexes.api import ensure_index_from_sequences + +from pandas.io.parsers.base_parser import ParserBase, is_index_col + + +class CParserWrapper(ParserBase): + def __init__(self, src: FilePathOrBuffer, kwds): + self.kwds = kwds + kwds = kwds.copy() + + ParserBase.__init__(self, kwds) + + # #2442 + kwds["allow_leading_cols"] = self.index_col is not False + + # GH20529, validate usecol arg before TextReader + self.usecols, self.usecols_dtype = self._validate_usecols_arg(kwds["usecols"]) + kwds["usecols"] = self.usecols + + # open handles + self._open_handles(src, kwds) + assert self.handles is not None + for key in ("storage_options", "encoding", "memory_map", "compression"): + kwds.pop(key, None) + if self.handles.is_mmap and hasattr(self.handles.handle, "mmap"): + # pandas\io\parsers.py:1861: error: Item "IO[Any]" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "RawIOBase" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "BufferedIOBase" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "TextIOBase" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "TextIOWrapper" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "mmap" of "Union[IO[Any], + # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap]" has + # no attribute "mmap" [union-attr] + self.handles.handle = self.handles.handle.mmap # type: ignore[union-attr] + + try: + self._reader = parsers.TextReader(self.handles.handle, kwds) + except Exception: + self.handles.close() + raise + self.unnamed_cols = self._reader.unnamed_cols + + passed_names = self.names is None + + if self._reader.header is None: + self.names = None + else: + if len(self._reader.header) > 1: + # we have a multi index in the columns + ( + self.names, + self.index_names, + self.col_names, + passed_names, + ) = self._extract_multi_indexer_columns( + self._reader.header, self.index_names, self.col_names, passed_names + ) + else: + self.names = list(self._reader.header[0]) + + if self.names is None: + if self.prefix: + self.names = [ + f"{self.prefix}{i}" for i in range(self._reader.table_width) + ] + else: + self.names = list(range(self._reader.table_width)) + + # gh-9755 + # + # need to set orig_names here first + # so that proper indexing can be done + # with _set_noconvert_columns + # + # once names has been filtered, we will + # then set orig_names again to names + self.orig_names = self.names[:] + + if self.usecols: + usecols = self._evaluate_usecols(self.usecols, self.orig_names) + + # GH 14671 + # assert for mypy, orig_names is List or None, None would error in issubset + assert self.orig_names is not None + if self.usecols_dtype == "string" and not set(usecols).issubset( + self.orig_names + ): + self._validate_usecols_names(usecols, self.orig_names) + + if len(self.names) > len(usecols): + self.names = [ + n + for i, n in enumerate(self.names) + if (i in usecols or n in usecols) + ] + + if len(self.names) < len(usecols): + self._validate_usecols_names(usecols, self.names) + + self._validate_parse_dates_presence(self.names) + self._set_noconvert_columns() + + self.orig_names = self.names + + if not self._has_complex_date_col: + if self._reader.leading_cols == 0 and is_index_col(self.index_col): + + self._name_processed = True + (index_names, self.names, self.index_col) = self._clean_index_names( + self.names, self.index_col, self.unnamed_cols + ) + + if self.index_names is None: + self.index_names = index_names + + if self._reader.header is None and not passed_names: + # pandas\io\parsers.py:1997: error: Argument 1 to "len" has + # incompatible type "Optional[Any]"; expected "Sized" + # [arg-type] + self.index_names = [None] * len( + self.index_names # type: ignore[arg-type] + ) + + self._implicit_index = self._reader.leading_cols > 0 + + def close(self) -> None: + super().close() + + # close additional handles opened by C parser + try: + self._reader.close() + except ValueError: + pass + + def _set_noconvert_columns(self): + """ + Set the columns that should not undergo dtype conversions. + + Currently, any column that is involved with date parsing will not + undergo such conversions. + """ + names = self.orig_names + if self.usecols_dtype == "integer": + # A set of integers will be converted to a list in + # the correct order every single time. + usecols = list(self.usecols) + usecols.sort() + elif callable(self.usecols) or self.usecols_dtype not in ("empty", None): + # The names attribute should have the correct columns + # in the proper order for indexing with parse_dates. + usecols = self.names[:] + else: + # Usecols is empty. + + # pandas\io\parsers.py:2030: error: Incompatible types in + # assignment (expression has type "None", variable has type + # "List[Any]") [assignment] + usecols = None # type: ignore[assignment] + + def _set(x): + if usecols is not None and is_integer(x): + x = usecols[x] + + if not is_integer(x): + # assert for mypy, names is List or None, None would error when calling + # .index() + assert names is not None + x = names.index(x) + + self._reader.set_noconvert(x) + + if isinstance(self.parse_dates, list): + for val in self.parse_dates: + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif isinstance(self.parse_dates, dict): + for val in self.parse_dates.values(): + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif self.parse_dates: + if isinstance(self.index_col, list): + for k in self.index_col: + _set(k) + elif self.index_col is not None: + _set(self.index_col) + + def set_error_bad_lines(self, status): + self._reader.set_error_bad_lines(int(status)) + + def read(self, nrows=None): + try: + data = self._reader.read(nrows) + except StopIteration: + if self._first_chunk: + self._first_chunk = False + names = self._maybe_dedup_names(self.orig_names) + index, columns, col_dict = self._get_empty_meta( + names, + self.index_col, + self.index_names, + dtype=self.kwds.get("dtype"), + ) + columns = self._maybe_make_multi_index_columns(columns, self.col_names) + + if self.usecols is not None: + columns = self._filter_usecols(columns) + + col_dict = {k: v for k, v in col_dict.items() if k in columns} + + return index, columns, col_dict + + else: + self.close() + raise + + # Done with first read, next time raise StopIteration + self._first_chunk = False + + names = self.names + + if self._reader.leading_cols: + if self._has_complex_date_col: + raise NotImplementedError("file structure not yet supported") + + # implicit index, no index names + arrays = [] + + for i in range(self._reader.leading_cols): + if self.index_col is None: + values = data.pop(i) + else: + values = data.pop(self.index_col[i]) + + values = self._maybe_parse_dates(values, i, try_parse_dates=True) + arrays.append(values) + + index = ensure_index_from_sequences(arrays) + + if self.usecols is not None: + names = self._filter_usecols(names) + + names = self._maybe_dedup_names(names) + + # rename dict keys + data = sorted(data.items()) + data = {k: v for k, (i, v) in zip(names, data)} + + names, data = self._do_date_conversions(names, data) + + else: + # rename dict keys + data = sorted(data.items()) + + # ugh, mutation + + # assert for mypy, orig_names is List or None, None would error in list(...) + assert self.orig_names is not None + names = list(self.orig_names) + names = self._maybe_dedup_names(names) + + if self.usecols is not None: + names = self._filter_usecols(names) + + # columns as list + alldata = [x[1] for x in data] + + data = {k: v for k, (i, v) in zip(names, data)} + + names, data = self._do_date_conversions(names, data) + index, names = self._make_index(data, alldata, names) + + # maybe create a mi on the columns + names = self._maybe_make_multi_index_columns(names, self.col_names) + + return index, names, data + + def _filter_usecols(self, names): + # hackish + usecols = self._evaluate_usecols(self.usecols, names) + if usecols is not None and len(names) != len(usecols): + names = [ + name for i, name in enumerate(names) if i in usecols or name in usecols + ] + return names + + def _get_index_names(self): + names = list(self._reader.header[0]) + idx_names = None + + if self._reader.leading_cols == 0 and self.index_col is not None: + (idx_names, names, self.index_col) = self._clean_index_names( + names, self.index_col, self.unnamed_cols + ) + + return names, idx_names + + def _maybe_parse_dates(self, values, index, try_parse_dates=True): + if try_parse_dates and self._should_parse_dates(index): + values = self._date_conv(values) + return values diff --git a/pandas/io/parsers/python_parser.py b/pandas/io/parsers/python_parser.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/python_parser.py @@ -0,0 +1,1263 @@ +from collections import abc, defaultdict +import csv +from io import StringIO +import re +import sys +from typing import Iterator, List, Optional, cast + +import numpy as np + +import pandas._libs.lib as lib +from pandas._typing import FilePathOrBuffer, Union +from pandas.errors import EmptyDataError, ParserError + +from pandas.core.dtypes.common import is_integer + +from pandas.io.parsers.base_parser import ParserBase, parser_defaults + +# BOM character (byte order mark) +# This exists at the beginning of a file to indicate endianness +# of a file (stream). Unfortunately, this marker screws up parsing, +# so we need to remove it if we see it. +_BOM = "\ufeff" + + +class PythonParser(ParserBase): + def __init__(self, f: Union[FilePathOrBuffer, List], *kwds): + """ + Workhorse function for processing nested list into DataFrame + """ + ParserBase.__init__(self, kwds) + + self.data: Optional[Iterator[str]] = None + self.buf: List = [] + self.pos = 0 + self.line_pos = 0 + + self.skiprows = kwds["skiprows"] + + if callable(self.skiprows): + self.skipfunc = self.skiprows + else: + self.skipfunc = lambda x: x in self.skiprows + + self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"]) + self.delimiter = kwds["delimiter"] + + self.quotechar = kwds["quotechar"] + if isinstance(self.quotechar, str): + self.quotechar = str(self.quotechar) + + self.escapechar = kwds["escapechar"] + self.doublequote = kwds["doublequote"] + self.skipinitialspace = kwds["skipinitialspace"] + self.lineterminator = kwds["lineterminator"] + self.quoting = kwds["quoting"] + self.usecols, _ = self._validate_usecols_arg(kwds["usecols"]) + self.skip_blank_lines = kwds["skip_blank_lines"] + + self.warn_bad_lines = kwds["warn_bad_lines"] + self.error_bad_lines = kwds["error_bad_lines"] + + self.names_passed = kwds["names"] or None + + self.has_index_names = False + if "has_index_names" in kwds: + self.has_index_names = kwds["has_index_names"] + + self.verbose = kwds["verbose"] + self.converters = kwds["converters"] + + self.dtype = kwds["dtype"] + self.thousands = kwds["thousands"] + self.decimal = kwds["decimal"] + + self.comment = kwds["comment"] + + # Set self.data to something that can read lines. + if isinstance(f, list): + # read_excel: f is a list + self.data = cast(Iterator[str], f) + else: + self._open_handles(f, kwds) + assert self.handles is not None + assert hasattr(self.handles.handle, "readline") + try: + self._make_reader(self.handles.handle) + except (csv.Error, UnicodeDecodeError): + self.close() + raise + + # Get columns in two steps: infer from data, then + # infer column indices from self.usecols if it is specified. + self._col_indices: Optional[List[int]] = None + try: + ( + self.columns, + self.num_original_columns, + self.unnamed_cols, + ) = self._infer_columns() + except (TypeError, ValueError): + self.close() + raise + + # Now self.columns has the set of columns that we will process. + # The original set is stored in self.original_columns. + if len(self.columns) > 1: + # we are processing a multi index column + ( + self.columns, + self.index_names, + self.col_names, + _, + ) = self._extract_multi_indexer_columns( + self.columns, self.index_names, self.col_names + ) + # Update list of original names to include all indices. + self.num_original_columns = len(self.columns) + else: + self.columns = self.columns[0] + + # get popped off for index + self.orig_names = list(self.columns) + + # needs to be cleaned/refactored + # multiple date column thing turning into a real spaghetti factory + + if not self._has_complex_date_col: + (index_names, self.orig_names, self.columns) = self._get_index_name( + self.columns + ) + self._name_processed = True + if self.index_names is None: + self.index_names = index_names + + if self._col_indices is None: + self._col_indices = list(range(len(self.columns))) + + self._validate_parse_dates_presence(self.columns) + if self.parse_dates: + self._no_thousands_columns = self._set_no_thousands_columns() + else: + self._no_thousands_columns = None + + if len(self.decimal) != 1: + raise ValueError("Only length-1 decimal markers supported") + + decimal = re.escape(self.decimal) + if self.thousands is None: + regex = fr"^[\-\+]?[0-9]({decimal}[0-9])?([0-9]?(E\|e)\-?[0-9]+)?$" + else: + thousands = re.escape(self.thousands) + regex = ( + fr"^[\-\+]?([0-9]+{thousands}\|[0-9])({decimal}[0-9])?" + fr"([0-9]?(E\|e)\-?[0-9]+)?$" + ) + self.num = re.compile(regex) + + def _set_no_thousands_columns(self): + # Create a set of column ids that are not to be stripped of thousands + # operators. + noconvert_columns = set() + + def _set(x): + if is_integer(x): + noconvert_columns.add(x) + else: + assert self._col_indices is not None + col_indices = self._col_indices + noconvert_columns.add(col_indices[self.columns.index(x)]) + + if isinstance(self.parse_dates, list): + for val in self.parse_dates: + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif isinstance(self.parse_dates, dict): + for val in self.parse_dates.values(): + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif self.parse_dates: + if isinstance(self.index_col, list): + for k in self.index_col: + _set(k) + elif self.index_col is not None: + _set(self.index_col) + + return noconvert_columns + + def _make_reader(self, f): + sep = self.delimiter + + if sep is None or len(sep) == 1: + if self.lineterminator: + raise ValueError( + "Custom line terminators not supported in python parser (yet)" + ) + + class MyDialect(csv.Dialect): + delimiter = self.delimiter + quotechar = self.quotechar + escapechar = self.escapechar + doublequote = self.doublequote + skipinitialspace = self.skipinitialspace + quoting = self.quoting + lineterminator = "\n" + + dia = MyDialect + + if sep is not None: + dia.delimiter = sep + else: + # attempt to sniff the delimiter from the first valid line, + # i.e. no comment line and not in skiprows + line = f.readline() + lines = self._check_comments([[line]])[0] + while self.skipfunc(self.pos) or not lines: + self.pos += 1 + line = f.readline() + lines = self._check_comments([[line]])[0] + + # since `line` was a string, lines will be a list containing + # only a single string + line = lines[0] + + self.pos += 1 + self.line_pos += 1 + sniffed = csv.Sniffer().sniff(line) + dia.delimiter = sniffed.delimiter + + # Note: encoding is irrelevant here + line_rdr = csv.reader(StringIO(line), dialect=dia) + self.buf.extend(list(line_rdr)) + + # Note: encoding is irrelevant here + reader = csv.reader(f, dialect=dia, strict=True) + + else: + + def _read(): + line = f.readline() + pat = re.compile(sep) + + yield pat.split(line.strip()) + + for line in f: + yield pat.split(line.strip()) + + reader = _read() + + # pandas\io\parsers.py:2427: error: Incompatible types in assignment + # (expression has type "_reader", variable has type "Union[IO[Any], + # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap, None]") + # [assignment] + self.data = reader # type: ignore[assignment] + + def read(self, rows=None): + try: + content = self._get_lines(rows) + except StopIteration: + if self._first_chunk: + content = [] + else: + self.close() + raise + + # done with first read, next time raise StopIteration + self._first_chunk = False + + # pandas\io\parsers.py:2480: error: Argument 1 to "list" has + # incompatible type "Optional[Any]"; expected "Iterable[Any]" + # [arg-type] + columns = list(self.orig_names) # type: ignore[arg-type] + if not len(content): # pragma: no cover + # DataFrame with the right metadata, even though it's length 0 + names = self._maybe_dedup_names(self.orig_names) + index, columns, col_dict = self._get_empty_meta( + names, self.index_col, self.index_names, self.dtype + ) + columns = self._maybe_make_multi_index_columns(columns, self.col_names) + return index, columns, col_dict + + # handle new style for names in index + count_empty_content_vals = count_empty_vals(content[0]) + indexnamerow = None + if self.has_index_names and count_empty_content_vals == len(columns): + indexnamerow = content[0] + content = content[1:] + + alldata = self._rows_to_cols(content) + data, columns = self._exclude_implicit_index(alldata) + + columns, data = self._do_date_conversions(columns, data) + + data = self._convert_data(data) + index, columns = self._make_index(data, alldata, columns, indexnamerow) + + return index, columns, data + + def _exclude_implicit_index(self, alldata): + names = self._maybe_dedup_names(self.orig_names) + + offset = 0 + if self._implicit_index: + offset = len(self.index_col) + + if self._col_indices is not None and len(names) != len(self._col_indices): + names = [names[i] for i in sorted(self._col_indices)] + + return {name: alldata[i + offset] for i, name in enumerate(names)}, names + + # legacy + def get_chunk(self, size=None): + if size is None: + # pandas\io\parsers.py:2528: error: "PythonParser" has no attribute + # "chunksize" [attr-defined] + size = self.chunksize # type: ignore[attr-defined] + return self.read(rows=size) + + def _convert_data(self, data): + # apply converters + def _clean_mapping(mapping): + """converts col numbers to names""" + clean = {} + for col, v in mapping.items(): + # pandas\io\parsers.py:2537: error: Unsupported right operand + # type for in ("Optional[Any]") [operator] + if ( + isinstance(col, int) + and col not in self.orig_names # type: ignore[operator] + ): + # pandas\io\parsers.py:2538: error: Value of type + # "Optional[Any]" is not indexable [index] + col = self.orig_names[col] # type: ignore[index] + clean[col] = v + return clean + + clean_conv = _clean_mapping(self.converters) + if not isinstance(self.dtype, dict): + # handles single dtype applied to all columns + clean_dtypes = self.dtype + else: + clean_dtypes = _clean_mapping(self.dtype) + + # Apply NA values. + clean_na_values = {} + clean_na_fvalues = {} + + if isinstance(self.na_values, dict): + for col in self.na_values: + na_value = self.na_values[col] + na_fvalue = self.na_fvalues[col] + + # pandas\io\parsers.py:2558: error: Unsupported right operand + # type for in ("Optional[Any]") [operator] + if ( + isinstance(col, int) + and col not in self.orig_names # type: ignore[operator] + ): + # pandas\io\parsers.py:2559: error: Value of type + # "Optional[Any]" is not indexable [index] + col = self.orig_names[col] # type: ignore[index] + + clean_na_values[col] = na_value + clean_na_fvalues[col] = na_fvalue + else: + clean_na_values = self.na_values + clean_na_fvalues = self.na_fvalues + + return self._convert_to_ndarrays( + data, + clean_na_values, + clean_na_fvalues, + self.verbose, + clean_conv, + clean_dtypes, + ) + + def _infer_columns(self): + names = self.names + num_original_columns = 0 + clear_buffer = True + # pandas\io\parsers.py:2580: error: Need type annotation for + # 'unnamed_cols' (hint: "unnamed_cols: Set[<type>] = ...") + # [var-annotated] + unnamed_cols = set() # type: ignore[var-annotated] + + if self.header is not None: + header = self.header + + if isinstance(header, (list, tuple, np.ndarray)): + have_mi_columns = len(header) > 1 + # we have a mi columns, so read an extra line + if have_mi_columns: + header = list(header) + [header[-1] + 1] + else: + have_mi_columns = False + header = [header] + + # pandas\io\parsers.py:2594: error: Need type annotation for + # 'columns' (hint: "columns: List[<type>] = ...") [var-annotated] + columns = [] # type: ignore[var-annotated] + for level, hr in enumerate(header): + try: + line = self._buffered_line() + + while self.line_pos <= hr: + line = self._next_line() + + except StopIteration as err: + if self.line_pos < hr: + raise ValueError( + f"Passed header={hr} but only {self.line_pos + 1} lines in " + "file" + ) from err + + # We have an empty file, so check + # if columns are provided. That will + # serve as the 'line' for parsing + if have_mi_columns and hr > 0: + if clear_buffer: + self._clear_buffer() + columns.append([None] len(columns[-1])) + return columns, num_original_columns, unnamed_cols + + if not self.names: + raise EmptyDataError("No columns to parse from file") from err + + line = self.names[:] + + this_columns = [] + this_unnamed_cols = [] + + for i, c in enumerate(line): + if c == "": + if have_mi_columns: + col_name = f"Unnamed: {i}_level_{level}" + else: + col_name = f"Unnamed: {i}" + + this_unnamed_cols.append(i) + this_columns.append(col_name) + else: + this_columns.append(c) + + if not have_mi_columns and self.mangle_dupe_cols: + # pandas\io\parsers.py:2639: error: Need type annotation + # for 'counts' [var-annotated] + counts = defaultdict(int) # type: ignore[var-annotated] + + for i, col in enumerate(this_columns): + cur_count = counts[col] + + while cur_count > 0: + counts[col] = cur_count + 1 + col = f"{col}.{cur_count}" + cur_count = counts[col] + + this_columns[i] = col + counts[col] = cur_count + 1 + elif have_mi_columns: + + # if we have grabbed an extra line, but its not in our + # format so save in the buffer, and create an blank extra + # line for the rest of the parsing code + if hr == header[-1]: + lc = len(this_columns) + ic = len(self.index_col) if self.index_col is not None else 0 + unnamed_count = len(this_unnamed_cols) + + if lc != unnamed_count and lc - ic > unnamed_count: + clear_buffer = False + # pandas\io\parsers.py:2663: error: List item 0 has + # incompatible type "None"; expected "str" + # [list-item] + this_columns = [None] * lc # type: ignore[list-item] + self.buf = [self.buf[-1]] + + # pandas\io\parsers.py:2666: error: Argument 1 to "append" of + # "list" has incompatible type "List[str]"; expected + # "List[None]" [arg-type] + columns.append(this_columns) # type: ignore[arg-type] + unnamed_cols.update({this_columns[i] for i in this_unnamed_cols}) + + if len(columns) == 1: + num_original_columns = len(this_columns) + + if clear_buffer: + self._clear_buffer() + + if names is not None: + if len(names) > len(columns[0]): + raise ValueError( + "Number of passed names did not match " + "number of header fields in the file" + ) + if len(columns) > 1: + raise TypeError("Cannot pass names with multi-index columns") + + if self.usecols is not None: + # Set _use_cols. We don't store columns because they are + # overwritten. + self._handle_usecols(columns, names) + else: + num_original_columns = len(names) + columns = [names] + else: + columns = self._handle_usecols(columns, columns[0]) + else: + try: + line = self._buffered_line() + + except StopIteration as err: + if not names: + raise EmptyDataError("No columns to parse from file") from err + + line = names[:] + + ncols = len(line) + num_original_columns = ncols + + if not names: + if self.prefix: + # pandas\io\parsers.py:2711: error: List comprehension has + # incompatible type List[str]; expected List[None] [misc] + columns = [ + [ + f"{self.prefix}{i}" # type: ignore[misc] + for i in range(ncols) + ] + ] + else: + # pandas\io\parsers.py:2713: error: Argument 1 to "list" + # has incompatible type "range"; expected "Iterable[None]" + # [arg-type] + columns = [list(range(ncols))] # type: ignore[arg-type] + columns = self._handle_usecols(columns, columns[0]) + else: + if self.usecols is None or len(names) >= num_original_columns: + columns = self._handle_usecols([names], names) + num_original_columns = len(names) + else: + if not callable(self.usecols) and len(names) != len(self.usecols): + raise ValueError( + "Number of passed names did not match number of " + "header fields in the file" + ) + # Ignore output but set used columns. + self._handle_usecols([names], names) + columns = [names] + num_original_columns = ncols + + return columns, num_original_columns, unnamed_cols + + def _handle_usecols(self, columns, usecols_key): + """ + Sets self._col_indices + + usecols_key is used if there are string usecols. + """ + if self.usecols is not None: + if callable(self.usecols): + col_indices = self._evaluate_usecols(self.usecols, usecols_key) + elif any(isinstance(u, str) for u in self.usecols): + if len(columns) > 1: + raise ValueError( + "If using multiple headers, usecols must be integers." + ) + col_indices = [] + + for col in self.usecols: + if isinstance(col, str): + try: + col_indices.append(usecols_key.index(col)) + except ValueError: + self._validate_usecols_names(self.usecols, usecols_key) + else: + col_indices.append(col) + else: + col_indices = self.usecols + + columns = [ + [n for i, n in enumerate(column) if i in col_indices] + for column in columns + ] + self._col_indices = sorted(col_indices) + return columns + + def _buffered_line(self): + """ + Return a line from buffer, filling buffer if required. + """ + if len(self.buf) > 0: + return self.buf[0] + else: + return self._next_line() + + def _check_for_bom(self, first_row): + """ + Checks whether the file begins with the BOM character. + If it does, remove it. In addition, if there is quoting + in the field subsequent to the BOM, remove it as well + because it technically takes place at the beginning of + the name, not the middle of it. + """ + # first_row will be a list, so we need to check + # that that list is not empty before proceeding. + if not first_row: + return first_row + + # The first element of this row is the one that could have the + # BOM that we want to remove. Check that the first element is a + # string before proceeding. + if not isinstance(first_row[0], str): + return first_row + + # Check that the string is not empty, as that would + # obviously not have a BOM at the start of it. + if not first_row[0]: + return first_row + + # Since the string is non-empty, check that it does + # in fact begin with a BOM. + first_elt = first_row[0][0] + if first_elt != _BOM: + return first_row + + first_row_bom = first_row[0] + + if len(first_row_bom) > 1 and first_row_bom[1] == self.quotechar: + start = 2 + quote = first_row_bom[1] + end = first_row_bom[2:].index(quote) + 2 + + # Extract the data between the quotation marks + new_row = first_row_bom[start:end] + + # Extract any remaining data after the second + # quotation mark. + if len(first_row_bom) > end + 1: + new_row += first_row_bom[end + 1 :] + + else: + + # No quotation so just remove BOM from first element + new_row = first_row_bom[1:] + return [new_row] + first_row[1:] + + def _is_line_empty(self, line): + """ + Check if a line is empty or not. + + Parameters + ---------- + line : str, array-like + The line of data to check. + + Returns + ------- + boolean : Whether or not the line is empty. + """ + return not line or all(not x for x in line) + + def _next_line(self): + if isinstance(self.data, list): + while self.skipfunc(self.pos): + self.pos += 1 + + while True: + try: + line = self._check_comments([self.data[self.pos]])[0] + self.pos += 1 + # either uncommented or blank to begin with + if not self.skip_blank_lines and ( + self._is_line_empty(self.data[self.pos - 1]) or line + ): + break + elif self.skip_blank_lines: + ret = self._remove_empty_lines([line]) + if ret: + line = ret[0] + break + except IndexError: + raise StopIteration + else: + while self.skipfunc(self.pos): + self.pos += 1 + # assert for mypy, data is Iterator[str] or None, would error in next + assert self.data is not None + next(self.data) + + while True: + orig_line = self._next_iter_line(row_num=self.pos + 1) + self.pos += 1 + + if orig_line is not None: + line = self._check_comments([orig_line])[0] + + if self.skip_blank_lines: + ret = self._remove_empty_lines([line]) + + if ret: + line = ret[0] + break + elif self._is_line_empty(orig_line) or line: + break + + # This was the first line of the file, + # which could contain the BOM at the + # beginning of it. + if self.pos == 1: + line = self._check_for_bom(line) + + self.line_pos += 1 + self.buf.append(line) + return line + + def _alert_malformed(self, msg, row_num): + """ + Alert a user about a malformed row. + + If `self.error_bad_lines` is True, the alert will be `ParserError`. + If `self.warn_bad_lines` is True, the alert will be printed out. + + Parameters + ---------- + msg : The error message to display. + row_num : The row number where the parsing error occurred. + Because this row number is displayed, we 1-index, + even though we 0-index internally. + """ + if self.error_bad_lines: + raise ParserError(msg) + elif self.warn_bad_lines: + base = f"Skipping line {row_num}: " + sys.stderr.write(base + msg + "\n") + + def _next_iter_line(self, row_num): + """ + Wrapper around iterating through `self.data` (CSV source). + + When a CSV error is raised, we check for specific + error messages that allow us to customize the + error message displayed to the user. + + Parameters + ---------- + row_num : The row number of the line being parsed. + """ + try: + # assert for mypy, data is Iterator[str] or None, would error in next + assert self.data is not None + return next(self.data) + except csv.Error as e: + if self.warn_bad_lines or self.error_bad_lines: + msg = str(e) + + if "NULL byte" in msg or "line contains NUL" in msg: + msg = ( + "NULL byte detected. This byte " + "cannot be processed in Python's " + "native csv library at the moment, " + "so please pass in engine='c' instead" + ) + + if self.skipfooter > 0: + reason = ( + "Error could possibly be due to " + "parsing errors in the skipped footer rows " + "(the skipfooter keyword is only applied " + "after Python's csv library has parsed " + "all rows)." + ) + msg += ". " + reason + + self._alert_malformed(msg, row_num) + return None + + def _check_comments(self, lines): + if self.comment is None: + return lines + ret = [] + for line in lines: + rl = [] + for x in line: + if ( + not isinstance(x, str) + or self.comment not in x + or x in self.na_values + ): + rl.append(x) + else: + x = x[: x.find(self.comment)] + if len(x) > 0: + rl.append(x) + break + ret.append(rl) + return ret + + def _remove_empty_lines(self, lines): + """ + Iterate through the lines and remove any that are + either empty or contain only one whitespace value + + Parameters + ---------- + lines : array-like + The array of lines that we are to filter. + + Returns + ------- + filtered_lines : array-like + The same array of lines with the "empty" ones removed. + """ + ret = [] + for line in lines: + # Remove empty lines and lines with only one whitespace value + if ( + len(line) > 1 + or len(line) == 1 + and (not isinstance(line[0], str) or line[0].strip()) + ): + ret.append(line) + return ret + + def _check_thousands(self, lines): + if self.thousands is None: + return lines + + return self._search_replace_num_columns( + lines=lines, search=self.thousands, replace="" + ) + + def _search_replace_num_columns(self, lines, search, replace): + ret = [] + for line in lines: + rl = [] + for i, x in enumerate(line): + if ( + not isinstance(x, str) + or search not in x + or (self._no_thousands_columns and i in self._no_thousands_columns) + or not self.num.search(x.strip()) + ): + rl.append(x) + else: + rl.append(x.replace(search, replace)) + ret.append(rl) + return ret + + def _check_decimal(self, lines): + if self.decimal == parser_defaults["decimal"]: + return lines + + return self._search_replace_num_columns( + lines=lines, search=self.decimal, replace="." + ) + + def _clear_buffer(self): + self.buf = [] + + _implicit_index = False + + def _get_index_name(self, columns): + """ + Try several cases to get lines: + + 0) There are headers on row 0 and row 1 and their + total summed lengths equals the length of the next line. + Treat row 0 as columns and row 1 as indices + 1) Look for implicit index: there are more columns + on row 1 than row 0. If this is true, assume that row + 1 lists index columns and row 0 lists normal columns. + 2) Get index from the columns if it was listed. + """ + orig_names = list(columns) + columns = list(columns) + + try: + line = self._next_line() + except StopIteration: + line = None + + try: + next_line = self._next_line() + except StopIteration: + next_line = None + + # implicitly index_col=0 b/c 1 fewer column names + implicit_first_cols = 0 + if line is not None: + # leave it 0, #2442 + # Case 1 + if self.index_col is not False: + implicit_first_cols = len(line) - self.num_original_columns + + # Case 0 + if next_line is not None: + if len(next_line) == len(line) + self.num_original_columns: + # column and index names on diff rows + self.index_col = list(range(len(line))) + self.buf = self.buf[1:] + + for c in reversed(line): + columns.insert(0, c) + + # Update list of original names to include all indices. + orig_names = list(columns) + self.num_original_columns = len(columns) + return line, orig_names, columns + + if implicit_first_cols > 0: + # Case 1 + self._implicit_index = True + if self.index_col is None: + self.index_col = list(range(implicit_first_cols)) + + index_name = None + + else: + # Case 2 + (index_name, columns_, self.index_col) = self._clean_index_names( + columns, self.index_col, self.unnamed_cols + ) + + return index_name, orig_names, columns + + def _rows_to_cols(self, content): + col_len = self.num_original_columns + + if self._implicit_index: + col_len += len(self.index_col) + + max_len = max(len(row) for row in content) + + # Check that there are no rows with too many + # elements in their row (rows with too few + # elements are padded with NaN). + if max_len > col_len and self.index_col is not False and self.usecols is None: + + footers = self.skipfooter if self.skipfooter else 0 + bad_lines = [] + + iter_content = enumerate(content) + content_len = len(content) + content = [] + + for (i, l) in iter_content: + actual_len = len(l) + + if actual_len > col_len: + if self.error_bad_lines or self.warn_bad_lines: + row_num = self.pos - (content_len - i + footers) + bad_lines.append((row_num, actual_len)) + + if self.error_bad_lines: + break + else: + content.append(l) + + for row_num, actual_len in bad_lines: + msg = ( + f"Expected {col_len} fields in line {row_num + 1}, saw " + f"{actual_len}" + ) + if ( + self.delimiter + and len(self.delimiter) > 1 + and self.quoting != csv.QUOTE_NONE + ): + # see gh-13374 + reason = ( + "Error could possibly be due to quotes being " + "ignored when a multi-char delimiter is used." + ) + msg += ". " + reason + + self._alert_malformed(msg, row_num + 1) + + # see gh-13320 + zipped_content = list(lib.to_object_array(content, min_width=col_len).T) + + if self.usecols: + assert self._col_indices is not None + col_indices = self._col_indices + + if self._implicit_index: + zipped_content = [ + a + for i, a in enumerate(zipped_content) + if ( + i < len(self.index_col) + or i - len(self.index_col) in col_indices + ) + ] + else: + zipped_content = [ + a for i, a in enumerate(zipped_content) if i in col_indices + ] + return zipped_content + + def _get_lines(self, rows=None): + lines = self.buf + new_rows = None + + # already fetched some number + if rows is not None: + # we already have the lines in the buffer + if len(self.buf) >= rows: + new_rows, self.buf = self.buf[:rows], self.buf[rows:] + + # need some lines + else: + rows -= len(self.buf) + + if new_rows is None: + if isinstance(self.data, list): + if self.pos > len(self.data): + raise StopIteration + if rows is None: + new_rows = self.data[self.pos :] + new_pos = len(self.data) + else: + new_rows = self.data[self.pos : self.pos + rows] + new_pos = self.pos + rows + + # Check for stop rows. n.b.: self.skiprows is a set. + if self.skiprows: + new_rows = [ + row + for i, row in enumerate(new_rows) + if not self.skipfunc(i + self.pos) + ] + + lines.extend(new_rows) + self.pos = new_pos + + else: + new_rows = [] + try: + if rows is not None: + for _ in range(rows): + # assert for mypy, data is Iterator[str] or None, would + # error in next + assert self.data is not None + new_rows.append(next(self.data)) + lines.extend(new_rows) + else: + rows = 0 + + while True: + new_row = self._next_iter_line(row_num=self.pos + rows + 1) + rows += 1 + + if new_row is not None: + new_rows.append(new_row) + + except StopIteration: + if self.skiprows: + new_rows = [ + row + for i, row in enumerate(new_rows) + if not self.skipfunc(i + self.pos) + ] + lines.extend(new_rows) + if len(lines) == 0: + raise + self.pos += len(new_rows) + + self.buf = [] + else: + lines = new_rows + + if self.skipfooter: + lines = lines[: -self.skipfooter] + + lines = self._check_comments(lines) + if self.skip_blank_lines: + lines = self._remove_empty_lines(lines) + lines = self._check_thousands(lines) + return self._check_decimal(lines) + + +class FixedWidthReader(abc.Iterator): + """ + A reader of fixed-width lines. + """ + + def __init__(self, f, colspecs, delimiter, comment, skiprows=None, infer_nrows=100): + self.f = f + self.buffer = None + self.delimiter = "\r\n" + delimiter if delimiter else "\n\r\t " + self.comment = comment + if colspecs == "infer": + self.colspecs = self.detect_colspecs( + infer_nrows=infer_nrows, skiprows=skiprows + ) + else: + self.colspecs = colspecs + + if not isinstance(self.colspecs, (tuple, list)): + raise TypeError( + "column specifications must be a list or tuple, " + f"input was a {type(colspecs).__name__}" + ) + + for colspec in self.colspecs: + if not ( + isinstance(colspec, (tuple, list)) + and len(colspec) == 2 + and isinstance(colspec[0], (int, np.integer, type(None))) + and isinstance(colspec[1], (int, np.integer, type(None))) + ): + raise TypeError( + "Each column specification must be " + "2 element tuple or list of integers" + ) + + def get_rows(self, infer_nrows, skiprows=None): + """ + Read rows from self.f, skipping as specified. + + We distinguish buffer_rows (the first <= infer_nrows + lines) from the rows returned to detect_colspecs + because it's simpler to leave the other locations + with skiprows logic alone than to modify them to + deal with the fact we skipped some rows here as + well. + + Parameters + ---------- + infer_nrows : int + Number of rows to read from self.f, not counting + rows that are skipped. + skiprows: set, optional + Indices of rows to skip. + + Returns + ------- + detect_rows : list of str + A list containing the rows to read. + + """ + if skiprows is None: + skiprows = set() + buffer_rows = [] + detect_rows = [] + for i, row in enumerate(self.f): + if i not in skiprows: + detect_rows.append(row) + buffer_rows.append(row) + if len(detect_rows) >= infer_nrows: + break + self.buffer = iter(buffer_rows) + return detect_rows + + def detect_colspecs(self, infer_nrows=100, skiprows=None): + # Regex escape the delimiters + delimiters = "".join(fr"\{x}" for x in self.delimiter) + pattern = re.compile(f"([^{delimiters}]+)") + rows = self.get_rows(infer_nrows, skiprows) + if not rows: + raise EmptyDataError("No rows from which to infer column width") + max_len = max(map(len, rows)) + mask = np.zeros(max_len + 1, dtype=int) + if self.comment is not None: + rows = [row.partition(self.comment)[0] for row in rows] + for row in rows: + for m in pattern.finditer(row): + mask[m.start() : m.end()] = 1 + shifted = np.roll(mask, 1) + shifted[0] = 0 + edges = np.where((mask ^ shifted) == 1)[0] + edge_pairs = list(zip(edges[::2], edges[1::2])) + return edge_pairs + + def __next__(self): + if self.buffer is not None: + try: + line = next(self.buffer) + except StopIteration: + self.buffer = None + line = next(self.f) + else: + line = next(self.f) + # Note: 'colspecs' is a sequence of half-open intervals. + return [line[fromm:to].strip(self.delimiter) for (fromm, to) in self.colspecs] + + +class FixedWidthFieldParser(PythonParser): + """ + Specialization that Converts fixed-width fields into DataFrames. + See PythonParser for details. + """ + + def __init__(self, f, kwds): + # Support iterators, convert to a list. + self.colspecs = kwds.pop("colspecs") + self.infer_nrows = kwds.pop("infer_nrows") + PythonParser.__init__(self, f, kwds) + + def _make_reader(self, f): + self.data = FixedWidthReader( + f, + self.colspecs, + self.delimiter, + self.comment, + self.skiprows, + self.infer_nrows, + ) + + def _remove_empty_lines(self, lines) -> List: + """ + Returns the list of lines without the empty ones. With fixed-width + fields, empty lines become arrays of empty strings. + + See PythonParser._remove_empty_lines. + """ + return [ + line + for line in lines + if any(not isinstance(e, str) or e.strip() for e in line) + ] + + +def count_empty_vals(vals) -> int: + return sum(1 for v in vals if v == "" or v is None) + + +def _validate_skipfooter_arg(skipfooter): + """ + Validate the 'skipfooter' parameter. + + Checks whether 'skipfooter' is a non-negative integer. + Raises a ValueError if that is not the case. + + Parameters + ---------- + skipfooter : non-negative integer + The number of rows to skip at the end of the file. + + Returns + ------- + validated_skipfooter : non-negative integer + The original input if the validation succeeds. + + Raises + ------ + ValueError : 'skipfooter' was not a non-negative integer. + """ + if not is_integer(skipfooter): + raise ValueError("skipfooter must be an integer") + + if skipfooter < 0: + raise ValueError("skipfooter cannot be negative") + + return skipfooter diff --git a/pandas/io/parsers/readers.py b/pandas/io/parsers/readers.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/readers.py @@ -0,0 +1,1339 @@ +""" +Module contains tools for processing files into DataFrames or other objects +""" + +from collections import abc +import csv +import sys +from textwrap import fill +from typing import Any, Dict, Optional, Set, Type +import warnings + +import numpy as np + +import pandas._libs.lib as lib +from pandas._libs.parsers import STR_NA_VALUES +from pandas._typing import DtypeArg, FilePathOrBuffer, StorageOptions, Union +from pandas.errors import AbstractMethodError, ParserWarning +from pandas.util._decorators import Appender + +from pandas.core.dtypes.common import is_file_like, is_float, is_integer, is_list_like + +from pandas.core import generic +from pandas.core.frame import DataFrame +from pandas.core.indexes.api import RangeIndex + +from pandas.io.common import validate_header_arg +from pandas.io.parsers.base_parser import ParserBase, is_index_col, parser_defaults +from pandas.io.parsers.c_parser_wrapper import CParserWrapper +from pandas.io.parsers.python_parser import FixedWidthFieldParser, PythonParser + +_doc_read_csv_and_table = ( + r""" +{summary} + +Also supports optionally iterating or breaking of the file +into chunks. + +Additional help can be found in the online docs for +`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. + +Parameters +---------- +filepath_or_buffer : str, path object or file-like object + Any valid string path is acceptable. The string could be a URL. Valid + URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is + expected. A local file could be: file://localhost/path/to/table.csv. + + If you want to pass in a path object, pandas accepts any ``os.PathLike``. + + By file-like object, we refer to objects with a ``read()`` method, such as + a file handle (e.g. via builtin ``open`` function) or ``StringIO``. +sep : str, default {_default_sep} + Delimiter to use. If sep is None, the C engine cannot automatically detect + the separator, but the Python parsing engine can, meaning the latter will + be used and automatically detect the separator by Python's builtin sniffer + tool, ``csv.Sniffer``. In addition, separators longer than 1 character and + different from ``'\s+'`` will be interpreted as regular expressions and + will also force the use of the Python parsing engine. Note that regex + delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``. +delimiter : str, default ``None`` + Alias for sep. +header : int, list of int, default 'infer' + Row number(s) to use as the column names, and the start of the + data. Default behavior is to infer the column names: if no names + are passed the behavior is identical to ``header=0`` and column + names are inferred from the first line of the file, if column + names are passed explicitly then the behavior is identical to + ``header=None``. Explicitly pass ``header=0`` to be able to + replace existing names. The header can be a list of integers that + specify row locations for a multi-index on the columns + e.g. [0,1,3]. Intervening rows that are not specified will be + skipped (e.g. 2 in this example is skipped). Note that this + parameter ignores commented lines and empty lines if + ``skip_blank_lines=True``, so ``header=0`` denotes the first line of + data rather than the first line of the file. +names : array-like, optional + List of column names to use. If the file contains a header row, + then you should explicitly pass ``header=0`` to override the column names. + Duplicates in this list are not allowed. +index_col : int, str, sequence of int / str, or False, default ``None`` + Column(s) to use as the row labels of the ``DataFrame``, either given as + string name or column index. If a sequence of int / str is given, a + MultiIndex is used. + + Note: ``index_col=False`` can be used to force pandas to not use the first + column as the index, e.g. when you have a malformed file with delimiters at + the end of each line. +usecols : list-like or callable, optional + Return a subset of the columns. If list-like, all elements must either + be positional (i.e. integer indices into the document columns) or strings + that correspond to column names provided either by the user in `names` or + inferred from the document header row(s). For example, a valid list-like + `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``. + Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``. + To instantiate a DataFrame from ``data`` with element order preserved use + ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns + in ``['foo', 'bar']`` order or + ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]`` + for ``['bar', 'foo']`` order. + + If callable, the callable function will be evaluated against the column + names, returning names where the callable function evaluates to True. An + example of a valid callable argument would be ``lambda x: x.upper() in + ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster + parsing time and lower memory usage. +squeeze : bool, default False + If the parsed data only contains one column then return a Series. +prefix : str, optional + Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... +mangle_dupe_cols : bool, default True + Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than + 'X'...'X'. Passing in False will cause data to be overwritten if there + are duplicate names in the columns. +dtype : Type name or dict of column -> type, optional + Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32, + 'c': 'Int64'}} + Use `str` or `object` together with suitable `na_values` settings + to preserve and not interpret dtype. + If converters are specified, they will be applied INSTEAD + of dtype conversion. +engine : {{'c', 'python'}}, optional + Parser engine to use. The C engine is faster while the python engine is + currently more feature-complete. +converters : dict, optional + Dict of functions for converting values in certain columns. Keys can either + be integers or column labels. +true_values : list, optional + Values to consider as True. +false_values : list, optional + Values to consider as False. +skipinitialspace : bool, default False + Skip spaces after delimiter. +skiprows : list-like, int or callable, optional + Line numbers to skip (0-indexed) or number of lines to skip (int) + at the start of the file. + + If callable, the callable function will be evaluated against the row + indices, returning True if the row should be skipped and False otherwise. + An example of a valid callable argument would be ``lambda x: x in [0, 2]``. +skipfooter : int, default 0 + Number of lines at bottom of file to skip (Unsupported with engine='c'). +nrows : int, optional + Number of rows of file to read. Useful for reading pieces of large files. +na_values : scalar, str, list-like, or dict, optional + Additional strings to recognize as NA/NaN. If dict passed, specific + per-column NA values. By default the following values are interpreted as + NaN: '""" + + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ") + + """'. +keep_default_na : bool, default True + Whether or not to include the default NaN values when parsing the data. + Depending on whether `na_values` is passed in, the behavior is as follows: + + * If `keep_default_na` is True, and `na_values` are specified, `na_values` + is appended to the default NaN values used for parsing. + * If `keep_default_na` is True, and `na_values` are not specified, only + the default NaN values are used for parsing. + * If `keep_default_na` is False, and `na_values` are specified, only + the NaN values specified `na_values` are used for parsing. + * If `keep_default_na` is False, and `na_values` are not specified, no + strings will be parsed as NaN. + + Note that if `na_filter` is passed in as False, the `keep_default_na` and + `na_values` parameters will be ignored. +na_filter : bool, default True + Detect missing value markers (empty strings and the value of na_values). In + data without any NAs, passing na_filter=False can improve the performance + of reading a large file. +verbose : bool, default False + Indicate number of NA values placed in non-numeric columns. +skip_blank_lines : bool, default True + If True, skip over blank lines rather than interpreting as NaN values. +parse_dates : bool or list of int or names or list of lists or dict, \ +default False + The behavior is as follows: + + * boolean. If True -> try parsing the index. + * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 + each as a separate date column. + * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as + a single date column. + * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call + result 'foo' + + If a column or index cannot be represented as an array of datetimes, + say because of an unparsable value or a mixture of timezones, the column + or index will be returned unaltered as an object data type. For + non-standard datetime parsing, use ``pd.to_datetime`` after + ``pd.read_csv``. To parse an index or column with a mixture of timezones, + specify ``date_parser`` to be a partially-applied + :func:`pandas.to_datetime` with ``utc=True``. See + :ref:`io.csv.mixed_timezones` for more. + + Note: A fast-path exists for iso8601-formatted dates. +infer_datetime_format : bool, default False + If True and `parse_dates` is enabled, pandas will attempt to infer the + format of the datetime strings in the columns, and if it can be inferred, + switch to a faster method of parsing them. In some cases this can increase + the parsing speed by 5-10x. +keep_date_col : bool, default False + If True and `parse_dates` specifies combining multiple columns then + keep the original columns. +date_parser : function, optional + Function to use for converting a sequence of string columns to an array of + datetime instances. The default uses ``dateutil.parser.parser`` to do the + conversion. Pandas will try to call `date_parser` in three different ways, + advancing to the next if an exception occurs: 1) Pass one or more arrays + (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the + string values from the columns defined by `parse_dates` into a single array + and pass that; and 3) call `date_parser` once for each row using one or + more strings (corresponding to the columns defined by `parse_dates`) as + arguments. +dayfirst : bool, default False + DD/MM format dates, international and European format. +cache_dates : bool, default True + If True, use a cache of unique, converted dates to apply the datetime + conversion. May produce significant speed-up when parsing duplicate + date strings, especially ones with timezone offsets. + + .. versionadded:: 0.25.0 +iterator : bool, default False + Return TextFileReader object for iteration or getting chunks with + ``get_chunk()``. + + .. versionchanged:: 1.2 + + ``TextFileReader`` is a context manager. +chunksize : int, optional + Return TextFileReader object for iteration. + See the `IO Tools docs + <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ + for more information on ``iterator`` and ``chunksize``. + + .. versionchanged:: 1.2 + + ``TextFileReader`` is a context manager. +compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' + For on-the-fly decompression of on-disk data. If 'infer' and + `filepath_or_buffer` is path-like, then detect compression from the + following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no + decompression). If using 'zip', the ZIP file must contain only one data + file to be read in. Set to None for no decompression. +thousands : str, optional + Thousands separator. +decimal : str, default '.' + Character to recognize as decimal point (e.g. use ',' for European data). +lineterminator : str (length 1), optional + Character to break file into lines. Only valid with C parser. +quotechar : str (length 1), optional + The character used to denote the start and end of a quoted item. Quoted + items can include the delimiter and it will be ignored. +quoting : int or csv.QUOTE_* instance, default 0 + Control field quoting behavior per ``csv.QUOTE_`` constants. Use one of + QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). +doublequote : bool, default ``True`` + When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate + whether or not to interpret two consecutive quotechar elements INSIDE a + field as a single ``quotechar`` element. +escapechar : str (length 1), optional + One-character string used to escape other characters. +comment : str, optional + Indicates remainder of line should not be parsed. If found at the beginning + of a line, the line will be ignored altogether. This parameter must be a + single character. Like empty lines (as long as ``skip_blank_lines=True``), + fully commented lines are ignored by the parameter `header` but not by + `skiprows`. For example, if ``comment='#'``, parsing + ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being + treated as the header. +encoding : str, optional + Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python + standard encodings + <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ . +dialect : str or csv.Dialect, optional + If provided, this parameter will override values (default or not) for the + following parameters: `delimiter`, `doublequote`, `escapechar`, + `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to + override values, a ParserWarning will be issued. See csv.Dialect + documentation for more details. +error_bad_lines : bool, default True + Lines with too many fields (e.g. a csv line with too many commas) will by + default cause an exception to be raised, and no DataFrame will be returned. + If False, then these "bad lines" will dropped from the DataFrame that is + returned. +warn_bad_lines : bool, default True + If error_bad_lines is False, and warn_bad_lines is True, a warning for each + "bad line" will be output. +delim_whitespace : bool, default False + Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be + used as the sep. Equivalent to setting ``sep='\\s+'``. If this option + is set to True, nothing should be passed in for the ``delimiter`` + parameter. +low_memory : bool, default True + Internally process the file in chunks, resulting in lower memory use + while parsing, but possibly mixed type inference. To ensure no mixed + types either set False, or specify the type with the `dtype` parameter. + Note that the entire file is read into a single DataFrame regardless, + use the `chunksize` or `iterator` parameter to return the data in chunks. + (Only valid with C parser). +memory_map : bool, default False + If a filepath is provided for `filepath_or_buffer`, map the file object + directly onto memory and access the data directly from there. Using this + option can improve performance because there is no longer any I/O overhead. +float_precision : str, optional + Specifies which converter the C engine should use for floating-point + values. The options are ``None`` or 'high' for the ordinary converter, + 'legacy' for the original lower precision pandas converter, and + 'round_trip' for the round-trip converter. + + .. versionchanged:: 1.2 + +{storage_options} + + .. versionadded:: 1.2 + +Returns +------- +DataFrame or TextParser + A comma-separated values (csv) file is returned as two-dimensional + data structure with labeled axes. + +See Also +-------- +DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. +read_csv : Read a comma-separated values (csv) file into DataFrame. +read_fwf : Read a table of fixed-width formatted lines into DataFrame. + +Examples +-------- +>>> pd.{func_name}('data.csv') # doctest: +SKIP +""" +) + + +_c_parser_defaults = { + "delim_whitespace": False, + "na_filter": True, + "low_memory": True, + "memory_map": False, + "error_bad_lines": True, + "warn_bad_lines": True, + "float_precision": None, +} + +_fwf_defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None} + +_c_unsupported = {"skipfooter"} +_python_unsupported = {"low_memory", "float_precision"} + +_deprecated_defaults: Dict[str, Any] = {} +_deprecated_args: Set[str] = set() + + +def validate_integer(name, val, min_val=0): + """ + Checks whether the 'name' parameter for parsing is either + an integer OR float that can SAFELY be cast to an integer + without losing accuracy. Raises a ValueError if that is + not the case. + + Parameters + ---------- + name : string + Parameter name (used for error reporting) + val : int or float + The value to check + min_val : int + Minimum allowed value (val < min_val will result in a ValueError) + """ + msg = f"'{name:s}' must be an integer >={min_val:d}" + + if val is not None: + if is_float(val): + if int(val) != val: + raise ValueError(msg) + val = int(val) + elif not (is_integer(val) and val >= min_val): + raise ValueError(msg) + + return val + + +def _validate_names(names): + """ + Raise ValueError if the `names` parameter contains duplicates or has an + invalid data type. + + Parameters + ---------- + names : array-like or None + An array containing a list of the names used for the output DataFrame. + + Raises + ------ + ValueError + If names are not unique or are not ordered (e.g. set). + """ + if names is not None: + if len(names) != len(set(names)): + raise ValueError("Duplicate names are not allowed.") + if not ( + is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView) + ): + raise ValueError("Names should be an ordered collection.") + + +def _read(filepath_or_buffer: FilePathOrBuffer, kwds): + """Generic reader of line files.""" + if kwds.get("date_parser", None) is not None: + if isinstance(kwds["parse_dates"], bool): + kwds["parse_dates"] = True + + # Extract some of the arguments (pass chunksize on). + iterator = kwds.get("iterator", False) + chunksize = validate_integer("chunksize", kwds.get("chunksize", None), 1) + nrows = kwds.get("nrows", None) + + # Check for duplicates in names. + _validate_names(kwds.get("names", None)) + + # Create the parser. + parser = TextFileReader(filepath_or_buffer, kwds) + + if chunksize or iterator: + return parser + + with parser: + return parser.read(nrows) + + +@Appender( + _doc_read_csv_and_table.format( + func_name="read_csv", + summary="Read a comma-separated values (csv) file into DataFrame.", + _default_sep="','", + storage_options=generic._shared_docs["storage_options"], + ) +) +def read_csv( + filepath_or_buffer: FilePathOrBuffer, + sep=lib.no_default, + delimiter=None, + # Column and Index Locations and Names + header="infer", + names=None, + index_col=None, + usecols=None, + squeeze=False, + prefix=None, + mangle_dupe_cols=True, + # General Parsing Configuration + dtype: Optional[DtypeArg] = None, + engine=None, + converters=None, + true_values=None, + false_values=None, + skipinitialspace=False, + skiprows=None, + skipfooter=0, + nrows=None, + # NA and Missing Data Handling + na_values=None, + keep_default_na=True, + na_filter=True, + verbose=False, + skip_blank_lines=True, + # Datetime Handling + parse_dates=False, + infer_datetime_format=False, + keep_date_col=False, + date_parser=None, + dayfirst=False, + cache_dates=True, + # Iteration + iterator=False, + chunksize=None, + # Quoting, Compression, and File Format + compression="infer", + thousands=None, + decimal: str = ".", + lineterminator=None, + quotechar='"', + quoting=csv.QUOTE_MINIMAL, + doublequote=True, + escapechar=None, + comment=None, + encoding=None, + dialect=None, + # Error Handling + error_bad_lines=True, + warn_bad_lines=True, + # Internal + delim_whitespace=False, + low_memory=_c_parser_defaults["low_memory"], + memory_map=False, + float_precision=None, + storage_options: StorageOptions = None, +): + kwds = locals() + del kwds["filepath_or_buffer"] + del kwds["sep"] + + kwds_defaults = _refine_defaults_read( + dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": ","} + ) + kwds.update(kwds_defaults) + + return _read(filepath_or_buffer, kwds) + + +@Appender( + _doc_read_csv_and_table.format( + func_name="read_table", + summary="Read general delimited file into DataFrame.", + _default_sep=r"'\\t' (tab-stop)", + storage_options=generic._shared_docs["storage_options"], + ) +) +def read_table( + filepath_or_buffer: FilePathOrBuffer, + sep=lib.no_default, + delimiter=None, + # Column and Index Locations and Names + header="infer", + names=None, + index_col=None, + usecols=None, + squeeze=False, + prefix=None, + mangle_dupe_cols=True, + # General Parsing Configuration + dtype: Optional[DtypeArg] = None, + engine=None, + converters=None, + true_values=None, + false_values=None, + skipinitialspace=False, + skiprows=None, + skipfooter=0, + nrows=None, + # NA and Missing Data Handling + na_values=None, + keep_default_na=True, + na_filter=True, + verbose=False, + skip_blank_lines=True, + # Datetime Handling + parse_dates=False, + infer_datetime_format=False, + keep_date_col=False, + date_parser=None, + dayfirst=False, + cache_dates=True, + # Iteration + iterator=False, + chunksize=None, + # Quoting, Compression, and File Format + compression="infer", + thousands=None, + decimal: str = ".", + lineterminator=None, + quotechar='"', + quoting=csv.QUOTE_MINIMAL, + doublequote=True, + escapechar=None, + comment=None, + encoding=None, + dialect=None, + # Error Handling + error_bad_lines=True, + warn_bad_lines=True, + # Internal + delim_whitespace=False, + low_memory=_c_parser_defaults["low_memory"], + memory_map=False, + float_precision=None, +): + kwds = locals() + del kwds["filepath_or_buffer"] + del kwds["sep"] + + kwds_defaults = _refine_defaults_read( + dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": "\t"} + ) + kwds.update(kwds_defaults) + + return _read(filepath_or_buffer, kwds) + + +def read_fwf( + filepath_or_buffer: FilePathOrBuffer, + colspecs="infer", + widths=None, + infer_nrows=100, + kwds, +): + r""" + Read a table of fixed-width formatted lines into DataFrame. + + Also supports optionally iterating or breaking of the file + into chunks. + + Additional help can be found in the `online docs for IO Tools + <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. + + Parameters + ---------- + filepath_or_buffer : str, path object or file-like object + Any valid string path is acceptable. The string could be a URL. Valid + URL schemes include http, ftp, s3, and file. For file URLs, a host is + expected. A local file could be: + ``file://localhost/path/to/table.csv``. + + If you want to pass in a path object, pandas accepts any + ``os.PathLike``. + + By file-like object, we refer to objects with a ``read()`` method, + such as a file handle (e.g. via builtin ``open`` function) + or ``StringIO``. + colspecs : list of tuple (int, int) or 'infer'. optional + A list of tuples giving the extents of the fixed-width + fields of each line as half-open intervals (i.e., [from, to[ ). + String value 'infer' can be used to instruct the parser to try + detecting the column specifications from the first 100 rows of + the data which are not being skipped via skiprows (default='infer'). + widths : list of int, optional + A list of field widths which can be used instead of 'colspecs' if + the intervals are contiguous. + infer_nrows : int, default 100 + The number of rows to consider when letting the parser determine the + `colspecs`. + + .. versionadded:: 0.24.0 + kwds : optional + Optional keyword arguments can be passed to ``TextFileReader``. + + Returns + ------- + DataFrame or TextParser + A comma-separated values (csv) file is returned as two-dimensional + data structure with labeled axes. + + See Also + -------- + DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. + read_csv : Read a comma-separated values (csv) file into DataFrame. + + Examples + -------- + >>> pd.read_fwf('data.csv') # doctest: +SKIP + """ + # Check input arguments. + if colspecs is None and widths is None: + raise ValueError("Must specify either colspecs or widths") + elif colspecs not in (None, "infer") and widths is not None: + raise ValueError("You must specify only one of 'widths' and 'colspecs'") + + # Compute 'colspecs' from 'widths', if specified. + if widths is not None: + colspecs, col = [], 0 + for w in widths: + colspecs.append((col, col + w)) + col += w + + kwds["colspecs"] = colspecs + kwds["infer_nrows"] = infer_nrows + kwds["engine"] = "python-fwf" + return _read(filepath_or_buffer, kwds) + + +class TextFileReader(abc.Iterator): + """ + + Passed dialect overrides any of the related parser options + + """ + + def __init__(self, f, engine=None, kwds): + + self.f = f + + if engine is not None: + engine_specified = True + else: + engine = "python" + engine_specified = False + self.engine = engine + self._engine_specified = kwds.get("engine_specified", engine_specified) + + _validate_skipfooter(kwds) + + dialect = _extract_dialect(kwds) + if dialect is not None: + kwds = _merge_with_dialect_properties(dialect, kwds) + + if kwds.get("header", "infer") == "infer": + kwds["header"] = 0 if kwds.get("names") is None else None + + self.orig_options = kwds + + # miscellanea + self._currow = 0 + + options = self._get_options_with_defaults(engine) + options["storage_options"] = kwds.get("storage_options", None) + + self.chunksize = options.pop("chunksize", None) + self.nrows = options.pop("nrows", None) + self.squeeze = options.pop("squeeze", False) + + self._check_file_or_buffer(f, engine) + self.options, self.engine = self._clean_options(options, engine) + + if "has_index_names" in kwds: + self.options["has_index_names"] = kwds["has_index_names"] + + self._engine = self._make_engine(self.engine) + + def close(self): + self._engine.close() + + def _get_options_with_defaults(self, engine): + kwds = self.orig_options + + options = {} + + for argname, default in parser_defaults.items(): + value = kwds.get(argname, default) + + # see gh-12935 + if argname == "mangle_dupe_cols" and not value: + raise ValueError("Setting mangle_dupe_cols=False is not supported yet") + else: + options[argname] = value + + for argname, default in _c_parser_defaults.items(): + if argname in kwds: + value = kwds[argname] + + if engine != "c" and value != default: + if "python" in engine and argname not in _python_unsupported: + pass + elif value == _deprecated_defaults.get(argname, default): + pass + else: + raise ValueError( + f"The {repr(argname)} option is not supported with the " + f"{repr(engine)} engine" + ) + else: + value = _deprecated_defaults.get(argname, default) + options[argname] = value + + if engine == "python-fwf": + # pandas\io\parsers.py:907: error: Incompatible types in assignment + # (expression has type "object", variable has type "Union[int, str, + # None]") [assignment] + for argname, default in _fwf_defaults.items(): # type: ignore[assignment] + options[argname] = kwds.get(argname, default) + + return options + + def _check_file_or_buffer(self, f, engine): + # see gh-16530 + if is_file_like(f) and engine != "c" and not hasattr(f, "__next__"): + # The C engine doesn't need the file-like to have the "__next__" + # attribute. However, the Python engine explicitly calls + # "__next__(...)" when iterating through such an object, meaning it + # needs to have that attribute + raise ValueError( + "The 'python' engine cannot iterate through this file buffer." + ) + + def _clean_options(self, options, engine): + result = options.copy() + + fallback_reason = None + + # C engine not supported yet + if engine == "c": + if options["skipfooter"] > 0: + fallback_reason = "the 'c' engine does not support skipfooter" + engine = "python" + + sep = options["delimiter"] + delim_whitespace = options["delim_whitespace"] + + if sep is None and not delim_whitespace: + if engine == "c": + fallback_reason = ( + "the 'c' engine does not support " + "sep=None with delim_whitespace=False" + ) + engine = "python" + elif sep is not None and len(sep) > 1: + if engine == "c" and sep == r"\s+": + result["delim_whitespace"] = True + del result["delimiter"] + elif engine not in ("python", "python-fwf"): + # wait until regex engine integrated + fallback_reason = ( + "the 'c' engine does not support " + "regex separators (separators > 1 char and " + r"different from '\s+' are interpreted as regex)" + ) + engine = "python" + elif delim_whitespace: + if "python" in engine: + result["delimiter"] = r"\s+" + elif sep is not None: + encodeable = True + encoding = sys.getfilesystemencoding() or "utf-8" + try: + if len(sep.encode(encoding)) > 1: + encodeable = False + except UnicodeDecodeError: + encodeable = False + if not encodeable and engine not in ("python", "python-fwf"): + fallback_reason = ( + f"the separator encoded in {encoding} " + "is > 1 char long, and the 'c' engine " + "does not support such separators" + ) + engine = "python" + + quotechar = options["quotechar"] + if quotechar is not None and isinstance(quotechar, (str, bytes)): + if ( + len(quotechar) == 1 + and ord(quotechar) > 127 + and engine not in ("python", "python-fwf") + ): + fallback_reason = ( + "ord(quotechar) > 127, meaning the " + "quotechar is larger than one byte, " + "and the 'c' engine does not support such quotechars" + ) + engine = "python" + + if fallback_reason and self._engine_specified: + raise ValueError(fallback_reason) + + if engine == "c": + for arg in _c_unsupported: + del result[arg] + + if "python" in engine: + for arg in _python_unsupported: + if fallback_reason and result[arg] != _c_parser_defaults[arg]: + raise ValueError( + "Falling back to the 'python' engine because " + f"{fallback_reason}, but this causes {repr(arg)} to be " + "ignored as it is not supported by the 'python' engine." + ) + del result[arg] + + if fallback_reason: + warnings.warn( + ( + "Falling back to the 'python' engine because " + f"{fallback_reason}; you can avoid this warning by specifying " + "engine='python'." + ), + ParserWarning, + stacklevel=5, + ) + + index_col = options["index_col"] + names = options["names"] + converters = options["converters"] + na_values = options["na_values"] + skiprows = options["skiprows"] + + validate_header_arg(options["header"]) + + for arg in _deprecated_args: + parser_default = _c_parser_defaults[arg] + depr_default = _deprecated_defaults[arg] + if result.get(arg, depr_default) != depr_default: + msg = ( + f"The {arg} argument has been deprecated and will be " + "removed in a future version.\n\n" + ) + warnings.warn(msg, FutureWarning, stacklevel=2) + else: + result[arg] = parser_default + + if index_col is True: + raise ValueError("The value of index_col couldn't be 'True'") + if is_index_col(index_col): + if not isinstance(index_col, (list, tuple, np.ndarray)): + index_col = [index_col] + result["index_col"] = index_col + + names = list(names) if names is not None else names + + # type conversion-related + if converters is not None: + if not isinstance(converters, dict): + raise TypeError( + "Type converters must be a dict or subclass, " + f"input was a {type(converters).__name__}" + ) + else: + converters = {} + + # Converting values to NA + keep_default_na = options["keep_default_na"] + na_values, na_fvalues = _clean_na_values(na_values, keep_default_na) + + # handle skiprows; this is internally handled by the + # c-engine, so only need for python parsers + if engine != "c": + if is_integer(skiprows): + skiprows = list(range(skiprows)) + if skiprows is None: + skiprows = set() + elif not callable(skiprows): + skiprows = set(skiprows) + + # put stuff back + result["names"] = names + result["converters"] = converters + result["na_values"] = na_values + result["na_fvalues"] = na_fvalues + result["skiprows"] = skiprows + + return result, engine + + def __next__(self): + try: + return self.get_chunk() + except StopIteration: + self.close() + raise + + def _make_engine(self, engine="c"): + mapping: Dict[str, Type[ParserBase]] = { + "c": CParserWrapper, + "python": PythonParser, + "python-fwf": FixedWidthFieldParser, + } + if engine not in mapping: + raise ValueError( + f"Unknown engine: {engine} (valid options are {mapping.keys()})" + ) + # error: Too many arguments for "ParserBase" + return mapping[engine](self.f, self.options) # type: ignore[call-arg] + + def _failover_to_python(self): + raise AbstractMethodError(self) + + def read(self, nrows=None): + nrows = validate_integer("nrows", nrows) + index, columns, col_dict = self._engine.read(nrows) + + if index is None: + if col_dict: + # Any column is actually fine: + new_rows = len(next(iter(col_dict.values()))) + index = RangeIndex(self._currow, self._currow + new_rows) + else: + new_rows = 0 + else: + new_rows = len(index) + + df = DataFrame(col_dict, columns=columns, index=index) + + self._currow += new_rows + + if self.squeeze and len(df.columns) == 1: + return df[df.columns[0]].copy() + return df + + def get_chunk(self, size=None): + if size is None: + size = self.chunksize + if self.nrows is not None: + if self._currow >= self.nrows: + raise StopIteration + size = min(size, self.nrows - self._currow) + return self.read(nrows=size) + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.close() + + +def TextParser(args, *kwds): + """ + Converts lists of lists/tuples into DataFrames with proper type inference + and optional (e.g. string to datetime) conversion. Also enables iterating + lazily over chunks of large files + + Parameters + ---------- + data : file-like object or list + delimiter : separator character to use + dialect : str or csv.Dialect instance, optional + Ignored if delimiter is longer than 1 character + names : sequence, default + header : int, default 0 + Row to use to parse column labels. Defaults to the first row. Prior + rows will be discarded + index_col : int or list, optional + Column or columns to use as the (possibly hierarchical) index + has_index_names: bool, default False + True if the cols defined in index_col have an index name and are + not in the header. + na_values : scalar, str, list-like, or dict, optional + Additional strings to recognize as NA/NaN. + keep_default_na : bool, default True + thousands : str, optional + Thousands separator + comment : str, optional + Comment out remainder of line + parse_dates : bool, default False + keep_date_col : bool, default False + date_parser : function, optional + skiprows : list of integers + Row numbers to skip + skipfooter : int + Number of line at bottom of file to skip + converters : dict, optional + Dict of functions for converting values in certain columns. Keys can + either be integers or column labels, values are functions that take one + input argument, the cell (not column) content, and return the + transformed content. + encoding : str, optional + Encoding to use for UTF when reading/writing (ex. 'utf-8') + squeeze : bool, default False + returns Series if only one column. + infer_datetime_format: bool, default False + If True and `parse_dates` is True for a column, try to infer the + datetime format based on the first datetime string. If the format + can be inferred, there often will be a large parsing speed-up. + float_precision : str, optional + Specifies which converter the C engine should use for floating-point + values. The options are `None` or `high` for the ordinary converter, + `legacy` for the original lower precision pandas converter, and + `round_trip` for the round-trip converter. + + .. versionchanged:: 1.2 + """ + kwds["engine"] = "python" + return TextFileReader(args, **kwds) + + +def _clean_na_values(na_values, keep_default_na=True): + + if na_values is None: + if keep_default_na: + na_values = STR_NA_VALUES + else: + na_values = set() + # pandas\io\parsers.py:3387: error: Need type annotation for + # 'na_fvalues' (hint: "na_fvalues: Set[<type>] = ...") [var-annotated] + na_fvalues = set() # type: ignore[var-annotated] + elif isinstance(na_values, dict): + old_na_values = na_values.copy() + na_values = {} # Prevent aliasing. + + # Convert the values in the na_values dictionary + # into array-likes for further use. This is also + # where we append the default NaN values, provided + # that `keep_default_na=True`. + for k, v in old_na_values.items(): + if not is_list_like(v): + v = [v] + + if keep_default_na: + v = set(v) \| STR_NA_VALUES + + na_values[k] = v + # pandas\io\parsers.py:3404: error: Incompatible types in assignment + # (expression has type "Dict[Any, Any]", variable has type "Set[Any]") + # [assignment] + na_fvalues = { # type: ignore[assignment] + k: _floatify_na_values(v) for k, v in na_values.items() + } + else: + if not is_list_like(na_values): + na_values = [na_values] + na_values = _stringify_na_values(na_values) + if keep_default_na: + na_values = na_values \| STR_NA_VALUES + + na_fvalues = _floatify_na_values(na_values) + + return na_values, na_fvalues + + +def _floatify_na_values(na_values): + # create float versions of the na_values + result = set() + for v in na_values: + try: + v = float(v) + if not np.isnan(v): + result.add(v) + except (TypeError, ValueError, OverflowError): + pass + return result + + +def _stringify_na_values(na_values): + """ return a stringified and numeric for these values """ + result = [] + for x in na_values: + result.append(str(x)) + result.append(x) + try: + v = float(x) + + # we are like 999 here + if v == int(v): + v = int(v) + result.append(f"{v}.0") + result.append(str(v)) + + # pandas\io\parsers.py:3522: error: Argument 1 to "append" of + # "list" has incompatible type "float"; expected "str" [arg-type] + result.append(v) # type: ignore[arg-type] + except (TypeError, ValueError, OverflowError): + pass + try: + # pandas\io\parsers.py:3526: error: Argument 1 to "append" of + # "list" has incompatible type "int"; expected "str" [arg-type] + result.append(int(x)) # type: ignore[arg-type] + except (TypeError, ValueError, OverflowError): + pass + return set(result) + + +def _refine_defaults_read( + dialect: Union[str, csv.Dialect], + delimiter: Union[str, object], + delim_whitespace: bool, + engine: str, + sep: Union[str, object], + defaults: Dict[str, Any], +): + """Validate/refine default values of input parameters of read_csv, read_table. + + Parameters + ---------- + dialect : str or csv.Dialect + If provided, this parameter will override values (default or not) for the + following parameters: `delimiter`, `doublequote`, `escapechar`, + `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to + override values, a ParserWarning will be issued. See csv.Dialect + documentation for more details. + delimiter : str or object + Alias for sep. + delim_whitespace : bool + Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be + used as the sep. Equivalent to setting ``sep='\\s+'``. If this option + is set to True, nothing should be passed in for the ``delimiter`` + parameter. + engine : {{'c', 'python'}} + Parser engine to use. The C engine is faster while the python engine is + currently more feature-complete. + sep : str or object + A delimiter provided by the user (str) or a sentinel value, i.e. + pandas._libs.lib.no_default. + defaults: dict + Default values of input parameters. + + Returns + ------- + kwds : dict + Input parameters with correct values. + + Raises + ------ + ValueError : If a delimiter was specified with ``sep`` (or ``delimiter``) and + ``delim_whitespace=True``. + """ + # fix types for sep, delimiter to Union(str, Any) + delim_default = defaults["delimiter"] + kwds: Dict[str, Any] = {} + # gh-23761 + # + # When a dialect is passed, it overrides any of the overlapping + # parameters passed in directly. We don't want to warn if the + # default parameters were passed in (since it probably means + # that the user didn't pass them in explicitly in the first place). + # + # "delimiter" is the annoying corner case because we alias it to + # "sep" before doing comparison to the dialect values later on. + # Thus, we need a flag to indicate that we need to "override" + # the comparison to dialect values by checking if default values + # for BOTH "delimiter" and "sep" were provided. + if dialect is not None: + kwds["sep_override"] = delimiter is None and ( + sep is lib.no_default or sep == delim_default + ) + + # Alias sep -> delimiter. + if delimiter is None: + delimiter = sep + + if delim_whitespace and (delimiter is not lib.no_default): + raise ValueError( + "Specified a delimiter with both sep and " + "delim_whitespace=True; you can only specify one." + ) + + if delimiter is lib.no_default: + # assign default separator value + kwds["delimiter"] = delim_default + else: + kwds["delimiter"] = delimiter + + if engine is not None: + kwds["engine_specified"] = True + else: + kwds["engine"] = "c" + kwds["engine_specified"] = False + + return kwds + + +def _extract_dialect(kwds: Dict[str, Any]) -> Optional[csv.Dialect]: + """ + Extract concrete csv dialect instance. + + Returns + ------- + csv.Dialect or None + """ + if kwds.get("dialect") is None: + return None + + dialect = kwds["dialect"] + if dialect in csv.list_dialects(): + dialect = csv.get_dialect(dialect) + + _validate_dialect(dialect) + + return dialect + + +MANDATORY_DIALECT_ATTRS = ( + "delimiter", + "doublequote", + "escapechar", + "skipinitialspace", + "quotechar", + "quoting", +) + + +def _validate_dialect(dialect: csv.Dialect) -> None: + """ + Validate csv dialect instance. + + Raises + ------ + ValueError + If incorrect dialect is provided. + """ + for param in MANDATORY_DIALECT_ATTRS: + if not hasattr(dialect, param): + raise ValueError(f"Invalid dialect {dialect} provided") + + +def _merge_with_dialect_properties( + dialect: csv.Dialect, + defaults: Dict[str, Any], +) -> Dict[str, Any]: + """ + Merge default kwargs in TextFileReader with dialect parameters. + + Parameters + ---------- + dialect : csv.Dialect + Concrete csv dialect. See csv.Dialect documentation for more details. + defaults : dict + Keyword arguments passed to TextFileReader. + + Returns + ------- + kwds : dict + Updated keyword arguments, merged with dialect parameters. + """ + kwds = defaults.copy() + + for param in MANDATORY_DIALECT_ATTRS: + dialect_val = getattr(dialect, param) + + parser_default = parser_defaults[param] + provided = kwds.get(param, parser_default) + + # Messages for conflicting values between the dialect + # instance and the actual parameters provided. + conflict_msgs = [] + + # Don't warn if the default parameter was passed in, + # even if it conflicts with the dialect (gh-23761). + if provided != parser_default and provided != dialect_val: + msg = ( + f"Conflicting values for '{param}': '{provided}' was " + f"provided, but the dialect specifies '{dialect_val}'. " + "Using the dialect-specified value." + ) + + # Annoying corner case for not warning about + # conflicts between dialect and delimiter parameter. + # Refer to the outer "_read_" function for more info. + if not (param == "delimiter" and kwds.pop("sep_override", False)): + conflict_msgs.append(msg) + + if conflict_msgs: + warnings.warn("\n\n".join(conflict_msgs), ParserWarning, stacklevel=2) + kwds[param] = dialect_val + return kwds + + +def _validate_skipfooter(kwds: Dict[str, Any]) -> None: + """ + Check whether skipfooter is compatible with other kwargs in TextFileReader. + + Parameters + ---------- + kwds : dict + Keyword arguments passed to TextFileReader. + + Raises + ------ + ValueError + If skipfooter is not compatible with other parameters. + """ + if kwds.get("skipfooter"): + if kwds.get("iterator") or kwds.get("chunksize"): + raise ValueError("'skipfooter' not supported for iteration") + if kwds.get("nrows"): + raise ValueError("'skipfooter' not supported with 'nrows'")	TST/REF: splitting pandas/io/parsers.py into multiple files `pandas/io/parsers.py` is close to 4k LOC (3986 on 1.3 master). Would it be reasonable to split it into multiple files (located, say, in a `pandas/io/parsers` directory)? AFAICT the file contains 4 logical pieces: - `read_...` methods (~700 LOC) - `ParserBase` and related module-level methods ( ~1100 LOC) - `CParser` and related module-level methods (~400LOC) - `PythonParser`, its derived classes and related module-level methods (~1800 LOC) Once #38370 goes in these would be joined by - `PyarrowParser` (~100 LOC right now) IMO this kind of refactor would make the module somewhat easier to grok	sure seems reasonable	2021-01-17T00:38:48Z	[]	[]

PrefectHQ/prefect

PrefectHQ__prefect-856

5739feb6eaac6a62e090e5c4590873d4474823bb

diff --git a/setup.py b/setup.py --- a/setup.py +++ b/setup.py @@ -17,6 +17,7 @@ "google-cloud-storage >= 1.13, < 2.0", ], "kubernetes": ["dask-kubernetes == 0.7.0", "kubernetes >= 8.0.1, < 9.0"], + "rss": ["feedparser >= 5.0.1, < 6.0"], "templates": ["jinja2 >= 2.0, < 3.0"], "viz": ["graphviz >= 0.8.3"], "twitter": ["tweepy >= 3.5, < 4.0"], diff --git a/src/prefect/tasks/rss/__init__.py b/src/prefect/tasks/rss/__init__.py new file mode 100644 --- /dev/null +++ b/src/prefect/tasks/rss/__init__.py @@ -0,0 +1,9 @@ +""" +Tasks for interacting with RSS feeds. +""" +try: + from prefect.tasks.rss.feed import ParseRSSFeed +except ImportError: + raise ImportError( + 'Using `prefect.tasks.rss` requires Prefect to be installed with the "rss" extra.' + ) diff --git a/src/prefect/tasks/rss/feed.py b/src/prefect/tasks/rss/feed.py new file mode 100644 --- /dev/null +++ b/src/prefect/tasks/rss/feed.py @@ -0,0 +1,42 @@ +from typing import Any + +import feedparser + +from prefect import Task +from prefect.utilities.tasks import defaults_from_attrs + + +class ParseRSSFeed(Task): + """ + Task for parsing RSS feeds. + + Args: + - feed_url (str): A remote URL pointing to an RSS feed + - **kwargs (dict, optional): additional keyword arguments to pass to the Task + constructor + """ + + def __init__(self, feed_url: str = None, **kwargs: Any): + self.feed_url = feed_url + + super().__init__(**kwargs) + + @defaults_from_attrs("feed_url") + def run(self, feed_url: str = None) -> "feedparser.FeedParserDict": + """ + Task run method. + + Args: + - feed_url (str): A remote URL pointing to an RSS feed + + Return: + - FeedParserDict: A dictionary representing the information from the + parsed feed. The object is accessable through indexing and attributes. + + Raises: + - ValueError: if `feed_url` is `None` + """ + if not feed_url: + raise ValueError("The feed_url must be provided.") + + return feedparser.parse(feed_url)

Create RSS Tasks for Task Library

2019-03-26T19:27:14Z

[]

Qiskit/qiskit

Qiskit__qiskit-1856

240398505c6df0308992e697b4ad26c611faf248

diff --git a/examples/python/hello_quantum.py b/examples/python/hello_quantum.py --- a/examples/python/hello_quantum.py +++ b/examples/python/hello_quantum.py @@ -50,16 +50,17 @@ # Compile and run the Quantum Program on a real device backend try: least_busy_device = least_busy(IBMQ.backends(simulator=False)) - print("Running on current least busy device: ", least_busy_device) - - #running the job - job_exp = execute(qc, least_busy_device, shots=1024, max_credits=10) - result_exp = job_exp.result() - - # Show the results - print(result_exp.get_counts(qc)) except: print("All devices are currently unavailable.") + print("Running on current least busy device: ", least_busy_device) + + #running the job + job_exp = execute(qc, least_busy_device, shots=1024, max_credits=10) + result_exp = job_exp.result() + + # Show the results + print(result_exp.get_counts(qc)) + except QiskitError as ex: print('There was an error in the circuit!. Error = {}'.format(ex)) diff --git a/examples/python/rippleadd.py b/examples/python/rippleadd.py --- a/examples/python/rippleadd.py +++ b/examples/python/rippleadd.py @@ -13,7 +13,8 @@ """ from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit -from qiskit import compile, BasicAer +from qiskit import BasicAer +from qiskit import execute ############################################################### # Set the backend name and coupling map. @@ -76,14 +77,12 @@ def unmajority(p, a, b, c): ############################################################### # First version: not mapped -qobj = compile(qc, backend=backend, coupling_map=None, shots=1024) -job = backend.run(qobj) +job = execute(qc, backend=backend, coupling_map=None, shots=1024) result = job.result() print(result.get_counts(qc)) # Second version: mapped to 2x8 array coupling graph -qobj = compile(qc, backend=backend, coupling_map=coupling_map, shots=1024) -job = backend.run(qobj) +job = execute(qc, backend=backend, coupling_map=coupling_map, shots=1024) result = job.result() print(result.get_counts(qc)) diff --git a/examples/python/teleport.py b/examples/python/teleport.py --- a/examples/python/teleport.py +++ b/examples/python/teleport.py @@ -13,7 +13,8 @@ """ from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit -from qiskit import compile, BasicAer +from qiskit import BasicAer +from qiskit import execute ############################################################### # Set the backend name and coupling map. @@ -58,20 +59,14 @@ ############################################################### # First version: not mapped -initial_layout = {("q", 0): ("q", 0), ("q", 1): ("q", 1), - ("q", 2): ("q", 2)} -qobj = compile(qc, backend=backend, coupling_map=None, shots=1024, initial_layout=initial_layout) -job = backend.run(qobj) -qobj_exp = qobj.experiments[0] +initial_layout = [q[0], q[1], q[2]] +job = execute(qc, backend=backend, coupling_map=None, shots=1024, initial_layout=initial_layout) result = job.result() print(result.get_counts(qc)) # Second version: mapped to 2x8 array coupling graph -qobj = compile(qc, backend=backend, coupling_map=coupling_map, shots=1024,initial_layout=initial_layout) -qobj_exp = qobj.experiments[0] -qobj_exp.header.compiled_circuit_qasm = "" -job = backend.run(qobj) +job = execute(qc, backend=backend, coupling_map=coupling_map, shots=1024,initial_layout=initial_layout) result = job.result() print(result.get_counts(qc)) # Both versions should give the same distribution diff --git a/examples/python/using_qiskit_terra_level_0.py b/examples/python/using_qiskit_terra_level_0.py --- a/examples/python/using_qiskit_terra_level_0.py +++ b/examples/python/using_qiskit_terra_level_0.py @@ -68,18 +68,20 @@ try: # select least busy available device and execute. least_busy_device = least_busy(IBMQ.backends(simulator=False)) - print("Running on current least busy device: ", least_busy_device) + except: + print("All devices are currently unavailable.") - # running the job - job_exp = execute([qc1, qc2], backend=least_busy_device, shots=1024, max_credits=10) + print("Running on current least busy device: ", least_busy_device) - job_monitor(job_exp) - exp_result = job_exp.result() + # running the job + job_exp = execute([qc1, qc2], backend=least_busy_device, shots=1024, max_credits=10) + + job_monitor(job_exp) + exp_result = job_exp.result() + + # Show the results + print(exp_result.get_counts(qc1)) + print(exp_result.get_counts(qc2)) - # Show the results - print(exp_result.get_counts(qc1)) - print(exp_result.get_counts(qc2)) - except: - print("All devices are currently unavailable.") except QiskitError as ex: print('There was an error in the circuit!. Error = {}'.format(ex)) diff --git a/examples/python/using_qiskit_terra_level_1.py b/examples/python/using_qiskit_terra_level_1.py --- a/examples/python/using_qiskit_terra_level_1.py +++ b/examples/python/using_qiskit_terra_level_1.py @@ -8,20 +8,28 @@ """ Example showing how to use Qiskit at level 1 (intermediate). -This example shows how an intermediate user interacts with Terra. It builds some circuits -and compiles them from compile parameters. It makes a qobj object which is just and container to be -run on a backend. The same qobj can run on many backends (as shown). It is the -user responsibility to make sure it can be run. This is useful when you want to compare the same -circuits on different backends or change the compile parameters. - -To control the passes and we have a pass manager for level 2 user. +This example shows how an intermediate user interacts with Terra. +It builds some circuits and transpiles them with transpile options. +It then makes a qobj object which is just a container to be run on a backend. +The same qobj can be submitted to many backends (as shown). +It is the user's responsibility to make sure it can be run (i.e. it conforms +to the restrictions of the backend, if any). +This is useful when you want to compare the same +circuit on different backends without recompiling the whole circuit, +or just want to change some runtime parameters. + +To control the passes that transform the circuit, we have a pass manager +for the level 2 user. """ import pprint, time # Import the Qiskit modules -from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, QiskitError -from qiskit import compile, IBMQ, BasicAer +from qiskit import IBMQ, BasicAer +from qiskit import QiskitError +from qiskit.circuit import QuantumCircuit, ClassicalRegister, QuantumRegister +from qiskit.compiler import transpile, assemble_circuits +from qiskit.compiler import TranspileConfig, RunConfig from qiskit.providers.ibmq import least_busy from qiskit.tools.monitor import job_monitor @@ -33,7 +41,7 @@ For now, there's only access to local simulator backends...""") try: - # Create a Quantum and Classical Register and giving a name. + # Create a Quantum and Classical Register and give them names. qubit_reg = QuantumRegister(2, name='q') clbit_reg = ClassicalRegister(2, name='c') @@ -52,31 +60,48 @@ print("(Aer Backends)") for backend in BasicAer.backends(): print(backend.status()) - my_backend = BasicAer.get_backend('qasm_simulator') + qasm_simulator = BasicAer.get_backend('qasm_simulator') print("(QASM Simulator configuration) ") - pprint.pprint(my_backend.configuration()) + pprint.pprint(qasm_simulator.configuration()) print("(QASM Simulator properties) ") - pprint.pprint(my_backend.properties()) + pprint.pprint(qasm_simulator.properties()) - print("\n(IMQ Backends)") + # Compile and run the circuit on a real device backend + # See a list of available remote backends + print("\n(IBMQ Backends)") for backend in IBMQ.backends(): print(backend.status()) - # select least busy available device and execute. - least_busy_device = least_busy(IBMQ.backends(simulator=False)) + try: + # select least busy available device and execute. + least_busy_device = least_busy(IBMQ.backends(simulator=False)) + except: + print("All devices are currently unavailable.") + print("Running on current least busy device: ", least_busy_device) print("(with configuration) ") pprint.pprint(least_busy_device.configuration()) print("(with properties) ") pprint.pprint(least_busy_device.properties()) + # Transpile the circuits to make them compatible with the experimental backend + [qc1_new, qc2_new] = transpile(circuits=[qc1, qc2], + transpile_config=TranspileConfig(backend=least_busy_device)) + print("Bell circuit before transpile:") + print(qc1) + print("Bell circuit after transpile:") + print(qc1_new) + print("Superposition circuit before transpile:") + print(qc2) + print("Superposition circuit after transpile:") + print(qc2_new) - # Compiling the job for the experimental backend - qobj = compile([qc1, qc2], backend=least_busy_device, shots=1024, max_credits=10) + # Assemble the two circuits into a runnable qobj + qobj = assemble_circuits([qc1_new, qc2_new], run_config=RunConfig(shots=1000)) - # Running the job - sim_job = my_backend.run(qobj) + # Running qobj on the simulator + sim_job = qasm_simulator.run(qobj) # Getting the result sim_result=sim_job.result() @@ -85,20 +110,15 @@ print(sim_result.get_counts(qc1)) print(sim_result.get_counts(qc2)) - # Compile and run the Quantum Program on a real device backend - # See a list of available remote backends - try: - # Running the job. - exp_job = least_busy_device.run(qobj) + # Running the job. + exp_job = least_busy_device.run(qobj) - job_monitor(exp_job) - exp_result = exp_job.result() + job_monitor(exp_job) + exp_result = exp_job.result() - # Show the results - print(exp_result.get_counts(qc1)) - print(exp_result.get_counts(qc2)) - except: - print("All devices are currently unavailable.") + # Show the results + print(exp_result.get_counts(qc1)) + print(exp_result.get_counts(qc2)) except QiskitError as ex: print('There was an error in the circuit!. Error = {}'.format(ex)) diff --git a/qiskit/__init__.py b/qiskit/__init__.py --- a/qiskit/__init__.py +++ b/qiskit/__init__.py @@ -7,6 +7,7 @@ # pylint: disable=wrong-import-order + """Main Qiskit public functionality.""" import pkgutil @@ -21,7 +22,9 @@ from qiskit.circuit import ClassicalRegister from qiskit.circuit import QuantumRegister from qiskit.circuit import QuantumCircuit -from .tools.compiler import (compile, execute) +# pylint: disable=redefined-builtin +from qiskit.tools.compiler import compile # TODO remove after 0.8 +from qiskit.execute import (execute_circuits, execute) # The qiskit.extensions.x imports needs to be placed here due to the # mechanism for adding gates dynamically. @@ -38,12 +41,12 @@ # Please note these are global instances, not modules. from qiskit.providers.basicaer import BasicAer -# Try to import the Aer provider if the Aer element is installed. +# Try to import the Aer provider if installed. try: from qiskit.providers.aer import Aer except ImportError: pass -# Try to import the IBQM provider if the IBMQ element is installed. +# Try to import the IBMQ provider if installed. try: from qiskit.providers.ibmq import IBMQ except ImportError: diff --git a/qiskit/compiler/__init__.py b/qiskit/compiler/__init__.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/__init__.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Helper module for Qiskit compiler. + +""" + +from .run_config import RunConfig +from .transpile_config import TranspileConfig +from .assembler import assemble_circuits +from .transpiler import transpile diff --git a/qiskit/compiler/assembler.py b/qiskit/compiler/assembler.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/assembler.py @@ -0,0 +1,122 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Assemble function for converting a list of circuits into a qobj""" +import uuid +import sympy +import numpy + +from qiskit.circuit.quantumcircuit import QuantumCircuit +from qiskit.qobj import Qobj, QobjConfig, QobjExperiment, QobjInstruction, QobjHeader +from qiskit.qobj import QobjExperimentConfig, QobjExperimentHeader, QobjConditional +from qiskit.compiler.run_config import RunConfig +from qiskit.qobj.utils import QobjType + + +def assemble_circuits(circuits, run_config=None, qobj_header=None, qobj_id=None): + """Assembles a list of circuits into a qobj which can be run on the backend. + + Args: + circuits (list[QuantumCircuits] or QuantumCircuit): circuits to assemble + run_config (RunConfig): RunConfig object + qobj_header (QobjHeader): header to pass to the results + qobj_id (int): identifier for the generated qobj + + Returns: + Qobj: the Qobj to be run on the backends + """ + qobj_header = qobj_header or QobjHeader() + run_config = run_config or RunConfig() + if isinstance(circuits, QuantumCircuit): + circuits = [circuits] + + userconfig = QobjConfig(**run_config.to_dict()) + experiments = [] + max_n_qubits = 0 + max_memory_slots = 0 + for circuit in circuits: + # header stuff + n_qubits = 0 + memory_slots = 0 + qubit_labels = [] + clbit_labels = [] + + qreg_sizes = [] + creg_sizes = [] + for qreg in circuit.qregs: + qreg_sizes.append([qreg.name, qreg.size]) + for j in range(qreg.size): + qubit_labels.append([qreg.name, j]) + n_qubits += qreg.size + for creg in circuit.cregs: + creg_sizes.append([creg.name, creg.size]) + for j in range(creg.size): + clbit_labels.append([creg.name, j]) + memory_slots += creg.size + + # TODO: why do we need creq_sizes and qreg_sizes in header + # TODO: we need to rethink memory_slots as they are tied to classical bit + experimentheader = QobjExperimentHeader(qubit_labels=qubit_labels, + n_qubits=n_qubits, + qreg_sizes=qreg_sizes, + clbit_labels=clbit_labels, + memory_slots=memory_slots, + creg_sizes=creg_sizes, + name=circuit.name) + # TODO: why do we need n_qubits and memory_slots in both the header and the config + experimentconfig = QobjExperimentConfig(n_qubits=n_qubits, memory_slots=memory_slots) + + instructions = [] + for opt in circuit.data: + current_instruction = QobjInstruction(name=opt.name) + if opt.qargs: + qubit_indices = [qubit_labels.index([qubit[0].name, qubit[1]]) + for qubit in opt.qargs] + current_instruction.qubits = qubit_indices + if opt.cargs: + clbit_indices = [clbit_labels.index([clbit[0].name, clbit[1]]) + for clbit in opt.cargs] + current_instruction.memory = clbit_indices + + if opt.params: + params = list(map(lambda x: x.evalf(), opt.params)) + params = [sympy.matrix2numpy(x, dtype=complex) + if isinstance(x, sympy.Matrix) else x for x in params] + if len(params) == 1 and isinstance(params[0], numpy.ndarray): + # TODO: Aer expects list of rows for unitary instruction params; + # change to matrix in Aer. + params = params[0] + current_instruction.params = params + # TODO (jay): I really dont like this for snapshot. I also think we should change + # type to snap_type + if opt.name == "snapshot": + current_instruction.label = str(opt.params[0]) + current_instruction.type = str(opt.params[1]) + if opt.control: + mask = 0 + for clbit in clbit_labels: + if clbit[0] == opt.control[0].name: + mask |= (1 << clbit_labels.index(clbit)) + + current_instruction.conditional = QobjConditional(mask="0x%X" % mask, + type='equals', + val="0x%X" % opt.control[1]) + + instructions.append(current_instruction) + experiments.append(QobjExperiment(instructions=instructions, header=experimentheader, + config=experimentconfig)) + if n_qubits > max_n_qubits: + max_n_qubits = n_qubits + if memory_slots > max_memory_slots: + max_memory_slots = memory_slots + + userconfig.memory_slots = max_memory_slots + userconfig.n_qubits = max_n_qubits + + return Qobj(qobj_id=qobj_id or str(uuid.uuid4()), config=userconfig, + experiments=experiments, header=qobj_header, + type=QobjType.QASM.value) diff --git a/qiskit/compiler/compile.py b/qiskit/compiler/compile.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/compile.py @@ -0,0 +1,75 @@ +# -*- coding: utf-8 -*- + +# Copyright 2018, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Helper module for simplified Qiskit usage.""" +import warnings +import logging + +from qiskit import transpiler +from qiskit.converters import circuits_to_qobj +from qiskit.compiler import RunConfig +from qiskit.qobj import QobjHeader +from qiskit.mapper import Layout + + +logger = logging.getLogger(__name__) + + +# pylint: disable=redefined-builtin +def compile(circuits, backend, + config=None, basis_gates=None, coupling_map=None, initial_layout=None, + shots=1024, max_credits=10, seed=None, qobj_id=None, seed_mapper=None, + pass_manager=None, memory=False): + """Compile a list of circuits into a qobj. + + Args: + circuits (QuantumCircuit or list[QuantumCircuit]): circuits to compile + backend (BaseBackend): a backend to compile for + config (dict): dictionary of parameters (e.g. noise) used by runner + basis_gates (list[str]): list of basis gates names supported by the + target. Default: ['u1','u2','u3','cx','id'] + coupling_map (list): coupling map (perhaps custom) to target in mapping + initial_layout (list): initial layout of qubits in mapping + shots (int): number of repetitions of each circuit, for sampling + max_credits (int): maximum credits to use + seed (int): random seed for simulators + seed_mapper (int): random seed for swapper mapper + qobj_id (int): identifier for the generated qobj + pass_manager (PassManager): a pass manger for the transpiler pipeline + memory (bool): if True, per-shot measurement bitstrings are returned as well + + Returns: + Qobj: the qobj to be run on the backends + + Raises: + QiskitError: if the desired options are not supported by backend + """ + if config: + warnings.warn('The `config` argument is deprecated and ' + 'does not do anything', DeprecationWarning) + + if initial_layout is not None and not isinstance(initial_layout, Layout): + initial_layout = Layout(initial_layout) + + circuits = transpiler.transpile(circuits, backend, basis_gates, coupling_map, initial_layout, + seed_mapper, pass_manager) + + # step 4: Making a qobj + run_config = RunConfig() + + if seed: + run_config.seed = seed + if shots: + run_config.shots = shots + if max_credits: + run_config.max_credits = max_credits + if memory: + run_config.memory = memory + qobj = circuits_to_qobj(circuits, qobj_header=QobjHeader(), run_config=run_config, + qobj_id=qobj_id) + + return qobj diff --git a/qiskit/compiler/models.py b/qiskit/compiler/models.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/models.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Models for RunConfig and its related components.""" + +from marshmallow.validate import Range + +from qiskit.validation import BaseSchema +from qiskit.validation.fields import Boolean, Integer + + +class TranspileConfigSchema(BaseSchema): + """Schema for TranspileConfig.""" + + # Required properties. + # None + + # Optional properties. + + +class RunConfigSchema(BaseSchema): + """Schema for RunConfig.""" + + # Required properties. + # None + + # Optional properties. + shots = Integer(validate=Range(min=1)) + max_credits = Integer(validate=Range(min=3, max=10)) # TODO: can we check the range + seed = Integer() + memory = Boolean() # set default to be False diff --git a/qiskit/qobj/run_config.py b/qiskit/compiler/run_config.py similarity index 60% rename from qiskit/qobj/run_config.py rename to qiskit/compiler/run_config.py --- a/qiskit/qobj/run_config.py +++ b/qiskit/compiler/run_config.py @@ -7,23 +7,8 @@ """Models for RunConfig and its related components.""" -from marshmallow.validate import Range - -from qiskit.validation import BaseModel, BaseSchema, bind_schema -from qiskit.validation.fields import Boolean, Integer - - -class RunConfigSchema(BaseSchema): - """Schema for RunConfig.""" - - # Required properties. - # None - - # Optional properties. - shots = Integer(validate=Range(min=1)) - max_credits = Integer(validate=Range(min=3, max=10)) # TODO: can we check the range - seed = Integer() - memory = Boolean() # set default to be False +from qiskit.compiler.models import RunConfigSchema +from qiskit.validation import BaseModel, bind_schema @bind_schema(RunConfigSchema) diff --git a/qiskit/compiler/transpile_config.py b/qiskit/compiler/transpile_config.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/transpile_config.py @@ -0,0 +1,23 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Models for TranspileConfig and its related components.""" + +from qiskit.compiler.models import TranspileConfigSchema +from qiskit.validation import BaseModel, bind_schema + + +@bind_schema(TranspileConfigSchema) +class TranspileConfig(BaseModel): + """Model for TranspileConfig. + + Please note that this class only describes the required fields. For the + full description of the model, please check ``TranspileConfigSchema``. + + Attributes: + + """ diff --git a/qiskit/compiler/transpiler.py b/qiskit/compiler/transpiler.py new file mode 100644 --- /dev/null +++ b/qiskit/compiler/transpiler.py @@ -0,0 +1,50 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +"""Circuit transpile function """ +import logging + +from qiskit import transpiler +from qiskit.mapper import Layout + + +logger = logging.getLogger(__name__) + + +def transpile(circuits, transpile_config=None): + """Compile a list of circuits into a list of optimized circuits. + + Args: + circuits (QuantumCircuit or list[QuantumCircuit]): circuits to compile + transpile_config (TranspileConfig): configuration for the transpiler + + Returns: + circuits: the optimized circuits + """ + + # ------------ + # TODO: This is a hack while we are still using the old transpiler. + initial_layout = getattr(transpile_config, 'initial_layout', None) + basis_gates = getattr(transpile_config, 'basis_gates', None) + coupling_map = getattr(transpile_config, 'coupling_map', None) + seed_mapper = getattr(transpile_config, 'seed_mapper', None) + + if initial_layout is not None and not isinstance(initial_layout, Layout): + initial_layout = Layout(initial_layout) + + pass_manager = None + backend = transpile_config.backend + new_circuits = transpiler.transpile(circuits, backend, basis_gates, coupling_map, + initial_layout, seed_mapper, pass_manager) + # --------- + + # THE IDEAL CODE HERE WILL BE. + # 1 set up the pass_manager using transpile_config options + # pass_manager = PassManager(TranspileConig) + # run the passes + # new_circuits = pass_manager.run(circuits) + return new_circuits diff --git a/qiskit/converters/circuits_to_qobj.py b/qiskit/converters/circuits_to_qobj.py --- a/qiskit/converters/circuits_to_qobj.py +++ b/qiskit/converters/circuits_to_qobj.py @@ -6,19 +6,14 @@ # the LICENSE.txt file in the root directory of this source tree. """Compile function for converting a list of circuits to the qobj""" -import uuid import warnings -import sympy -import numpy -from qiskit.circuit.quantumcircuit import QuantumCircuit -from qiskit.qobj import Qobj, QobjConfig, QobjExperiment, QobjInstruction, QobjHeader -from qiskit.qobj import QobjExperimentConfig, QobjExperimentHeader, QobjConditional -from qiskit.qobj.run_config import RunConfig -from qiskit.qobj.utils import QobjType +from qiskit.qobj import QobjHeader +from qiskit.compiler.run_config import RunConfig +from qiskit.compiler import assemble_circuits -def circuits_to_qobj(circuits, user_qobj_header=None, run_config=None, +def circuits_to_qobj(circuits, qobj_header=None, run_config=None, qobj_id=None, backend_name=None, config=None, shots=None, max_credits=None, basis_gates=None, @@ -27,10 +22,10 @@ def circuits_to_qobj(circuits, user_qobj_header=None, run_config=None, Args: circuits (list[QuantumCircuits] or QuantumCircuit): circuits to compile - user_qobj_header (QobjHeader): header to pass to the results + qobj_header (QobjHeader): header to pass to the results run_config (RunConfig): RunConfig object - qobj_id (int): identifier for the generated qobj + qobj_id (int): TODO: delete after qiskit-terra 0.8 backend_name (str): TODO: delete after qiskit-terra 0.8 config (dict): TODO: delete after qiskit-terra 0.8 shots (int): TODO: delete after qiskit-terra 0.8 @@ -43,14 +38,16 @@ def circuits_to_qobj(circuits, user_qobj_header=None, run_config=None, Returns: Qobj: the Qobj to be run on the backends """ - user_qobj_header = user_qobj_header or QobjHeader() + warnings.warn('circuits_to_qobj is deprecated and will be removed in Qiskit Terra 0.9. ' + 'Use qiskit.compiler.assemble_circuits() to serialize circuits into a qobj.', + DeprecationWarning) + + qobj_header = qobj_header or QobjHeader() run_config = run_config or RunConfig() - if isinstance(circuits, QuantumCircuit): - circuits = [circuits] if backend_name: warnings.warn('backend_name is not required anymore', DeprecationWarning) - user_qobj_header.backend_name = backend_name + qobj_header.backend_name = backend_name if config: warnings.warn('config is not used anymore. Set all configs in ' 'run_config.', DeprecationWarning) @@ -70,90 +67,9 @@ def circuits_to_qobj(circuits, user_qobj_header=None, run_config=None, if max_credits: warnings.warn('max_credits is not used anymore. Set it via run_config', DeprecationWarning) run_config.max_credits = max_credits + if qobj_id: + warnings.warn('qobj_id is not used anymore', DeprecationWarning) - userconfig = QobjConfig(**run_config.to_dict()) - experiments = [] - max_n_qubits = 0 - max_memory_slots = 0 - for circuit in circuits: - # header stuff - n_qubits = 0 - memory_slots = 0 - qubit_labels = [] - clbit_labels = [] - - qreg_sizes = [] - creg_sizes = [] - for qreg in circuit.qregs: - qreg_sizes.append([qreg.name, qreg.size]) - for j in range(qreg.size): - qubit_labels.append([qreg.name, j]) - n_qubits += qreg.size - for creg in circuit.cregs: - creg_sizes.append([creg.name, creg.size]) - for j in range(creg.size): - clbit_labels.append([creg.name, j]) - memory_slots += creg.size - - # TODO: why do we need creq_sizes and qreg_sizes in header - # TODO: we need to rethink memory_slots as they are tied to classical bit - experimentheader = QobjExperimentHeader(qubit_labels=qubit_labels, - n_qubits=n_qubits, - qreg_sizes=qreg_sizes, - clbit_labels=clbit_labels, - memory_slots=memory_slots, - creg_sizes=creg_sizes, - name=circuit.name) - # TODO: why do we need n_qubits and memory_slots in both the header and the config - experimentconfig = QobjExperimentConfig(n_qubits=n_qubits, memory_slots=memory_slots) - - instructions = [] - for opt in circuit.data: - current_instruction = QobjInstruction(name=opt.name) - if opt.qargs: - qubit_indices = [qubit_labels.index([qubit[0].name, qubit[1]]) - for qubit in opt.qargs] - current_instruction.qubits = qubit_indices - if opt.cargs: - clbit_indices = [clbit_labels.index([clbit[0].name, clbit[1]]) - for clbit in opt.cargs] - current_instruction.memory = clbit_indices - - if opt.params: - params = list(map(lambda x: x.evalf(), opt.params)) - params = [sympy.matrix2numpy(x, dtype=complex) - if isinstance(x, sympy.Matrix) else x for x in params] - if len(params) == 1 and isinstance(params[0], numpy.ndarray): - # TODO: Aer expects list of rows for unitary instruction params; - # change to matrix in Aer. - params = params[0] - current_instruction.params = params - # TODO: I really dont like this for snapshot. I also think we should change - # type to snap_type - if opt.name == "snapshot": - current_instruction.label = str(opt.params[0]) - current_instruction.type = str(opt.params[1]) - if opt.control: - mask = 0 - for clbit in clbit_labels: - if clbit[0] == opt.control[0].name: - mask |= (1 << clbit_labels.index(clbit)) - - current_instruction.conditional = QobjConditional(mask="0x%X" % mask, - type='equals', - val="0x%X" % opt.control[1]) - - instructions.append(current_instruction) - experiments.append(QobjExperiment(instructions=instructions, header=experimentheader, - config=experimentconfig)) - if n_qubits > max_n_qubits: - max_n_qubits = n_qubits - if memory_slots > max_memory_slots: - max_memory_slots = memory_slots - - userconfig.memory_slots = max_memory_slots - userconfig.n_qubits = max_n_qubits + qobj = assemble_circuits(circuits, qobj_header, run_config) - return Qobj(qobj_id=qobj_id or str(uuid.uuid4()), config=userconfig, - experiments=experiments, header=user_qobj_header, - type=QobjType.QASM.value) + return qobj diff --git a/qiskit/execute.py b/qiskit/execute.py new file mode 100644 --- /dev/null +++ b/qiskit/execute.py @@ -0,0 +1,133 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019, IBM. +# +# This source code is licensed under the Apache License, Version 2.0 found in +# the LICENSE.txt file in the root directory of this source tree. + +""" +Helper module for simplified Qiskit usage. + +This module includes + execute_circuits: compile and run a list of quantum circuits. + execute: simplified usage of either execute_circuits or execute_schedules + +In general we recommend using the SDK functions directly. However, to get something +running quickly we have provider this wrapper module. +""" + +import logging +import warnings + +from qiskit.compiler import assemble_circuits, transpile +from qiskit.compiler import RunConfig, TranspileConfig +from qiskit.qobj import QobjHeader + +logger = logging.getLogger(__name__) + + +def execute(circuits, backend, qobj_header=None, config=None, basis_gates=None, + coupling_map=None, initial_layout=None, shots=1024, max_credits=10, + seed=None, qobj_id=None, seed_mapper=None, pass_manager=None, + memory=False, **kwargs): + """Executes a set of circuits. + + Args: + circuits (QuantumCircuit or list[QuantumCircuit]): circuits to execute + backend (BaseBackend): a backend to execute the circuits on + qobj_header (QobjHeader): user input to go into the header + config (dict): dictionary of parameters (e.g. noise) used by runner + basis_gates (list[str]): list of basis gate names supported by the + target. Default: ['u1','u2','u3','cx','id'] + coupling_map (list): coupling map (perhaps custom) to target in mapping + initial_layout (list): initial layout of qubits in mapping + shots (int): number of repetitions of each circuit, for sampling + max_credits (int): maximum credits to use + seed (int): random seed for simulators + seed_mapper (int): random seed for swapper mapper + qobj_id (int): identifier for the generated qobj + pass_manager (PassManager): a pass manger for the transpiler pipeline + memory (bool): if True, per-shot measurement bitstrings are returned as well. + kwargs: extra arguments used by AER for running configurable backends. + Refer to the backend documentation for details on these arguments + + Returns: + BaseJob: returns job instance derived from BaseJob + """ + + transpile_config = TranspileConfig() + run_config = RunConfig() + + if config: + warnings.warn('config is deprecated in terra 0.8', DeprecationWarning) + if qobj_id: + warnings.warn('qobj_id is deprecated in terra 0.8', DeprecationWarning) + if basis_gates: + transpile_config.basis_gate = basis_gates + if coupling_map: + transpile_config.coupling_map = coupling_map + if initial_layout: + transpile_config.initial_layout = initial_layout + if seed_mapper: + transpile_config.seed_mapper = seed_mapper + if shots: + run_config.shots = shots + if max_credits: + run_config.max_credits = max_credits + if seed: + run_config.seed = seed + if memory: + run_config.memory = memory + if pass_manager: + warnings.warn('pass_manager in the execute function is deprecated in terra 0.8.', + DeprecationWarning) + + job = execute_circuits(circuits, backend, qobj_header=qobj_header, + run_config=run_config, + transpile_config=transpile_config, **kwargs) + + return job + + +def execute_circuits(circuits, backend, qobj_header=None, + transpile_config=None, run_config=None, **kwargs): + """Executes a list of circuits. + + Args: + circuits (QuantumCircuit or list[QuantumCircuit]): circuits to execute + backend (BaseBackend): a backend to execute the circuits on + qobj_header (QobjHeader): User input to go in the header + transpile_config (TranspileConfig): Configurations for the transpiler + run_config (RunConfig): Run Configuration + kwargs: extra arguments used by AER for running configurable backends. + Refer to the backend documentation for details on these arguments + + Returns: + BaseJob: returns job instance derived from BaseJob + """ + + # TODO: a hack, remove when backend is not needed in transpile + # ------ + transpile_config = transpile_config or TranspileConfig() + transpile_config.backend = backend + # ------ + + # filling in the header with the backend name the qobj was run on + qobj_header = qobj_header or QobjHeader() + qobj_header.backend_name = backend.name() + + # default values + if not run_config: + # TODO remove max_credits from the default when it is not + # required by by the backend. + run_config = RunConfig(shots=1024, max_credits=10, memory=False) + + # transpiling the circuits using the transpiler_config + new_circuits = transpile(circuits, transpile_config=transpile_config) + + # assembling the circuits into a qobj to be run on the backend + qobj = assemble_circuits(new_circuits, qobj_header=qobj_header, + run_config=run_config) + + # executing the circuits on the backend and returning the job + return backend.run(qobj, **kwargs) diff --git a/qiskit/qobj/__init__.py b/qiskit/qobj/__init__.py --- a/qiskit/qobj/__init__.py +++ b/qiskit/qobj/__init__.py @@ -11,6 +11,5 @@ from .models import (QobjConfig, QobjExperiment, QobjInstruction, QobjHeader, QobjExperimentHeader, QobjConditional, QobjExperimentConfig) from .exceptions import QobjValidationError -from .run_config import RunConfig from ._validation import validate_qobj_against_schema diff --git a/qiskit/qobj/qobj.py b/qiskit/qobj/qobj.py --- a/qiskit/qobj/qobj.py +++ b/qiskit/qobj/qobj.py @@ -20,9 +20,9 @@ """Current version of the Qobj schema. Qobj schema versions: -* 1.1.0: Qiskit 0.8 -* 1.0.0: Qiskit 0.6 -* 0.0.1: Qiskit 0.5.x format (pre-schemas). +* 1.1.0: Qiskit Terra 0.8 +* 1.0.0: Qiskit Terra 0.6 +* 0.0.1: Qiskit Terra 0.5.x format (pre-schemas). """ diff --git a/qiskit/tools/__init__.py b/qiskit/tools/__init__.py --- a/qiskit/tools/__init__.py +++ b/qiskit/tools/__init__.py @@ -16,4 +16,4 @@ """ from .parallel import parallel_map -from .compiler import (compile, execute) +from .compiler import compile diff --git a/qiskit/tools/compiler.py b/qiskit/tools/compiler.py --- a/qiskit/tools/compiler.py +++ b/qiskit/tools/compiler.py @@ -5,17 +5,14 @@ # This source code is licensed under the Apache License, Version 2.0 found in # the LICENSE.txt file in the root directory of this source tree. -"""Helper module for simplified Qiskit usage.""" +"""Helper module for simplified Qiskit usage. THIS WILL BE REMOVED IN AFTER 0.8.""" import warnings import logging +from qiskit.compiler import assemble_circuits, RunConfig from qiskit import transpiler -from qiskit.converters import circuits_to_qobj -from qiskit.qobj import RunConfig -from qiskit.qobj import QobjHeader from qiskit.mapper import Layout - logger = logging.getLogger(__name__) @@ -48,64 +45,32 @@ def compile(circuits, backend, Raises: QiskitError: if the desired options are not supported by backend """ - if config: - warnings.warn('The `config` argument is deprecated and ' - 'does not do anything', DeprecationWarning) - - if initial_layout is not None and not isinstance(initial_layout, Layout): - initial_layout = Layout(initial_layout) - - circuits = transpiler.transpile(circuits, backend, basis_gates, coupling_map, initial_layout, - seed_mapper, pass_manager) + warnings.warn('qiskit.compile() is deprecated and will be removed in Qiskit Terra 0.9. ' + 'Please use qiskit.transpile() to transform circuits ' + 'and qiskit.assemble_circuits() to produce qobj.', + DeprecationWarning) - # step 4: Making a qobj run_config = RunConfig() - if seed: - run_config.seed = seed + if config: + warnings.warn('config is not used anymore. Set all configs in ' + 'run_config.', DeprecationWarning) if shots: run_config.shots = shots if max_credits: run_config.max_credits = max_credits + if seed: + run_config.seed = seed if memory: run_config.memory = memory - qobj = circuits_to_qobj(circuits, user_qobj_header=QobjHeader(), run_config=run_config, - qobj_id=qobj_id) - - return qobj - -def execute(circuits, backend, config=None, basis_gates=None, coupling_map=None, - initial_layout=None, shots=1024, max_credits=10, seed=None, - qobj_id=None, seed_mapper=None, pass_manager=None, - memory=False, **kwargs): - """Executes a set of circuits. - - Args: - circuits (QuantumCircuit or list[QuantumCircuit]): circuits to execute - backend (BaseBackend): a backend to execute the circuits on - config (dict): dictionary of parameters (e.g. noise) used by runner - basis_gates (list[str]): list of basis gate names supported by the - target. Default: ['u1','u2','u3','cx','id'] - coupling_map (list): coupling map (perhaps custom) to target in mapping - initial_layout (list): initial layout of qubits in mapping - shots (int): number of repetitions of each circuit, for sampling - max_credits (int): maximum credits to use - seed (int): random seed for simulators - seed_mapper (int): random seed for swapper mapper - qobj_id (int): identifier for the generated qobj - pass_manager (PassManager): a pass manger for the transpiler pipeline - memory (bool): if True, per-shot measurement bitstrings are returned as well. - kwargs: extra arguments used by AER for running configurable backends. - Refer to the backend documentation for details on these arguments + if initial_layout is not None and not isinstance(initial_layout, Layout): + initial_layout = Layout(initial_layout) - Returns: - BaseJob: returns job instance derived from BaseJob - """ + new_circuits = transpiler.transpile(circuits, backend, basis_gates, coupling_map, + initial_layout, seed_mapper, pass_manager) - qobj = compile(circuits, backend, - config, basis_gates, coupling_map, initial_layout, - shots, max_credits, seed, qobj_id, seed_mapper, - pass_manager, memory) + qobj = assemble_circuits(new_circuits, qobj_header=None, run_config=run_config, + qobj_id=qobj_id) - return backend.run(qobj, **kwargs) + return qobj

Allow for setting qobj header description in execute and propagate to job   ### What is the expected enhancement? The Qobj accepts a description in the header that allows the user to label the qobj with whatever message they want, e.g. `'cool_exp_v1'`. However, this can not be set unless one first does `compile` and then manually sets the description. Because the user never sees the qobj (and perhaps never should since it is just a fancy box) when using `execute` it is impossible to save this info. In addition, if I try to grab a job by the description (see #1710), or some substring in description, to the best of my knowledge I need to load all the jobs, and then make another API call for each job to load the qobj and then search the header description. It would be nice if the qobj header description could be set in execute, and then this description gets added to the resulting job, e.g. `job.description` so that I can search jobs by this value without needing to load qobjs.

This is good but it has to be done after the update to the compile interfaces. I will try and do it with that.

2019-02-24T16:30:32Z

[]

apache/airflow

apache__airflow-33043

d989e9dba5899e87780df9a8c5994ed1e3f8a776

diff --git a/airflow/auth/managers/fab/views/roles_list.py b/airflow/auth/managers/fab/views/roles_list.py new file mode 100644 --- /dev/null +++ b/airflow/auth/managers/fab/views/roles_list.py @@ -0,0 +1,42 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +from __future__ import annotations + +from flask_appbuilder.security.views import RoleModelView + +from airflow.security import permissions + + +class CustomRoleModelView(RoleModelView): + """Customize permission names for FAB's builtin RoleModelView.""" + + class_permission_name = permissions.RESOURCE_ROLE + method_permission_name = { + "delete": "delete", + "download": "read", + "show": "read", + "list": "read", + "edit": "edit", + "add": "create", + "copy_role": "create", + } + base_permissions = [ + permissions.ACTION_CAN_CREATE, + permissions.ACTION_CAN_READ, + permissions.ACTION_CAN_EDIT, + permissions.ACTION_CAN_DELETE, + ] diff --git a/airflow/www/fab_security/manager.py b/airflow/www/fab_security/manager.py --- a/airflow/www/fab_security/manager.py +++ b/airflow/www/fab_security/manager.py @@ -258,7 +258,7 @@ def create_builtin_roles(self): """Returns FAB builtin roles.""" return self.appbuilder.get_app.config.get("FAB_ROLES", {}) - def get_roles_from_keys(self, role_keys: list[str]) -> set[RoleModelView]: + def get_roles_from_keys(self, role_keys: list[str]) -> set[Role]: """ Construct a list of FAB role objects, from a list of keys. @@ -267,7 +267,7 @@ def get_roles_from_keys(self, role_keys: list[str]) -> set[RoleModelView]: - we use AUTH_ROLES_MAPPING to map from keys, to FAB role names :param role_keys: the list of FAB role keys - :return: a list of RoleModelView + :return: a list of Role """ _roles = set() _role_keys = set(role_keys) diff --git a/airflow/www/fab_security/views.py b/airflow/www/fab_security/views.py --- a/airflow/www/fab_security/views.py +++ b/airflow/www/fab_security/views.py @@ -20,7 +20,6 @@ from flask_appbuilder.security.views import ( PermissionModelView, PermissionViewModelView, - RoleModelView, ViewMenuModelView, ) from flask_babel import lazy_gettext @@ -72,27 +71,6 @@ class PermissionPairModelView(PermissionViewModelView): list_columns = ["action", "resource"] -class CustomRoleModelView(RoleModelView): - """Customize permission names for FAB's builtin RoleModelView.""" - - class_permission_name = permissions.RESOURCE_ROLE - method_permission_name = { - "delete": "delete", - "download": "read", - "show": "read", - "list": "read", - "edit": "edit", - "add": "create", - "copy_role": "create", - } - base_permissions = [ - permissions.ACTION_CAN_CREATE, - permissions.ACTION_CAN_READ, - permissions.ACTION_CAN_EDIT, - permissions.ACTION_CAN_DELETE, - ] - - class ResourceModelView(ViewMenuModelView): """Customize permission names for FAB's builtin ViewMenuModelView.""" diff --git a/airflow/www/security.py b/airflow/www/security.py --- a/airflow/www/security.py +++ b/airflow/www/security.py @@ -25,6 +25,7 @@ from sqlalchemy.orm import Session, joinedload from airflow.auth.managers.fab.models import Permission, Resource, Role, User +from airflow.auth.managers.fab.views.roles_list import CustomRoleModelView from airflow.auth.managers.fab.views.user import ( CustomUserDBModelView, CustomUserLDAPModelView, @@ -47,7 +48,6 @@ from airflow.www.fab_security.sqla.manager import SecurityManager from airflow.www.fab_security.views import ( ActionModelView, - CustomRoleModelView, PermissionPairModelView, ResourceModelView, )

AIP-56 - FAB AM - Role views Move role related views to FAB Auth manager: - List roles - Edit role - Create role - View role

2023-08-02T16:35:14Z

[]

celery/celery

celery__celery-6629

c7f2f141627de69645d1885b000b12def97152ec

diff --git a/celery/backends/base.py b/celery/backends/base.py --- a/celery/backends/base.py +++ b/celery/backends/base.py @@ -853,7 +853,11 @@ def _store_result(self, task_id, result, state, if current_meta['status'] == states.SUCCESS: return result - self._set_with_state(self.get_key_for_task(task_id), self.encode(meta), state) + try: + self._set_with_state(self.get_key_for_task(task_id), self.encode(meta), state) + except BackendStoreError as ex: + raise BackendStoreError(str(ex), state=state, task_id=task_id) from ex + return result def _save_group(self, group_id, result): diff --git a/celery/backends/redis.py b/celery/backends/redis.py --- a/celery/backends/redis.py +++ b/celery/backends/redis.py @@ -12,7 +12,7 @@ from celery import states from celery._state import task_join_will_block from celery.canvas import maybe_signature -from celery.exceptions import ChordError, ImproperlyConfigured +from celery.exceptions import BackendStoreError, ChordError, ImproperlyConfigured from celery.result import GroupResult, allow_join_result from celery.utils.functional import dictfilter from celery.utils.log import get_logger @@ -192,6 +192,10 @@ class RedisBackend(BaseKeyValueStoreBackend, AsyncBackendMixin): supports_autoexpire = True supports_native_join = True + #: Maximal length of string value in Redis. + #: 512 MB - https://redis.io/topics/data-types + _MAX_STR_VALUE_SIZE = 536870912 + def __init__(self, host=None, port=None, db=None, password=None, max_connections=None, url=None, connection_pool=None, **kwargs): @@ -364,6 +368,9 @@ def on_connection_error(self, max_retries, exc, intervals, retries): return tts def set(self, key, value, **retry_policy): + if len(value) > self._MAX_STR_VALUE_SIZE: + raise BackendStoreError('value too large for Redis backend') + return self.ensure(self._set, (key, value), **retry_policy) def _set(self, key, value): diff --git a/celery/exceptions.py b/celery/exceptions.py --- a/celery/exceptions.py +++ b/celery/exceptions.py @@ -288,7 +288,7 @@ def __repr__(self): class BackendStoreError(BackendError): - """An issue writing from the backend.""" + """An issue writing to the backend.""" def __init__(self, *args, **kwargs): self.state = kwargs.get('state', "")

Workers retry Redis connection when task result is larger than 512MB - retry is useless as it never fits to Redis  # Checklist  - [x] I have verified that the issue exists against the `master` branch of Celery. - [ ] This has already been asked to the [discussion group](https://groups.google.com/forum/#!forum/celery-users) first. - [x] I have read the relevant section in the [contribution guide](http://docs.celeryproject.org/en/latest/contributing.html#other-bugs) on reporting bugs. - [x] I have checked the [issues list](https://github.com/celery/celery/issues?q=is%3Aissue+label%3A%22Issue+Type%3A+Bug+Report%22+-label%3A%22Category%3A+Documentation%22) for similar or identical bug reports. - [x] I have checked the [pull requests list](https://github.com/celery/celery/pulls?q=is%3Apr+label%3A%22PR+Type%3A+Bugfix%22+-label%3A%22Category%3A+Documentation%22) for existing proposed fixes. - [x] I have checked the [commit log](https://github.com/celery/celery/commits/master) to find out if the bug was already fixed in the master branch. - [x] I have included all related issues and possible duplicate issues in this issue (If there are none, check this box anyway). ## Mandatory Debugging Information - [x] I have included the output of ``celery -A proj report`` in the issue. (if you are not able to do this, then at least specify the Celery version affected). - [x] I have verified that the issue exists against the `master` branch of Celery. - [x] I have included the contents of ``pip freeze`` in the issue. - [x] I have included all the versions of all the external dependencies required to reproduce this bug. ## Optional Debugging Information  - [ ] I have tried reproducing the issue on more than one Python version and/or implementation. - [ ] I have tried reproducing the issue on more than one message broker and/or result backend. - [ ] I have tried reproducing the issue on more than one version of the message broker and/or result backend. - [ ] I have tried reproducing the issue on more than one operating system. - [ ] I have tried reproducing the issue on more than one workers pool. - [ ] I have tried reproducing the issue with autoscaling, retries, ETA/Countdown & rate limits disabled. - [ ] I have tried reproducing the issue after downgrading and/or upgrading Celery and its dependencies. ## Related Issues and Possible Duplicates  #### Related Issues - None #### Possible Duplicates - None ## Environment & Settings  **Celery version**: 5.0.4 (singularity)  <details> <summary><code>celery report</code> Output:</summary> ``` software -> celery:5.0.4 (singularity) kombu:5.0.2 py:3.9.0 billiard:3.6.3.0 py-amqp:5.0.2 platform -> system:Linux arch:64bit, ELF kernel version:5.9.12-arch1-1 imp:CPython loader -> celery.loaders.app.AppLoader settings -> transport:amqp results:redis://:**@****:6379/10 broker_url: 'amqp://**:********@*****:5672/**' result_backend: 'redis://:********@*****:6379/10' deprecated_settings: None ``` </details> # Steps to Reproduce ## Required Dependencies  * **Minimal Python Version**: N/A or Unknown * **Minimal Celery Version**: N/A or Unknown * **Minimal Kombu Version**: N/A or Unknown * **Minimal Broker Version**: N/A or Unknown * **Minimal Result Backend Version**: N/A or Unknown * **Minimal OS and/or Kernel Version**: N/A or Unknown * **Minimal Broker Client Version**: N/A or Unknown * **Minimal Result Backend Client Version**: N/A or Unknown ### Python Packages  <details> <summary><code>pip freeze</code> Output:</summary> ``` amqp==5.0.2 billiard==3.6.3.0 celery @ git+https://github.com/celery/celery.git@420e3931a63538bd225ef57916deccf53cbcb57a // == master, tried also 5.0.4 click==7.1.2 click-didyoumean==0.0.3 click-plugins==1.1.1 click-repl==0.1.6 kombu==5.0.2 prompt-toolkit==3.0.8 pytz==2020.4 redis==3.5.3 six==1.15.0 vine==5.0.0 wcwidth==0.2.5 ``` </details> ### Other Dependencies  <details> N/A </details> ## Minimally Reproducible Test Case  <details> ```python #!/usr/bin/env python3 from celery import Celery app = Celery( 'tasks', broker='amqp://user:***@**:5672/**', backend='redis://:**@**:6379/1', ) @app.task(ignore_result=False) def test(*args, **kwargs): return 'x' * 536870911 # 512MB ``` </details> # Expected Behavior  I'd except an exception or error. There is no point in retrying storing result to Redis when it simply never fits there. String limit is 512 MB. I could check size of the data I'm returning in task. However, Celery adds additional metadata to my result so I do not know to what size should I limit my result. # Actual Behavior  ``` [2020-12-09 08:38:52,786: ERROR/ForkPoolWorker-8] Connection to Redis lost: Retry (0/20) now. [2020-12-09 08:38:53,328: ERROR/ForkPoolWorker-8] Connection to Redis lost: Retry (1/20) in 1.00 second. [2020-12-09 08:38:54,940: ERROR/ForkPoolWorker-8] Connection to Redis lost: Retry (2/20) in 1.00 second. [2020-12-09 08:38:56,547: ERROR/ForkPoolWorker-8] Connection to Redis lost: Retry (3/20) in 1.00 second. .... and so on ``` Redis fails with the following error when you try to set string larger than 512 MB: ``` raise ConnectionError("Error %s while writing to socket. %s." % redis.exceptions.ConnectionError: Error 32 while writing to socket. Broken pipe. ``` Tried via another script with direct Redis connection. This says nothing about input size therefore I think value size should be checked before sending data to Redis.

Pull requests are welcome.

2021-02-07T09:06:11Z

[]

pandas-dev/pandas

pandas-dev__pandas-39217

b31c069ded78a67e0a6f54fc776a7920fd09beca

diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py --- a/pandas/io/json/_json.py +++ b/pandas/io/json/_json.py @@ -38,7 +38,7 @@ ) from pandas.io.json._normalize import convert_to_line_delimits from pandas.io.json._table_schema import build_table_schema, parse_table_schema -from pandas.io.parsers import validate_integer +from pandas.io.parsers.readers import validate_integer loads = json.loads dumps = json.dumps diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py deleted file mode 100644 --- a/pandas/io/parsers.py +++ /dev/null @@ -1,3997 +0,0 @@ -""" -Module contains tools for processing files into DataFrames or other objects -""" - -from collections import abc, defaultdict -import csv -import datetime -from io import StringIO -import itertools -import re -import sys -from textwrap import fill -from typing import ( - Any, - Dict, - Iterable, - Iterator, - List, - Optional, - Sequence, - Set, - Type, - cast, -) -import warnings - -import numpy as np - -import pandas._libs.lib as lib -import pandas._libs.ops as libops -import pandas._libs.parsers as parsers -from pandas._libs.parsers import STR_NA_VALUES -from pandas._libs.tslibs import parsing -from pandas._typing import DtypeArg, FilePathOrBuffer, StorageOptions, Union -from pandas.errors import ( - AbstractMethodError, - EmptyDataError, - ParserError, - ParserWarning, -) -from pandas.util._decorators import Appender - -from pandas.core.dtypes.cast import astype_nansafe -from pandas.core.dtypes.common import ( - ensure_object, - ensure_str, - is_bool_dtype, - is_categorical_dtype, - is_dict_like, - is_dtype_equal, - is_extension_array_dtype, - is_file_like, - is_float, - is_integer, - is_integer_dtype, - is_list_like, - is_object_dtype, - is_scalar, - is_string_dtype, - pandas_dtype, -) -from pandas.core.dtypes.dtypes import CategoricalDtype -from pandas.core.dtypes.missing import isna - -from pandas.core import algorithms, generic -from pandas.core.arrays import Categorical -from pandas.core.frame import DataFrame -from pandas.core.indexes.api import ( - Index, - MultiIndex, - RangeIndex, - ensure_index_from_sequences, -) -from pandas.core.series import Series -from pandas.core.tools import datetimes as tools - -from pandas.io.common import IOHandles, get_handle, validate_header_arg -from pandas.io.date_converters import generic_parser - -# BOM character (byte order mark) -# This exists at the beginning of a file to indicate endianness -# of a file (stream). Unfortunately, this marker screws up parsing, -# so we need to remove it if we see it. -_BOM = "\ufeff" - -_doc_read_csv_and_table = ( - r""" -{summary} - -Also supports optionally iterating or breaking of the file -into chunks. - -Additional help can be found in the online docs for -`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. - -Parameters ----------- -filepath_or_buffer : str, path object or file-like object - Any valid string path is acceptable. The string could be a URL. Valid - URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is - expected. A local file could be: file://localhost/path/to/table.csv. - - If you want to pass in a path object, pandas accepts any ``os.PathLike``. - - By file-like object, we refer to objects with a ``read()`` method, such as - a file handle (e.g. via builtin ``open`` function) or ``StringIO``. -sep : str, default {_default_sep} - Delimiter to use. If sep is None, the C engine cannot automatically detect - the separator, but the Python parsing engine can, meaning the latter will - be used and automatically detect the separator by Python's builtin sniffer - tool, ``csv.Sniffer``. In addition, separators longer than 1 character and - different from ``'\s+'`` will be interpreted as regular expressions and - will also force the use of the Python parsing engine. Note that regex - delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``. -delimiter : str, default ``None`` - Alias for sep. -header : int, list of int, default 'infer' - Row number(s) to use as the column names, and the start of the - data. Default behavior is to infer the column names: if no names - are passed the behavior is identical to ``header=0`` and column - names are inferred from the first line of the file, if column - names are passed explicitly then the behavior is identical to - ``header=None``. Explicitly pass ``header=0`` to be able to - replace existing names. The header can be a list of integers that - specify row locations for a multi-index on the columns - e.g. [0,1,3]. Intervening rows that are not specified will be - skipped (e.g. 2 in this example is skipped). Note that this - parameter ignores commented lines and empty lines if - ``skip_blank_lines=True``, so ``header=0`` denotes the first line of - data rather than the first line of the file. -names : array-like, optional - List of column names to use. If the file contains a header row, - then you should explicitly pass ``header=0`` to override the column names. - Duplicates in this list are not allowed. -index_col : int, str, sequence of int / str, or False, default ``None`` - Column(s) to use as the row labels of the ``DataFrame``, either given as - string name or column index. If a sequence of int / str is given, a - MultiIndex is used. - - Note: ``index_col=False`` can be used to force pandas to *not* use the first - column as the index, e.g. when you have a malformed file with delimiters at - the end of each line. -usecols : list-like or callable, optional - Return a subset of the columns. If list-like, all elements must either - be positional (i.e. integer indices into the document columns) or strings - that correspond to column names provided either by the user in `names` or - inferred from the document header row(s). For example, a valid list-like - `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``. - Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``. - To instantiate a DataFrame from ``data`` with element order preserved use - ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns - in ``['foo', 'bar']`` order or - ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]`` - for ``['bar', 'foo']`` order. - - If callable, the callable function will be evaluated against the column - names, returning names where the callable function evaluates to True. An - example of a valid callable argument would be ``lambda x: x.upper() in - ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster - parsing time and lower memory usage. -squeeze : bool, default False - If the parsed data only contains one column then return a Series. -prefix : str, optional - Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... -mangle_dupe_cols : bool, default True - Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than - 'X'...'X'. Passing in False will cause data to be overwritten if there - are duplicate names in the columns. -dtype : Type name or dict of column -> type, optional - Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32, - 'c': 'Int64'}} - Use `str` or `object` together with suitable `na_values` settings - to preserve and not interpret dtype. - If converters are specified, they will be applied INSTEAD - of dtype conversion. -engine : {{'c', 'python'}}, optional - Parser engine to use. The C engine is faster while the python engine is - currently more feature-complete. -converters : dict, optional - Dict of functions for converting values in certain columns. Keys can either - be integers or column labels. -true_values : list, optional - Values to consider as True. -false_values : list, optional - Values to consider as False. -skipinitialspace : bool, default False - Skip spaces after delimiter. -skiprows : list-like, int or callable, optional - Line numbers to skip (0-indexed) or number of lines to skip (int) - at the start of the file. - - If callable, the callable function will be evaluated against the row - indices, returning True if the row should be skipped and False otherwise. - An example of a valid callable argument would be ``lambda x: x in [0, 2]``. -skipfooter : int, default 0 - Number of lines at bottom of file to skip (Unsupported with engine='c'). -nrows : int, optional - Number of rows of file to read. Useful for reading pieces of large files. -na_values : scalar, str, list-like, or dict, optional - Additional strings to recognize as NA/NaN. If dict passed, specific - per-column NA values. By default the following values are interpreted as - NaN: '""" - + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ") - + """'. -keep_default_na : bool, default True - Whether or not to include the default NaN values when parsing the data. - Depending on whether `na_values` is passed in, the behavior is as follows: - - * If `keep_default_na` is True, and `na_values` are specified, `na_values` - is appended to the default NaN values used for parsing. - * If `keep_default_na` is True, and `na_values` are not specified, only - the default NaN values are used for parsing. - * If `keep_default_na` is False, and `na_values` are specified, only - the NaN values specified `na_values` are used for parsing. - * If `keep_default_na` is False, and `na_values` are not specified, no - strings will be parsed as NaN. - - Note that if `na_filter` is passed in as False, the `keep_default_na` and - `na_values` parameters will be ignored. -na_filter : bool, default True - Detect missing value markers (empty strings and the value of na_values). In - data without any NAs, passing na_filter=False can improve the performance - of reading a large file. -verbose : bool, default False - Indicate number of NA values placed in non-numeric columns. -skip_blank_lines : bool, default True - If True, skip over blank lines rather than interpreting as NaN values. -parse_dates : bool or list of int or names or list of lists or dict, \ -default False - The behavior is as follows: - - * boolean. If True -> try parsing the index. - * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 - each as a separate date column. - * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as - a single date column. - * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call - result 'foo' - - If a column or index cannot be represented as an array of datetimes, - say because of an unparsable value or a mixture of timezones, the column - or index will be returned unaltered as an object data type. For - non-standard datetime parsing, use ``pd.to_datetime`` after - ``pd.read_csv``. To parse an index or column with a mixture of timezones, - specify ``date_parser`` to be a partially-applied - :func:`pandas.to_datetime` with ``utc=True``. See - :ref:`io.csv.mixed_timezones` for more. - - Note: A fast-path exists for iso8601-formatted dates. -infer_datetime_format : bool, default False - If True and `parse_dates` is enabled, pandas will attempt to infer the - format of the datetime strings in the columns, and if it can be inferred, - switch to a faster method of parsing them. In some cases this can increase - the parsing speed by 5-10x. -keep_date_col : bool, default False - If True and `parse_dates` specifies combining multiple columns then - keep the original columns. -date_parser : function, optional - Function to use for converting a sequence of string columns to an array of - datetime instances. The default uses ``dateutil.parser.parser`` to do the - conversion. Pandas will try to call `date_parser` in three different ways, - advancing to the next if an exception occurs: 1) Pass one or more arrays - (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the - string values from the columns defined by `parse_dates` into a single array - and pass that; and 3) call `date_parser` once for each row using one or - more strings (corresponding to the columns defined by `parse_dates`) as - arguments. -dayfirst : bool, default False - DD/MM format dates, international and European format. -cache_dates : bool, default True - If True, use a cache of unique, converted dates to apply the datetime - conversion. May produce significant speed-up when parsing duplicate - date strings, especially ones with timezone offsets. - - .. versionadded:: 0.25.0 -iterator : bool, default False - Return TextFileReader object for iteration or getting chunks with - ``get_chunk()``. - - .. versionchanged:: 1.2 - - ``TextFileReader`` is a context manager. -chunksize : int, optional - Return TextFileReader object for iteration. - See the `IO Tools docs - <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ - for more information on ``iterator`` and ``chunksize``. - - .. versionchanged:: 1.2 - - ``TextFileReader`` is a context manager. -compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' - For on-the-fly decompression of on-disk data. If 'infer' and - `filepath_or_buffer` is path-like, then detect compression from the - following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no - decompression). If using 'zip', the ZIP file must contain only one data - file to be read in. Set to None for no decompression. -thousands : str, optional - Thousands separator. -decimal : str, default '.' - Character to recognize as decimal point (e.g. use ',' for European data). -lineterminator : str (length 1), optional - Character to break file into lines. Only valid with C parser. -quotechar : str (length 1), optional - The character used to denote the start and end of a quoted item. Quoted - items can include the delimiter and it will be ignored. -quoting : int or csv.QUOTE_* instance, default 0 - Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of - QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). -doublequote : bool, default ``True`` - When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate - whether or not to interpret two consecutive quotechar elements INSIDE a - field as a single ``quotechar`` element. -escapechar : str (length 1), optional - One-character string used to escape other characters. -comment : str, optional - Indicates remainder of line should not be parsed. If found at the beginning - of a line, the line will be ignored altogether. This parameter must be a - single character. Like empty lines (as long as ``skip_blank_lines=True``), - fully commented lines are ignored by the parameter `header` but not by - `skiprows`. For example, if ``comment='#'``, parsing - ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being - treated as the header. -encoding : str, optional - Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python - standard encodings - <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ . -dialect : str or csv.Dialect, optional - If provided, this parameter will override values (default or not) for the - following parameters: `delimiter`, `doublequote`, `escapechar`, - `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to - override values, a ParserWarning will be issued. See csv.Dialect - documentation for more details. -error_bad_lines : bool, default True - Lines with too many fields (e.g. a csv line with too many commas) will by - default cause an exception to be raised, and no DataFrame will be returned. - If False, then these "bad lines" will dropped from the DataFrame that is - returned. -warn_bad_lines : bool, default True - If error_bad_lines is False, and warn_bad_lines is True, a warning for each - "bad line" will be output. -delim_whitespace : bool, default False - Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be - used as the sep. Equivalent to setting ``sep='\\s+'``. If this option - is set to True, nothing should be passed in for the ``delimiter`` - parameter. -low_memory : bool, default True - Internally process the file in chunks, resulting in lower memory use - while parsing, but possibly mixed type inference. To ensure no mixed - types either set False, or specify the type with the `dtype` parameter. - Note that the entire file is read into a single DataFrame regardless, - use the `chunksize` or `iterator` parameter to return the data in chunks. - (Only valid with C parser). -memory_map : bool, default False - If a filepath is provided for `filepath_or_buffer`, map the file object - directly onto memory and access the data directly from there. Using this - option can improve performance because there is no longer any I/O overhead. -float_precision : str, optional - Specifies which converter the C engine should use for floating-point - values. The options are ``None`` or 'high' for the ordinary converter, - 'legacy' for the original lower precision pandas converter, and - 'round_trip' for the round-trip converter. - - .. versionchanged:: 1.2 - -{storage_options} - - .. versionadded:: 1.2 - -Returns -------- -DataFrame or TextParser - A comma-separated values (csv) file is returned as two-dimensional - data structure with labeled axes. - -See Also --------- -DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. -read_csv : Read a comma-separated values (csv) file into DataFrame. -read_fwf : Read a table of fixed-width formatted lines into DataFrame. - -Examples --------- ->>> pd.{func_name}('data.csv') # doctest: +SKIP -""" -) - - -def validate_integer(name, val, min_val=0): - """ - Checks whether the 'name' parameter for parsing is either - an integer OR float that can SAFELY be cast to an integer - without losing accuracy. Raises a ValueError if that is - not the case. - - Parameters - ---------- - name : string - Parameter name (used for error reporting) - val : int or float - The value to check - min_val : int - Minimum allowed value (val < min_val will result in a ValueError) - """ - msg = f"'{name:s}' must be an integer >={min_val:d}" - - if val is not None: - if is_float(val): - if int(val) != val: - raise ValueError(msg) - val = int(val) - elif not (is_integer(val) and val >= min_val): - raise ValueError(msg) - - return val - - -def _validate_names(names): - """ - Raise ValueError if the `names` parameter contains duplicates or has an - invalid data type. - - Parameters - ---------- - names : array-like or None - An array containing a list of the names used for the output DataFrame. - - Raises - ------ - ValueError - If names are not unique or are not ordered (e.g. set). - """ - if names is not None: - if len(names) != len(set(names)): - raise ValueError("Duplicate names are not allowed.") - if not ( - is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView) - ): - raise ValueError("Names should be an ordered collection.") - - -def _read(filepath_or_buffer: FilePathOrBuffer, kwds): - """Generic reader of line files.""" - if kwds.get("date_parser", None) is not None: - if isinstance(kwds["parse_dates"], bool): - kwds["parse_dates"] = True - - # Extract some of the arguments (pass chunksize on). - iterator = kwds.get("iterator", False) - chunksize = validate_integer("chunksize", kwds.get("chunksize", None), 1) - nrows = kwds.get("nrows", None) - - # Check for duplicates in names. - _validate_names(kwds.get("names", None)) - - # Create the parser. - parser = TextFileReader(filepath_or_buffer, **kwds) - - if chunksize or iterator: - return parser - - with parser: - return parser.read(nrows) - - -_parser_defaults = { - "delimiter": None, - "escapechar": None, - "quotechar": '"', - "quoting": csv.QUOTE_MINIMAL, - "doublequote": True, - "skipinitialspace": False, - "lineterminator": None, - "header": "infer", - "index_col": None, - "names": None, - "prefix": None, - "skiprows": None, - "skipfooter": 0, - "nrows": None, - "na_values": None, - "keep_default_na": True, - "true_values": None, - "false_values": None, - "converters": None, - "dtype": None, - "cache_dates": True, - "thousands": None, - "comment": None, - "decimal": ".", - # 'engine': 'c', - "parse_dates": False, - "keep_date_col": False, - "dayfirst": False, - "date_parser": None, - "usecols": None, - # 'iterator': False, - "chunksize": None, - "verbose": False, - "encoding": None, - "squeeze": False, - "compression": None, - "mangle_dupe_cols": True, - "infer_datetime_format": False, - "skip_blank_lines": True, -} - - -_c_parser_defaults = { - "delim_whitespace": False, - "na_filter": True, - "low_memory": True, - "memory_map": False, - "error_bad_lines": True, - "warn_bad_lines": True, - "float_precision": None, -} - -_fwf_defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None} - -_c_unsupported = {"skipfooter"} -_python_unsupported = {"low_memory", "float_precision"} - -_deprecated_defaults: Dict[str, Any] = {} -_deprecated_args: Set[str] = set() - - -@Appender( - _doc_read_csv_and_table.format( - func_name="read_csv", - summary="Read a comma-separated values (csv) file into DataFrame.", - _default_sep="','", - storage_options=generic._shared_docs["storage_options"], - ) -) -def read_csv( - filepath_or_buffer: FilePathOrBuffer, - sep=lib.no_default, - delimiter=None, - # Column and Index Locations and Names - header="infer", - names=None, - index_col=None, - usecols=None, - squeeze=False, - prefix=None, - mangle_dupe_cols=True, - # General Parsing Configuration - dtype: Optional[DtypeArg] = None, - engine=None, - converters=None, - true_values=None, - false_values=None, - skipinitialspace=False, - skiprows=None, - skipfooter=0, - nrows=None, - # NA and Missing Data Handling - na_values=None, - keep_default_na=True, - na_filter=True, - verbose=False, - skip_blank_lines=True, - # Datetime Handling - parse_dates=False, - infer_datetime_format=False, - keep_date_col=False, - date_parser=None, - dayfirst=False, - cache_dates=True, - # Iteration - iterator=False, - chunksize=None, - # Quoting, Compression, and File Format - compression="infer", - thousands=None, - decimal: str = ".", - lineterminator=None, - quotechar='"', - quoting=csv.QUOTE_MINIMAL, - doublequote=True, - escapechar=None, - comment=None, - encoding=None, - dialect=None, - # Error Handling - error_bad_lines=True, - warn_bad_lines=True, - # Internal - delim_whitespace=False, - low_memory=_c_parser_defaults["low_memory"], - memory_map=False, - float_precision=None, - storage_options: StorageOptions = None, -): - kwds = locals() - del kwds["filepath_or_buffer"] - del kwds["sep"] - - kwds_defaults = _refine_defaults_read( - dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": ","} - ) - kwds.update(kwds_defaults) - - return _read(filepath_or_buffer, kwds) - - -@Appender( - _doc_read_csv_and_table.format( - func_name="read_table", - summary="Read general delimited file into DataFrame.", - _default_sep=r"'\\t' (tab-stop)", - storage_options=generic._shared_docs["storage_options"], - ) -) -def read_table( - filepath_or_buffer: FilePathOrBuffer, - sep=lib.no_default, - delimiter=None, - # Column and Index Locations and Names - header="infer", - names=None, - index_col=None, - usecols=None, - squeeze=False, - prefix=None, - mangle_dupe_cols=True, - # General Parsing Configuration - dtype: Optional[DtypeArg] = None, - engine=None, - converters=None, - true_values=None, - false_values=None, - skipinitialspace=False, - skiprows=None, - skipfooter=0, - nrows=None, - # NA and Missing Data Handling - na_values=None, - keep_default_na=True, - na_filter=True, - verbose=False, - skip_blank_lines=True, - # Datetime Handling - parse_dates=False, - infer_datetime_format=False, - keep_date_col=False, - date_parser=None, - dayfirst=False, - cache_dates=True, - # Iteration - iterator=False, - chunksize=None, - # Quoting, Compression, and File Format - compression="infer", - thousands=None, - decimal: str = ".", - lineterminator=None, - quotechar='"', - quoting=csv.QUOTE_MINIMAL, - doublequote=True, - escapechar=None, - comment=None, - encoding=None, - dialect=None, - # Error Handling - error_bad_lines=True, - warn_bad_lines=True, - # Internal - delim_whitespace=False, - low_memory=_c_parser_defaults["low_memory"], - memory_map=False, - float_precision=None, -): - kwds = locals() - del kwds["filepath_or_buffer"] - del kwds["sep"] - - kwds_defaults = _refine_defaults_read( - dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": "\t"} - ) - kwds.update(kwds_defaults) - - return _read(filepath_or_buffer, kwds) - - -def read_fwf( - filepath_or_buffer: FilePathOrBuffer, - colspecs="infer", - widths=None, - infer_nrows=100, - **kwds, -): - r""" - Read a table of fixed-width formatted lines into DataFrame. - - Also supports optionally iterating or breaking of the file - into chunks. - - Additional help can be found in the `online docs for IO Tools - <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. - - Parameters - ---------- - filepath_or_buffer : str, path object or file-like object - Any valid string path is acceptable. The string could be a URL. Valid - URL schemes include http, ftp, s3, and file. For file URLs, a host is - expected. A local file could be: - ``file://localhost/path/to/table.csv``. - - If you want to pass in a path object, pandas accepts any - ``os.PathLike``. - - By file-like object, we refer to objects with a ``read()`` method, - such as a file handle (e.g. via builtin ``open`` function) - or ``StringIO``. - colspecs : list of tuple (int, int) or 'infer'. optional - A list of tuples giving the extents of the fixed-width - fields of each line as half-open intervals (i.e., [from, to[ ). - String value 'infer' can be used to instruct the parser to try - detecting the column specifications from the first 100 rows of - the data which are not being skipped via skiprows (default='infer'). - widths : list of int, optional - A list of field widths which can be used instead of 'colspecs' if - the intervals are contiguous. - infer_nrows : int, default 100 - The number of rows to consider when letting the parser determine the - `colspecs`. - - .. versionadded:: 0.24.0 - **kwds : optional - Optional keyword arguments can be passed to ``TextFileReader``. - - Returns - ------- - DataFrame or TextParser - A comma-separated values (csv) file is returned as two-dimensional - data structure with labeled axes. - - See Also - -------- - DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. - read_csv : Read a comma-separated values (csv) file into DataFrame. - - Examples - -------- - >>> pd.read_fwf('data.csv') # doctest: +SKIP - """ - # Check input arguments. - if colspecs is None and widths is None: - raise ValueError("Must specify either colspecs or widths") - elif colspecs not in (None, "infer") and widths is not None: - raise ValueError("You must specify only one of 'widths' and 'colspecs'") - - # Compute 'colspecs' from 'widths', if specified. - if widths is not None: - colspecs, col = [], 0 - for w in widths: - colspecs.append((col, col + w)) - col += w - - kwds["colspecs"] = colspecs - kwds["infer_nrows"] = infer_nrows - kwds["engine"] = "python-fwf" - return _read(filepath_or_buffer, kwds) - - -class TextFileReader(abc.Iterator): - """ - - Passed dialect overrides any of the related parser options - - """ - - def __init__(self, f, engine=None, **kwds): - - self.f = f - - if engine is not None: - engine_specified = True - else: - engine = "python" - engine_specified = False - self.engine = engine - self._engine_specified = kwds.get("engine_specified", engine_specified) - - _validate_skipfooter(kwds) - - dialect = _extract_dialect(kwds) - if dialect is not None: - kwds = _merge_with_dialect_properties(dialect, kwds) - - if kwds.get("header", "infer") == "infer": - kwds["header"] = 0 if kwds.get("names") is None else None - - self.orig_options = kwds - - # miscellanea - self._currow = 0 - - options = self._get_options_with_defaults(engine) - options["storage_options"] = kwds.get("storage_options", None) - - self.chunksize = options.pop("chunksize", None) - self.nrows = options.pop("nrows", None) - self.squeeze = options.pop("squeeze", False) - - self._check_file_or_buffer(f, engine) - self.options, self.engine = self._clean_options(options, engine) - - if "has_index_names" in kwds: - self.options["has_index_names"] = kwds["has_index_names"] - - self._engine = self._make_engine(self.engine) - - def close(self): - self._engine.close() - - def _get_options_with_defaults(self, engine): - kwds = self.orig_options - - options = {} - - for argname, default in _parser_defaults.items(): - value = kwds.get(argname, default) - - # see gh-12935 - if argname == "mangle_dupe_cols" and not value: - raise ValueError("Setting mangle_dupe_cols=False is not supported yet") - else: - options[argname] = value - - for argname, default in _c_parser_defaults.items(): - if argname in kwds: - value = kwds[argname] - - if engine != "c" and value != default: - if "python" in engine and argname not in _python_unsupported: - pass - elif value == _deprecated_defaults.get(argname, default): - pass - else: - raise ValueError( - f"The {repr(argname)} option is not supported with the " - f"{repr(engine)} engine" - ) - else: - value = _deprecated_defaults.get(argname, default) - options[argname] = value - - if engine == "python-fwf": - # pandas\io\parsers.py:907: error: Incompatible types in assignment - # (expression has type "object", variable has type "Union[int, str, - # None]") [assignment] - for argname, default in _fwf_defaults.items(): # type: ignore[assignment] - options[argname] = kwds.get(argname, default) - - return options - - def _check_file_or_buffer(self, f, engine): - # see gh-16530 - if is_file_like(f) and engine != "c" and not hasattr(f, "__next__"): - # The C engine doesn't need the file-like to have the "__next__" - # attribute. However, the Python engine explicitly calls - # "__next__(...)" when iterating through such an object, meaning it - # needs to have that attribute - raise ValueError( - "The 'python' engine cannot iterate through this file buffer." - ) - - def _clean_options(self, options, engine): - result = options.copy() - - fallback_reason = None - - # C engine not supported yet - if engine == "c": - if options["skipfooter"] > 0: - fallback_reason = "the 'c' engine does not support skipfooter" - engine = "python" - - sep = options["delimiter"] - delim_whitespace = options["delim_whitespace"] - - if sep is None and not delim_whitespace: - if engine == "c": - fallback_reason = ( - "the 'c' engine does not support " - "sep=None with delim_whitespace=False" - ) - engine = "python" - elif sep is not None and len(sep) > 1: - if engine == "c" and sep == r"\s+": - result["delim_whitespace"] = True - del result["delimiter"] - elif engine not in ("python", "python-fwf"): - # wait until regex engine integrated - fallback_reason = ( - "the 'c' engine does not support " - "regex separators (separators > 1 char and " - r"different from '\s+' are interpreted as regex)" - ) - engine = "python" - elif delim_whitespace: - if "python" in engine: - result["delimiter"] = r"\s+" - elif sep is not None: - encodeable = True - encoding = sys.getfilesystemencoding() or "utf-8" - try: - if len(sep.encode(encoding)) > 1: - encodeable = False - except UnicodeDecodeError: - encodeable = False - if not encodeable and engine not in ("python", "python-fwf"): - fallback_reason = ( - f"the separator encoded in {encoding} " - "is > 1 char long, and the 'c' engine " - "does not support such separators" - ) - engine = "python" - - quotechar = options["quotechar"] - if quotechar is not None and isinstance(quotechar, (str, bytes)): - if ( - len(quotechar) == 1 - and ord(quotechar) > 127 - and engine not in ("python", "python-fwf") - ): - fallback_reason = ( - "ord(quotechar) > 127, meaning the " - "quotechar is larger than one byte, " - "and the 'c' engine does not support such quotechars" - ) - engine = "python" - - if fallback_reason and self._engine_specified: - raise ValueError(fallback_reason) - - if engine == "c": - for arg in _c_unsupported: - del result[arg] - - if "python" in engine: - for arg in _python_unsupported: - if fallback_reason and result[arg] != _c_parser_defaults[arg]: - raise ValueError( - "Falling back to the 'python' engine because " - f"{fallback_reason}, but this causes {repr(arg)} to be " - "ignored as it is not supported by the 'python' engine." - ) - del result[arg] - - if fallback_reason: - warnings.warn( - ( - "Falling back to the 'python' engine because " - f"{fallback_reason}; you can avoid this warning by specifying " - "engine='python'." - ), - ParserWarning, - stacklevel=5, - ) - - index_col = options["index_col"] - names = options["names"] - converters = options["converters"] - na_values = options["na_values"] - skiprows = options["skiprows"] - - validate_header_arg(options["header"]) - - for arg in _deprecated_args: - parser_default = _c_parser_defaults[arg] - depr_default = _deprecated_defaults[arg] - if result.get(arg, depr_default) != depr_default: - msg = ( - f"The {arg} argument has been deprecated and will be " - "removed in a future version.\n\n" - ) - warnings.warn(msg, FutureWarning, stacklevel=2) - else: - result[arg] = parser_default - - if index_col is True: - raise ValueError("The value of index_col couldn't be 'True'") - if _is_index_col(index_col): - if not isinstance(index_col, (list, tuple, np.ndarray)): - index_col = [index_col] - result["index_col"] = index_col - - names = list(names) if names is not None else names - - # type conversion-related - if converters is not None: - if not isinstance(converters, dict): - raise TypeError( - "Type converters must be a dict or subclass, " - f"input was a {type(converters).__name__}" - ) - else: - converters = {} - - # Converting values to NA - keep_default_na = options["keep_default_na"] - na_values, na_fvalues = _clean_na_values(na_values, keep_default_na) - - # handle skiprows; this is internally handled by the - # c-engine, so only need for python parsers - if engine != "c": - if is_integer(skiprows): - skiprows = list(range(skiprows)) - if skiprows is None: - skiprows = set() - elif not callable(skiprows): - skiprows = set(skiprows) - - # put stuff back - result["names"] = names - result["converters"] = converters - result["na_values"] = na_values - result["na_fvalues"] = na_fvalues - result["skiprows"] = skiprows - - return result, engine - - def __next__(self): - try: - return self.get_chunk() - except StopIteration: - self.close() - raise - - def _make_engine(self, engine="c"): - mapping: Dict[str, Type[ParserBase]] = { - "c": CParserWrapper, - "python": PythonParser, - "python-fwf": FixedWidthFieldParser, - } - if engine not in mapping: - raise ValueError( - f"Unknown engine: {engine} (valid options are {mapping.keys()})" - ) - # error: Too many arguments for "ParserBase" - return mapping[engine](self.f, **self.options) # type: ignore[call-arg] - - def _failover_to_python(self): - raise AbstractMethodError(self) - - def read(self, nrows=None): - nrows = validate_integer("nrows", nrows) - index, columns, col_dict = self._engine.read(nrows) - - if index is None: - if col_dict: - # Any column is actually fine: - new_rows = len(next(iter(col_dict.values()))) - index = RangeIndex(self._currow, self._currow + new_rows) - else: - new_rows = 0 - else: - new_rows = len(index) - - df = DataFrame(col_dict, columns=columns, index=index) - - self._currow += new_rows - - if self.squeeze and len(df.columns) == 1: - return df[df.columns[0]].copy() - return df - - def get_chunk(self, size=None): - if size is None: - size = self.chunksize - if self.nrows is not None: - if self._currow >= self.nrows: - raise StopIteration - size = min(size, self.nrows - self._currow) - return self.read(nrows=size) - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, traceback): - self.close() - - -def _is_index_col(col): - return col is not None and col is not False - - -def _is_potential_multi_index( - columns, index_col: Optional[Union[bool, Sequence[int]]] = None -): - """ - Check whether or not the `columns` parameter - could be converted into a MultiIndex. - - Parameters - ---------- - columns : array-like - Object which may or may not be convertible into a MultiIndex - index_col : None, bool or list, optional - Column or columns to use as the (possibly hierarchical) index - - Returns - ------- - boolean : Whether or not columns could become a MultiIndex - """ - if index_col is None or isinstance(index_col, bool): - index_col = [] - - return ( - len(columns) - and not isinstance(columns, MultiIndex) - and all(isinstance(c, tuple) for c in columns if c not in list(index_col)) - ) - - -def _evaluate_usecols(usecols, names): - """ - Check whether or not the 'usecols' parameter - is a callable. If so, enumerates the 'names' - parameter and returns a set of indices for - each entry in 'names' that evaluates to True. - If not a callable, returns 'usecols'. - """ - if callable(usecols): - return {i for i, name in enumerate(names) if usecols(name)} - return usecols - - -def _validate_usecols_names(usecols, names): - """ - Validates that all usecols are present in a given - list of names. If not, raise a ValueError that - shows what usecols are missing. - - Parameters - ---------- - usecols : iterable of usecols - The columns to validate are present in names. - names : iterable of names - The column names to check against. - - Returns - ------- - usecols : iterable of usecols - The `usecols` parameter if the validation succeeds. - - Raises - ------ - ValueError : Columns were missing. Error message will list them. - """ - missing = [c for c in usecols if c not in names] - if len(missing) > 0: - raise ValueError( - f"Usecols do not match columns, columns expected but not found: {missing}" - ) - - return usecols - - -def _validate_skipfooter_arg(skipfooter): - """ - Validate the 'skipfooter' parameter. - - Checks whether 'skipfooter' is a non-negative integer. - Raises a ValueError if that is not the case. - - Parameters - ---------- - skipfooter : non-negative integer - The number of rows to skip at the end of the file. - - Returns - ------- - validated_skipfooter : non-negative integer - The original input if the validation succeeds. - - Raises - ------ - ValueError : 'skipfooter' was not a non-negative integer. - """ - if not is_integer(skipfooter): - raise ValueError("skipfooter must be an integer") - - if skipfooter < 0: - raise ValueError("skipfooter cannot be negative") - - return skipfooter - - -def _validate_usecols_arg(usecols): - """ - Validate the 'usecols' parameter. - - Checks whether or not the 'usecols' parameter contains all integers - (column selection by index), strings (column by name) or is a callable. - Raises a ValueError if that is not the case. - - Parameters - ---------- - usecols : list-like, callable, or None - List of columns to use when parsing or a callable that can be used - to filter a list of table columns. - - Returns - ------- - usecols_tuple : tuple - A tuple of (verified_usecols, usecols_dtype). - - 'verified_usecols' is either a set if an array-like is passed in or - 'usecols' if a callable or None is passed in. - - 'usecols_dtype` is the inferred dtype of 'usecols' if an array-like - is passed in or None if a callable or None is passed in. - """ - msg = ( - "'usecols' must either be list-like of all strings, all unicode, " - "all integers or a callable." - ) - if usecols is not None: - if callable(usecols): - return usecols, None - - if not is_list_like(usecols): - # see gh-20529 - # - # Ensure it is iterable container but not string. - raise ValueError(msg) - - usecols_dtype = lib.infer_dtype(usecols, skipna=False) - - if usecols_dtype not in ("empty", "integer", "string"): - raise ValueError(msg) - - usecols = set(usecols) - - return usecols, usecols_dtype - return usecols, None - - -def _validate_parse_dates_arg(parse_dates): - """ - Check whether or not the 'parse_dates' parameter - is a non-boolean scalar. Raises a ValueError if - that is the case. - """ - msg = ( - "Only booleans, lists, and dictionaries are accepted " - "for the 'parse_dates' parameter" - ) - - if parse_dates is not None: - if is_scalar(parse_dates): - if not lib.is_bool(parse_dates): - raise TypeError(msg) - - elif not isinstance(parse_dates, (list, dict)): - raise TypeError(msg) - - return parse_dates - - -class ParserBase: - def __init__(self, kwds): - - self.names = kwds.get("names") - self.orig_names: Optional[List] = None - self.prefix = kwds.pop("prefix", None) - - self.index_col = kwds.get("index_col", None) - self.unnamed_cols: Set = set() - self.index_names: Optional[List] = None - self.col_names = None - - self.parse_dates = _validate_parse_dates_arg(kwds.pop("parse_dates", False)) - self.date_parser = kwds.pop("date_parser", None) - self.dayfirst = kwds.pop("dayfirst", False) - self.keep_date_col = kwds.pop("keep_date_col", False) - - self.na_values = kwds.get("na_values") - self.na_fvalues = kwds.get("na_fvalues") - self.na_filter = kwds.get("na_filter", False) - self.keep_default_na = kwds.get("keep_default_na", True) - - self.true_values = kwds.get("true_values") - self.false_values = kwds.get("false_values") - self.mangle_dupe_cols = kwds.get("mangle_dupe_cols", True) - self.infer_datetime_format = kwds.pop("infer_datetime_format", False) - self.cache_dates = kwds.pop("cache_dates", True) - - self._date_conv = _make_date_converter( - date_parser=self.date_parser, - dayfirst=self.dayfirst, - infer_datetime_format=self.infer_datetime_format, - cache_dates=self.cache_dates, - ) - - # validate header options for mi - self.header = kwds.get("header") - if isinstance(self.header, (list, tuple, np.ndarray)): - if not all(map(is_integer, self.header)): - raise ValueError("header must be integer or list of integers") - if any(i < 0 for i in self.header): - raise ValueError( - "cannot specify multi-index header with negative integers" - ) - if kwds.get("usecols"): - raise ValueError( - "cannot specify usecols when specifying a multi-index header" - ) - if kwds.get("names"): - raise ValueError( - "cannot specify names when specifying a multi-index header" - ) - - # validate index_col that only contains integers - if self.index_col is not None: - is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray)) - if not ( - is_sequence - and all(map(is_integer, self.index_col)) - or is_integer(self.index_col) - ): - raise ValueError( - "index_col must only contain row numbers " - "when specifying a multi-index header" - ) - elif self.header is not None: - # GH 27394 - if self.prefix is not None: - raise ValueError( - "Argument prefix must be None if argument header is not None" - ) - # GH 16338 - elif not is_integer(self.header): - raise ValueError("header must be integer or list of integers") - # GH 27779 - elif self.header < 0: - raise ValueError( - "Passing negative integer to header is invalid. " - "For no header, use header=None instead" - ) - - self._name_processed = False - - self._first_chunk = True - - self.handles: Optional[IOHandles] = None - - def _open_handles(self, src: FilePathOrBuffer, kwds: Dict[str, Any]) -> None: - """ - Let the readers open IOHanldes after they are done with their potential raises. - """ - self.handles = get_handle( - src, - "r", - encoding=kwds.get("encoding", None), - compression=kwds.get("compression", None), - memory_map=kwds.get("memory_map", False), - storage_options=kwds.get("storage_options", None), - ) - - def _validate_parse_dates_presence(self, columns: List[str]) -> None: - """ - Check if parse_dates are in columns. - - If user has provided names for parse_dates, check if those columns - are available. - - Parameters - ---------- - columns : list - List of names of the dataframe. - - Raises - ------ - ValueError - If column to parse_date is not in dataframe. - - """ - cols_needed: Iterable - if is_dict_like(self.parse_dates): - cols_needed = itertools.chain(*self.parse_dates.values()) - elif is_list_like(self.parse_dates): - # a column in parse_dates could be represented - # ColReference = Union[int, str] - # DateGroups = List[ColReference] - # ParseDates = Union[DateGroups, List[DateGroups], - # Dict[ColReference, DateGroups]] - cols_needed = itertools.chain.from_iterable( - col if is_list_like(col) else [col] for col in self.parse_dates - ) - else: - cols_needed = [] - - # get only columns that are references using names (str), not by index - missing_cols = ", ".join( - sorted( - { - col - for col in cols_needed - if isinstance(col, str) and col not in columns - } - ) - ) - if missing_cols: - raise ValueError( - f"Missing column provided to 'parse_dates': '{missing_cols}'" - ) - - def close(self): - if self.handles is not None: - self.handles.close() - - @property - def _has_complex_date_col(self): - return isinstance(self.parse_dates, dict) or ( - isinstance(self.parse_dates, list) - and len(self.parse_dates) > 0 - and isinstance(self.parse_dates[0], list) - ) - - def _should_parse_dates(self, i): - if isinstance(self.parse_dates, bool): - return self.parse_dates - else: - if self.index_names is not None: - name = self.index_names[i] - else: - name = None - j = i if self.index_col is None else self.index_col[i] - - if is_scalar(self.parse_dates): - return (j == self.parse_dates) or ( - name is not None and name == self.parse_dates - ) - else: - return (j in self.parse_dates) or ( - name is not None and name in self.parse_dates - ) - - def _extract_multi_indexer_columns( - self, header, index_names, col_names, passed_names=False - ): - """ - extract and return the names, index_names, col_names - header is a list-of-lists returned from the parsers - """ - if len(header) < 2: - return header[0], index_names, col_names, passed_names - - # the names are the tuples of the header that are not the index cols - # 0 is the name of the index, assuming index_col is a list of column - # numbers - ic = self.index_col - if ic is None: - ic = [] - - if not isinstance(ic, (list, tuple, np.ndarray)): - ic = [ic] - sic = set(ic) - - # clean the index_names - index_names = header.pop(-1) - index_names, _, _ = _clean_index_names( - index_names, self.index_col, self.unnamed_cols - ) - - # extract the columns - field_count = len(header[0]) - - def extract(r): - return tuple(r[i] for i in range(field_count) if i not in sic) - - columns = list(zip(*(extract(r) for r in header))) - names = ic + columns - - # If we find unnamed columns all in a single - # level, then our header was too long. - for n in range(len(columns[0])): - if all(ensure_str(col[n]) in self.unnamed_cols for col in columns): - header = ",".join(str(x) for x in self.header) - raise ParserError( - f"Passed header=[{header}] are too many rows " - "for this multi_index of columns" - ) - - # Clean the column names (if we have an index_col). - if len(ic): - col_names = [ - r[0] if ((r[0] is not None) and r[0] not in self.unnamed_cols) else None - for r in header - ] - else: - col_names = [None] * len(header) - - passed_names = True - - return names, index_names, col_names, passed_names - - def _maybe_dedup_names(self, names): - # see gh-7160 and gh-9424: this helps to provide - # immediate alleviation of the duplicate names - # issue and appears to be satisfactory to users, - # but ultimately, not needing to butcher the names - # would be nice! - if self.mangle_dupe_cols: - names = list(names) # so we can index - # pandas\io\parsers.py:1559: error: Need type annotation for - # 'counts' [var-annotated] - counts = defaultdict(int) # type: ignore[var-annotated] - is_potential_mi = _is_potential_multi_index(names, self.index_col) - - for i, col in enumerate(names): - cur_count = counts[col] - - while cur_count > 0: - counts[col] = cur_count + 1 - - if is_potential_mi: - col = col[:-1] + (f"{col[-1]}.{cur_count}",) - else: - col = f"{col}.{cur_count}" - cur_count = counts[col] - - names[i] = col - counts[col] = cur_count + 1 - - return names - - def _maybe_make_multi_index_columns(self, columns, col_names=None): - # possibly create a column mi here - if _is_potential_multi_index(columns): - columns = MultiIndex.from_tuples(columns, names=col_names) - return columns - - def _make_index(self, data, alldata, columns, indexnamerow=False): - if not _is_index_col(self.index_col) or not self.index_col: - index = None - - elif not self._has_complex_date_col: - index = self._get_simple_index(alldata, columns) - index = self._agg_index(index) - elif self._has_complex_date_col: - if not self._name_processed: - (self.index_names, _, self.index_col) = _clean_index_names( - list(columns), self.index_col, self.unnamed_cols - ) - self._name_processed = True - index = self._get_complex_date_index(data, columns) - index = self._agg_index(index, try_parse_dates=False) - - # add names for the index - if indexnamerow: - coffset = len(indexnamerow) - len(columns) - # pandas\io\parsers.py:1604: error: Item "None" of "Optional[Any]" - # has no attribute "set_names" [union-attr] - index = index.set_names(indexnamerow[:coffset]) # type: ignore[union-attr] - - # maybe create a mi on the columns - columns = self._maybe_make_multi_index_columns(columns, self.col_names) - - return index, columns - - _implicit_index = False - - def _get_simple_index(self, data, columns): - def ix(col): - if not isinstance(col, str): - return col - raise ValueError(f"Index {col} invalid") - - to_remove = [] - index = [] - for idx in self.index_col: - i = ix(idx) - to_remove.append(i) - index.append(data[i]) - - # remove index items from content and columns, don't pop in - # loop - for i in sorted(to_remove, reverse=True): - data.pop(i) - if not self._implicit_index: - columns.pop(i) - - return index - - def _get_complex_date_index(self, data, col_names): - def _get_name(icol): - if isinstance(icol, str): - return icol - - if col_names is None: - raise ValueError(f"Must supply column order to use {icol!s} as index") - - for i, c in enumerate(col_names): - if i == icol: - return c - - to_remove = [] - index = [] - for idx in self.index_col: - name = _get_name(idx) - to_remove.append(name) - index.append(data[name]) - - # remove index items from content and columns, don't pop in - # loop - for c in sorted(to_remove, reverse=True): - data.pop(c) - col_names.remove(c) - - return index - - def _agg_index(self, index, try_parse_dates=True) -> Index: - arrays = [] - - for i, arr in enumerate(index): - - if try_parse_dates and self._should_parse_dates(i): - arr = self._date_conv(arr) - - if self.na_filter: - col_na_values = self.na_values - col_na_fvalues = self.na_fvalues - else: - col_na_values = set() - col_na_fvalues = set() - - if isinstance(self.na_values, dict): - # pandas\io\parsers.py:1678: error: Value of type - # "Optional[Any]" is not indexable [index] - col_name = self.index_names[i] # type: ignore[index] - if col_name is not None: - col_na_values, col_na_fvalues = _get_na_values( - col_name, self.na_values, self.na_fvalues, self.keep_default_na - ) - - arr, _ = self._infer_types(arr, col_na_values | col_na_fvalues) - arrays.append(arr) - - names = self.index_names - index = ensure_index_from_sequences(arrays, names) - - return index - - def _convert_to_ndarrays( - self, dct, na_values, na_fvalues, verbose=False, converters=None, dtypes=None - ): - result = {} - for c, values in dct.items(): - conv_f = None if converters is None else converters.get(c, None) - if isinstance(dtypes, dict): - cast_type = dtypes.get(c, None) - else: - # single dtype or None - cast_type = dtypes - - if self.na_filter: - col_na_values, col_na_fvalues = _get_na_values( - c, na_values, na_fvalues, self.keep_default_na - ) - else: - col_na_values, col_na_fvalues = set(), set() - - if conv_f is not None: - # conv_f applied to data before inference - if cast_type is not None: - warnings.warn( - ( - "Both a converter and dtype were specified " - f"for column {c} - only the converter will be used" - ), - ParserWarning, - stacklevel=7, - ) - - try: - values = lib.map_infer(values, conv_f) - except ValueError: - mask = algorithms.isin(values, list(na_values)).view(np.uint8) - values = lib.map_infer_mask(values, conv_f, mask) - - cvals, na_count = self._infer_types( - values, set(col_na_values) | col_na_fvalues, try_num_bool=False - ) - else: - is_ea = is_extension_array_dtype(cast_type) - is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type) - # skip inference if specified dtype is object - # or casting to an EA - try_num_bool = not (cast_type and is_str_or_ea_dtype) - - # general type inference and conversion - cvals, na_count = self._infer_types( - values, set(col_na_values) | col_na_fvalues, try_num_bool - ) - - # type specified in dtype param or cast_type is an EA - if cast_type and ( - not is_dtype_equal(cvals, cast_type) - or is_extension_array_dtype(cast_type) - ): - if not is_ea and na_count > 0: - try: - if is_bool_dtype(cast_type): - raise ValueError( - f"Bool column has NA values in column {c}" - ) - except (AttributeError, TypeError): - # invalid input to is_bool_dtype - pass - cast_type = pandas_dtype(cast_type) - cvals = self._cast_types(cvals, cast_type, c) - - result[c] = cvals - if verbose and na_count: - print(f"Filled {na_count} NA values in column {c!s}") - return result - - def _infer_types(self, values, na_values, try_num_bool=True): - """ - Infer types of values, possibly casting - - Parameters - ---------- - values : ndarray - na_values : set - try_num_bool : bool, default try - try to cast values to numeric (first preference) or boolean - - Returns - ------- - converted : ndarray - na_count : int - """ - na_count = 0 - if issubclass(values.dtype.type, (np.number, np.bool_)): - mask = algorithms.isin(values, list(na_values)) - na_count = mask.sum() - if na_count > 0: - if is_integer_dtype(values): - values = values.astype(np.float64) - np.putmask(values, mask, np.nan) - return values, na_count - - if try_num_bool and is_object_dtype(values.dtype): - # exclude e.g DatetimeIndex here - try: - result = lib.maybe_convert_numeric(values, na_values, False) - except (ValueError, TypeError): - # e.g. encountering datetime string gets ValueError - # TypeError can be raised in floatify - result = values - na_count = parsers.sanitize_objects(result, na_values, False) - else: - na_count = isna(result).sum() - else: - result = values - if values.dtype == np.object_: - na_count = parsers.sanitize_objects(values, na_values, False) - - if result.dtype == np.object_ and try_num_bool: - result = libops.maybe_convert_bool( - np.asarray(values), - true_values=self.true_values, - false_values=self.false_values, - ) - - return result, na_count - - def _cast_types(self, values, cast_type, column): - """ - Cast values to specified type - - Parameters - ---------- - values : ndarray - cast_type : string or np.dtype - dtype to cast values to - column : string - column name - used only for error reporting - - Returns - ------- - converted : ndarray - """ - if is_categorical_dtype(cast_type): - known_cats = ( - isinstance(cast_type, CategoricalDtype) - and cast_type.categories is not None - ) - - if not is_object_dtype(values) and not known_cats: - # TODO: this is for consistency with - # c-parser which parses all categories - # as strings - values = astype_nansafe(values, str) - - cats = Index(values).unique().dropna() - values = Categorical._from_inferred_categories( - cats, cats.get_indexer(values), cast_type, true_values=self.true_values - ) - - # use the EA's implementation of casting - elif is_extension_array_dtype(cast_type): - # ensure cast_type is an actual dtype and not a string - cast_type = pandas_dtype(cast_type) - array_type = cast_type.construct_array_type() - try: - if is_bool_dtype(cast_type): - return array_type._from_sequence_of_strings( - values, - dtype=cast_type, - true_values=self.true_values, - false_values=self.false_values, - ) - else: - return array_type._from_sequence_of_strings(values, dtype=cast_type) - except NotImplementedError as err: - raise NotImplementedError( - f"Extension Array: {array_type} must implement " - "_from_sequence_of_strings in order to be used in parser methods" - ) from err - - else: - try: - values = astype_nansafe(values, cast_type, copy=True, skipna=True) - except ValueError as err: - raise ValueError( - f"Unable to convert column {column} to type {cast_type}" - ) from err - return values - - def _do_date_conversions(self, names, data): - # returns data, columns - - if self.parse_dates is not None: - data, names = _process_date_conversion( - data, - self._date_conv, - self.parse_dates, - self.index_col, - self.index_names, - names, - keep_date_col=self.keep_date_col, - ) - - return names, data - - -class CParserWrapper(ParserBase): - def __init__(self, src: FilePathOrBuffer, **kwds): - self.kwds = kwds - kwds = kwds.copy() - - ParserBase.__init__(self, kwds) - - # #2442 - kwds["allow_leading_cols"] = self.index_col is not False - - # GH20529, validate usecol arg before TextReader - self.usecols, self.usecols_dtype = _validate_usecols_arg(kwds["usecols"]) - kwds["usecols"] = self.usecols - - # open handles - self._open_handles(src, kwds) - assert self.handles is not None - for key in ("storage_options", "encoding", "memory_map", "compression"): - kwds.pop(key, None) - if self.handles.is_mmap and hasattr(self.handles.handle, "mmap"): - # pandas\io\parsers.py:1861: error: Item "IO[Any]" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "RawIOBase" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "BufferedIOBase" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "TextIOBase" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "TextIOWrapper" of - # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, - # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] - - # pandas\io\parsers.py:1861: error: Item "mmap" of "Union[IO[Any], - # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap]" has - # no attribute "mmap" [union-attr] - self.handles.handle = self.handles.handle.mmap # type: ignore[union-attr] - - try: - self._reader = parsers.TextReader(self.handles.handle, **kwds) - except Exception: - self.handles.close() - raise - self.unnamed_cols = self._reader.unnamed_cols - - passed_names = self.names is None - - if self._reader.header is None: - self.names = None - else: - if len(self._reader.header) > 1: - # we have a multi index in the columns - ( - self.names, - self.index_names, - self.col_names, - passed_names, - ) = self._extract_multi_indexer_columns( - self._reader.header, self.index_names, self.col_names, passed_names - ) - else: - self.names = list(self._reader.header[0]) - - if self.names is None: - if self.prefix: - self.names = [ - f"{self.prefix}{i}" for i in range(self._reader.table_width) - ] - else: - self.names = list(range(self._reader.table_width)) - - # gh-9755 - # - # need to set orig_names here first - # so that proper indexing can be done - # with _set_noconvert_columns - # - # once names has been filtered, we will - # then set orig_names again to names - self.orig_names = self.names[:] - - if self.usecols: - usecols = _evaluate_usecols(self.usecols, self.orig_names) - - # GH 14671 - # assert for mypy, orig_names is List or None, None would error in issubset - assert self.orig_names is not None - if self.usecols_dtype == "string" and not set(usecols).issubset( - self.orig_names - ): - _validate_usecols_names(usecols, self.orig_names) - - if len(self.names) > len(usecols): - self.names = [ - n - for i, n in enumerate(self.names) - if (i in usecols or n in usecols) - ] - - if len(self.names) < len(usecols): - _validate_usecols_names(usecols, self.names) - - self._validate_parse_dates_presence(self.names) - self._set_noconvert_columns() - - self.orig_names = self.names - - if not self._has_complex_date_col: - if self._reader.leading_cols == 0 and _is_index_col(self.index_col): - - self._name_processed = True - (index_names, self.names, self.index_col) = _clean_index_names( - self.names, self.index_col, self.unnamed_cols - ) - - if self.index_names is None: - self.index_names = index_names - - if self._reader.header is None and not passed_names: - # pandas\io\parsers.py:1997: error: Argument 1 to "len" has - # incompatible type "Optional[Any]"; expected "Sized" - # [arg-type] - self.index_names = [None] * len( - self.index_names # type: ignore[arg-type] - ) - - self._implicit_index = self._reader.leading_cols > 0 - - def close(self) -> None: - super().close() - - # close additional handles opened by C parser - try: - self._reader.close() - except ValueError: - pass - - def _set_noconvert_columns(self): - """ - Set the columns that should not undergo dtype conversions. - - Currently, any column that is involved with date parsing will not - undergo such conversions. - """ - names = self.orig_names - if self.usecols_dtype == "integer": - # A set of integers will be converted to a list in - # the correct order every single time. - usecols = list(self.usecols) - usecols.sort() - elif callable(self.usecols) or self.usecols_dtype not in ("empty", None): - # The names attribute should have the correct columns - # in the proper order for indexing with parse_dates. - usecols = self.names[:] - else: - # Usecols is empty. - - # pandas\io\parsers.py:2030: error: Incompatible types in - # assignment (expression has type "None", variable has type - # "List[Any]") [assignment] - usecols = None # type: ignore[assignment] - - def _set(x): - if usecols is not None and is_integer(x): - x = usecols[x] - - if not is_integer(x): - # assert for mypy, names is List or None, None would error when calling - # .index() - assert names is not None - x = names.index(x) - - self._reader.set_noconvert(x) - - if isinstance(self.parse_dates, list): - for val in self.parse_dates: - if isinstance(val, list): - for k in val: - _set(k) - else: - _set(val) - - elif isinstance(self.parse_dates, dict): - for val in self.parse_dates.values(): - if isinstance(val, list): - for k in val: - _set(k) - else: - _set(val) - - elif self.parse_dates: - if isinstance(self.index_col, list): - for k in self.index_col: - _set(k) - elif self.index_col is not None: - _set(self.index_col) - - def set_error_bad_lines(self, status): - self._reader.set_error_bad_lines(int(status)) - - def read(self, nrows=None): - try: - data = self._reader.read(nrows) - except StopIteration: - if self._first_chunk: - self._first_chunk = False - names = self._maybe_dedup_names(self.orig_names) - index, columns, col_dict = _get_empty_meta( - names, - self.index_col, - self.index_names, - dtype=self.kwds.get("dtype"), - ) - columns = self._maybe_make_multi_index_columns(columns, self.col_names) - - if self.usecols is not None: - columns = self._filter_usecols(columns) - - col_dict = {k: v for k, v in col_dict.items() if k in columns} - - return index, columns, col_dict - - else: - self.close() - raise - - # Done with first read, next time raise StopIteration - self._first_chunk = False - - names = self.names - - if self._reader.leading_cols: - if self._has_complex_date_col: - raise NotImplementedError("file structure not yet supported") - - # implicit index, no index names - arrays = [] - - for i in range(self._reader.leading_cols): - if self.index_col is None: - values = data.pop(i) - else: - values = data.pop(self.index_col[i]) - - values = self._maybe_parse_dates(values, i, try_parse_dates=True) - arrays.append(values) - - index = ensure_index_from_sequences(arrays) - - if self.usecols is not None: - names = self._filter_usecols(names) - - names = self._maybe_dedup_names(names) - - # rename dict keys - data = sorted(data.items()) - data = {k: v for k, (i, v) in zip(names, data)} - - names, data = self._do_date_conversions(names, data) - - else: - # rename dict keys - data = sorted(data.items()) - - # ugh, mutation - - # assert for mypy, orig_names is List or None, None would error in list(...) - assert self.orig_names is not None - names = list(self.orig_names) - names = self._maybe_dedup_names(names) - - if self.usecols is not None: - names = self._filter_usecols(names) - - # columns as list - alldata = [x[1] for x in data] - - data = {k: v for k, (i, v) in zip(names, data)} - - names, data = self._do_date_conversions(names, data) - index, names = self._make_index(data, alldata, names) - - # maybe create a mi on the columns - names = self._maybe_make_multi_index_columns(names, self.col_names) - - return index, names, data - - def _filter_usecols(self, names): - # hackish - usecols = _evaluate_usecols(self.usecols, names) - if usecols is not None and len(names) != len(usecols): - names = [ - name for i, name in enumerate(names) if i in usecols or name in usecols - ] - return names - - def _get_index_names(self): - names = list(self._reader.header[0]) - idx_names = None - - if self._reader.leading_cols == 0 and self.index_col is not None: - (idx_names, names, self.index_col) = _clean_index_names( - names, self.index_col, self.unnamed_cols - ) - - return names, idx_names - - def _maybe_parse_dates(self, values, index, try_parse_dates=True): - if try_parse_dates and self._should_parse_dates(index): - values = self._date_conv(values) - return values - - -def TextParser(*args, **kwds): - """ - Converts lists of lists/tuples into DataFrames with proper type inference - and optional (e.g. string to datetime) conversion. Also enables iterating - lazily over chunks of large files - - Parameters - ---------- - data : file-like object or list - delimiter : separator character to use - dialect : str or csv.Dialect instance, optional - Ignored if delimiter is longer than 1 character - names : sequence, default - header : int, default 0 - Row to use to parse column labels. Defaults to the first row. Prior - rows will be discarded - index_col : int or list, optional - Column or columns to use as the (possibly hierarchical) index - has_index_names: bool, default False - True if the cols defined in index_col have an index name and are - not in the header. - na_values : scalar, str, list-like, or dict, optional - Additional strings to recognize as NA/NaN. - keep_default_na : bool, default True - thousands : str, optional - Thousands separator - comment : str, optional - Comment out remainder of line - parse_dates : bool, default False - keep_date_col : bool, default False - date_parser : function, optional - skiprows : list of integers - Row numbers to skip - skipfooter : int - Number of line at bottom of file to skip - converters : dict, optional - Dict of functions for converting values in certain columns. Keys can - either be integers or column labels, values are functions that take one - input argument, the cell (not column) content, and return the - transformed content. - encoding : str, optional - Encoding to use for UTF when reading/writing (ex. 'utf-8') - squeeze : bool, default False - returns Series if only one column. - infer_datetime_format: bool, default False - If True and `parse_dates` is True for a column, try to infer the - datetime format based on the first datetime string. If the format - can be inferred, there often will be a large parsing speed-up. - float_precision : str, optional - Specifies which converter the C engine should use for floating-point - values. The options are `None` or `high` for the ordinary converter, - `legacy` for the original lower precision pandas converter, and - `round_trip` for the round-trip converter. - - .. versionchanged:: 1.2 - """ - kwds["engine"] = "python" - return TextFileReader(*args, **kwds) - - -def count_empty_vals(vals) -> int: - return sum(1 for v in vals if v == "" or v is None) - - -class PythonParser(ParserBase): - def __init__(self, f: Union[FilePathOrBuffer, List], **kwds): - """ - Workhorse function for processing nested list into DataFrame - """ - ParserBase.__init__(self, kwds) - - self.data: Optional[Iterator[str]] = None - self.buf: List = [] - self.pos = 0 - self.line_pos = 0 - - self.skiprows = kwds["skiprows"] - - if callable(self.skiprows): - self.skipfunc = self.skiprows - else: - self.skipfunc = lambda x: x in self.skiprows - - self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"]) - self.delimiter = kwds["delimiter"] - - self.quotechar = kwds["quotechar"] - if isinstance(self.quotechar, str): - self.quotechar = str(self.quotechar) - - self.escapechar = kwds["escapechar"] - self.doublequote = kwds["doublequote"] - self.skipinitialspace = kwds["skipinitialspace"] - self.lineterminator = kwds["lineterminator"] - self.quoting = kwds["quoting"] - self.usecols, _ = _validate_usecols_arg(kwds["usecols"]) - self.skip_blank_lines = kwds["skip_blank_lines"] - - self.warn_bad_lines = kwds["warn_bad_lines"] - self.error_bad_lines = kwds["error_bad_lines"] - - self.names_passed = kwds["names"] or None - - self.has_index_names = False - if "has_index_names" in kwds: - self.has_index_names = kwds["has_index_names"] - - self.verbose = kwds["verbose"] - self.converters = kwds["converters"] - - self.dtype = kwds["dtype"] - self.thousands = kwds["thousands"] - self.decimal = kwds["decimal"] - - self.comment = kwds["comment"] - - # Set self.data to something that can read lines. - if isinstance(f, list): - # read_excel: f is a list - self.data = cast(Iterator[str], f) - else: - self._open_handles(f, kwds) - assert self.handles is not None - assert hasattr(self.handles.handle, "readline") - try: - self._make_reader(self.handles.handle) - except (csv.Error, UnicodeDecodeError): - self.close() - raise - - # Get columns in two steps: infer from data, then - # infer column indices from self.usecols if it is specified. - self._col_indices: Optional[List[int]] = None - try: - ( - self.columns, - self.num_original_columns, - self.unnamed_cols, - ) = self._infer_columns() - except (TypeError, ValueError): - self.close() - raise - - # Now self.columns has the set of columns that we will process. - # The original set is stored in self.original_columns. - if len(self.columns) > 1: - # we are processing a multi index column - ( - self.columns, - self.index_names, - self.col_names, - _, - ) = self._extract_multi_indexer_columns( - self.columns, self.index_names, self.col_names - ) - # Update list of original names to include all indices. - self.num_original_columns = len(self.columns) - else: - self.columns = self.columns[0] - - # get popped off for index - self.orig_names = list(self.columns) - - # needs to be cleaned/refactored - # multiple date column thing turning into a real spaghetti factory - - if not self._has_complex_date_col: - (index_names, self.orig_names, self.columns) = self._get_index_name( - self.columns - ) - self._name_processed = True - if self.index_names is None: - self.index_names = index_names - - if self._col_indices is None: - self._col_indices = list(range(len(self.columns))) - - self._validate_parse_dates_presence(self.columns) - if self.parse_dates: - self._no_thousands_columns = self._set_no_thousands_columns() - else: - self._no_thousands_columns = None - - if len(self.decimal) != 1: - raise ValueError("Only length-1 decimal markers supported") - - decimal = re.escape(self.decimal) - if self.thousands is None: - regex = fr"^[\-\+]?[0-9]*({decimal}[0-9]*)?([0-9]?(E|e)\-?[0-9]+)?$" - else: - thousands = re.escape(self.thousands) - regex = ( - fr"^[\-\+]?([0-9]+{thousands}|[0-9])*({decimal}[0-9]*)?" - fr"([0-9]?(E|e)\-?[0-9]+)?$" - ) - self.num = re.compile(regex) - - def _set_no_thousands_columns(self): - # Create a set of column ids that are not to be stripped of thousands - # operators. - noconvert_columns = set() - - def _set(x): - if is_integer(x): - noconvert_columns.add(x) - else: - assert self._col_indices is not None - col_indices = self._col_indices - noconvert_columns.add(col_indices[self.columns.index(x)]) - - if isinstance(self.parse_dates, list): - for val in self.parse_dates: - if isinstance(val, list): - for k in val: - _set(k) - else: - _set(val) - - elif isinstance(self.parse_dates, dict): - for val in self.parse_dates.values(): - if isinstance(val, list): - for k in val: - _set(k) - else: - _set(val) - - elif self.parse_dates: - if isinstance(self.index_col, list): - for k in self.index_col: - _set(k) - elif self.index_col is not None: - _set(self.index_col) - - return noconvert_columns - - def _make_reader(self, f): - sep = self.delimiter - - if sep is None or len(sep) == 1: - if self.lineterminator: - raise ValueError( - "Custom line terminators not supported in python parser (yet)" - ) - - class MyDialect(csv.Dialect): - delimiter = self.delimiter - quotechar = self.quotechar - escapechar = self.escapechar - doublequote = self.doublequote - skipinitialspace = self.skipinitialspace - quoting = self.quoting - lineterminator = "\n" - - dia = MyDialect - - if sep is not None: - dia.delimiter = sep - else: - # attempt to sniff the delimiter from the first valid line, - # i.e. no comment line and not in skiprows - line = f.readline() - lines = self._check_comments([[line]])[0] - while self.skipfunc(self.pos) or not lines: - self.pos += 1 - line = f.readline() - lines = self._check_comments([[line]])[0] - - # since `line` was a string, lines will be a list containing - # only a single string - line = lines[0] - - self.pos += 1 - self.line_pos += 1 - sniffed = csv.Sniffer().sniff(line) - dia.delimiter = sniffed.delimiter - - # Note: encoding is irrelevant here - line_rdr = csv.reader(StringIO(line), dialect=dia) - self.buf.extend(list(line_rdr)) - - # Note: encoding is irrelevant here - reader = csv.reader(f, dialect=dia, strict=True) - - else: - - def _read(): - line = f.readline() - pat = re.compile(sep) - - yield pat.split(line.strip()) - - for line in f: - yield pat.split(line.strip()) - - reader = _read() - - # pandas\io\parsers.py:2427: error: Incompatible types in assignment - # (expression has type "_reader", variable has type "Union[IO[Any], - # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap, None]") - # [assignment] - self.data = reader # type: ignore[assignment] - - def read(self, rows=None): - try: - content = self._get_lines(rows) - except StopIteration: - if self._first_chunk: - content = [] - else: - self.close() - raise - - # done with first read, next time raise StopIteration - self._first_chunk = False - - # pandas\io\parsers.py:2480: error: Argument 1 to "list" has - # incompatible type "Optional[Any]"; expected "Iterable[Any]" - # [arg-type] - columns = list(self.orig_names) # type: ignore[arg-type] - if not len(content): # pragma: no cover - # DataFrame with the right metadata, even though it's length 0 - names = self._maybe_dedup_names(self.orig_names) - index, columns, col_dict = _get_empty_meta( - names, self.index_col, self.index_names, self.dtype - ) - columns = self._maybe_make_multi_index_columns(columns, self.col_names) - return index, columns, col_dict - - # handle new style for names in index - count_empty_content_vals = count_empty_vals(content[0]) - indexnamerow = None - if self.has_index_names and count_empty_content_vals == len(columns): - indexnamerow = content[0] - content = content[1:] - - alldata = self._rows_to_cols(content) - data, columns = self._exclude_implicit_index(alldata) - - columns, data = self._do_date_conversions(columns, data) - - data = self._convert_data(data) - index, columns = self._make_index(data, alldata, columns, indexnamerow) - - return index, columns, data - - def _exclude_implicit_index(self, alldata): - names = self._maybe_dedup_names(self.orig_names) - - offset = 0 - if self._implicit_index: - offset = len(self.index_col) - - if self._col_indices is not None and len(names) != len(self._col_indices): - names = [names[i] for i in sorted(self._col_indices)] - - return {name: alldata[i + offset] for i, name in enumerate(names)}, names - - # legacy - def get_chunk(self, size=None): - if size is None: - # pandas\io\parsers.py:2528: error: "PythonParser" has no attribute - # "chunksize" [attr-defined] - size = self.chunksize # type: ignore[attr-defined] - return self.read(rows=size) - - def _convert_data(self, data): - # apply converters - def _clean_mapping(mapping): - """converts col numbers to names""" - clean = {} - for col, v in mapping.items(): - # pandas\io\parsers.py:2537: error: Unsupported right operand - # type for in ("Optional[Any]") [operator] - if ( - isinstance(col, int) - and col not in self.orig_names # type: ignore[operator] - ): - # pandas\io\parsers.py:2538: error: Value of type - # "Optional[Any]" is not indexable [index] - col = self.orig_names[col] # type: ignore[index] - clean[col] = v - return clean - - clean_conv = _clean_mapping(self.converters) - if not isinstance(self.dtype, dict): - # handles single dtype applied to all columns - clean_dtypes = self.dtype - else: - clean_dtypes = _clean_mapping(self.dtype) - - # Apply NA values. - clean_na_values = {} - clean_na_fvalues = {} - - if isinstance(self.na_values, dict): - for col in self.na_values: - na_value = self.na_values[col] - na_fvalue = self.na_fvalues[col] - - # pandas\io\parsers.py:2558: error: Unsupported right operand - # type for in ("Optional[Any]") [operator] - if ( - isinstance(col, int) - and col not in self.orig_names # type: ignore[operator] - ): - # pandas\io\parsers.py:2559: error: Value of type - # "Optional[Any]" is not indexable [index] - col = self.orig_names[col] # type: ignore[index] - - clean_na_values[col] = na_value - clean_na_fvalues[col] = na_fvalue - else: - clean_na_values = self.na_values - clean_na_fvalues = self.na_fvalues - - return self._convert_to_ndarrays( - data, - clean_na_values, - clean_na_fvalues, - self.verbose, - clean_conv, - clean_dtypes, - ) - - def _infer_columns(self): - names = self.names - num_original_columns = 0 - clear_buffer = True - # pandas\io\parsers.py:2580: error: Need type annotation for - # 'unnamed_cols' (hint: "unnamed_cols: Set[<type>] = ...") - # [var-annotated] - unnamed_cols = set() # type: ignore[var-annotated] - - if self.header is not None: - header = self.header - - if isinstance(header, (list, tuple, np.ndarray)): - have_mi_columns = len(header) > 1 - # we have a mi columns, so read an extra line - if have_mi_columns: - header = list(header) + [header[-1] + 1] - else: - have_mi_columns = False - header = [header] - - # pandas\io\parsers.py:2594: error: Need type annotation for - # 'columns' (hint: "columns: List[<type>] = ...") [var-annotated] - columns = [] # type: ignore[var-annotated] - for level, hr in enumerate(header): - try: - line = self._buffered_line() - - while self.line_pos <= hr: - line = self._next_line() - - except StopIteration as err: - if self.line_pos < hr: - raise ValueError( - f"Passed header={hr} but only {self.line_pos + 1} lines in " - "file" - ) from err - - # We have an empty file, so check - # if columns are provided. That will - # serve as the 'line' for parsing - if have_mi_columns and hr > 0: - if clear_buffer: - self._clear_buffer() - columns.append([None] * len(columns[-1])) - return columns, num_original_columns, unnamed_cols - - if not self.names: - raise EmptyDataError("No columns to parse from file") from err - - line = self.names[:] - - this_columns = [] - this_unnamed_cols = [] - - for i, c in enumerate(line): - if c == "": - if have_mi_columns: - col_name = f"Unnamed: {i}_level_{level}" - else: - col_name = f"Unnamed: {i}" - - this_unnamed_cols.append(i) - this_columns.append(col_name) - else: - this_columns.append(c) - - if not have_mi_columns and self.mangle_dupe_cols: - # pandas\io\parsers.py:2639: error: Need type annotation - # for 'counts' [var-annotated] - counts = defaultdict(int) # type: ignore[var-annotated] - - for i, col in enumerate(this_columns): - cur_count = counts[col] - - while cur_count > 0: - counts[col] = cur_count + 1 - col = f"{col}.{cur_count}" - cur_count = counts[col] - - this_columns[i] = col - counts[col] = cur_count + 1 - elif have_mi_columns: - - # if we have grabbed an extra line, but its not in our - # format so save in the buffer, and create an blank extra - # line for the rest of the parsing code - if hr == header[-1]: - lc = len(this_columns) - ic = len(self.index_col) if self.index_col is not None else 0 - unnamed_count = len(this_unnamed_cols) - - if lc != unnamed_count and lc - ic > unnamed_count: - clear_buffer = False - # pandas\io\parsers.py:2663: error: List item 0 has - # incompatible type "None"; expected "str" - # [list-item] - this_columns = [None] * lc # type: ignore[list-item] - self.buf = [self.buf[-1]] - - # pandas\io\parsers.py:2666: error: Argument 1 to "append" of - # "list" has incompatible type "List[str]"; expected - # "List[None]" [arg-type] - columns.append(this_columns) # type: ignore[arg-type] - unnamed_cols.update({this_columns[i] for i in this_unnamed_cols}) - - if len(columns) == 1: - num_original_columns = len(this_columns) - - if clear_buffer: - self._clear_buffer() - - if names is not None: - if len(names) > len(columns[0]): - raise ValueError( - "Number of passed names did not match " - "number of header fields in the file" - ) - if len(columns) > 1: - raise TypeError("Cannot pass names with multi-index columns") - - if self.usecols is not None: - # Set _use_cols. We don't store columns because they are - # overwritten. - self._handle_usecols(columns, names) - else: - num_original_columns = len(names) - columns = [names] - else: - columns = self._handle_usecols(columns, columns[0]) - else: - try: - line = self._buffered_line() - - except StopIteration as err: - if not names: - raise EmptyDataError("No columns to parse from file") from err - - line = names[:] - - ncols = len(line) - num_original_columns = ncols - - if not names: - if self.prefix: - # pandas\io\parsers.py:2711: error: List comprehension has - # incompatible type List[str]; expected List[None] [misc] - columns = [ - [ - f"{self.prefix}{i}" # type: ignore[misc] - for i in range(ncols) - ] - ] - else: - # pandas\io\parsers.py:2713: error: Argument 1 to "list" - # has incompatible type "range"; expected "Iterable[None]" - # [arg-type] - columns = [list(range(ncols))] # type: ignore[arg-type] - columns = self._handle_usecols(columns, columns[0]) - else: - if self.usecols is None or len(names) >= num_original_columns: - columns = self._handle_usecols([names], names) - num_original_columns = len(names) - else: - if not callable(self.usecols) and len(names) != len(self.usecols): - raise ValueError( - "Number of passed names did not match number of " - "header fields in the file" - ) - # Ignore output but set used columns. - self._handle_usecols([names], names) - columns = [names] - num_original_columns = ncols - - return columns, num_original_columns, unnamed_cols - - def _handle_usecols(self, columns, usecols_key): - """ - Sets self._col_indices - - usecols_key is used if there are string usecols. - """ - if self.usecols is not None: - if callable(self.usecols): - col_indices = _evaluate_usecols(self.usecols, usecols_key) - elif any(isinstance(u, str) for u in self.usecols): - if len(columns) > 1: - raise ValueError( - "If using multiple headers, usecols must be integers." - ) - col_indices = [] - - for col in self.usecols: - if isinstance(col, str): - try: - col_indices.append(usecols_key.index(col)) - except ValueError: - _validate_usecols_names(self.usecols, usecols_key) - else: - col_indices.append(col) - else: - col_indices = self.usecols - - columns = [ - [n for i, n in enumerate(column) if i in col_indices] - for column in columns - ] - self._col_indices = sorted(col_indices) - return columns - - def _buffered_line(self): - """ - Return a line from buffer, filling buffer if required. - """ - if len(self.buf) > 0: - return self.buf[0] - else: - return self._next_line() - - def _check_for_bom(self, first_row): - """ - Checks whether the file begins with the BOM character. - If it does, remove it. In addition, if there is quoting - in the field subsequent to the BOM, remove it as well - because it technically takes place at the beginning of - the name, not the middle of it. - """ - # first_row will be a list, so we need to check - # that that list is not empty before proceeding. - if not first_row: - return first_row - - # The first element of this row is the one that could have the - # BOM that we want to remove. Check that the first element is a - # string before proceeding. - if not isinstance(first_row[0], str): - return first_row - - # Check that the string is not empty, as that would - # obviously not have a BOM at the start of it. - if not first_row[0]: - return first_row - - # Since the string is non-empty, check that it does - # in fact begin with a BOM. - first_elt = first_row[0][0] - if first_elt != _BOM: - return first_row - - first_row_bom = first_row[0] - - if len(first_row_bom) > 1 and first_row_bom[1] == self.quotechar: - start = 2 - quote = first_row_bom[1] - end = first_row_bom[2:].index(quote) + 2 - - # Extract the data between the quotation marks - new_row = first_row_bom[start:end] - - # Extract any remaining data after the second - # quotation mark. - if len(first_row_bom) > end + 1: - new_row += first_row_bom[end + 1 :] - - else: - - # No quotation so just remove BOM from first element - new_row = first_row_bom[1:] - return [new_row] + first_row[1:] - - def _is_line_empty(self, line): - """ - Check if a line is empty or not. - - Parameters - ---------- - line : str, array-like - The line of data to check. - - Returns - ------- - boolean : Whether or not the line is empty. - """ - return not line or all(not x for x in line) - - def _next_line(self): - if isinstance(self.data, list): - while self.skipfunc(self.pos): - self.pos += 1 - - while True: - try: - line = self._check_comments([self.data[self.pos]])[0] - self.pos += 1 - # either uncommented or blank to begin with - if not self.skip_blank_lines and ( - self._is_line_empty(self.data[self.pos - 1]) or line - ): - break - elif self.skip_blank_lines: - ret = self._remove_empty_lines([line]) - if ret: - line = ret[0] - break - except IndexError: - raise StopIteration - else: - while self.skipfunc(self.pos): - self.pos += 1 - # assert for mypy, data is Iterator[str] or None, would error in next - assert self.data is not None - next(self.data) - - while True: - orig_line = self._next_iter_line(row_num=self.pos + 1) - self.pos += 1 - - if orig_line is not None: - line = self._check_comments([orig_line])[0] - - if self.skip_blank_lines: - ret = self._remove_empty_lines([line]) - - if ret: - line = ret[0] - break - elif self._is_line_empty(orig_line) or line: - break - - # This was the first line of the file, - # which could contain the BOM at the - # beginning of it. - if self.pos == 1: - line = self._check_for_bom(line) - - self.line_pos += 1 - self.buf.append(line) - return line - - def _alert_malformed(self, msg, row_num): - """ - Alert a user about a malformed row. - - If `self.error_bad_lines` is True, the alert will be `ParserError`. - If `self.warn_bad_lines` is True, the alert will be printed out. - - Parameters - ---------- - msg : The error message to display. - row_num : The row number where the parsing error occurred. - Because this row number is displayed, we 1-index, - even though we 0-index internally. - """ - if self.error_bad_lines: - raise ParserError(msg) - elif self.warn_bad_lines: - base = f"Skipping line {row_num}: " - sys.stderr.write(base + msg + "\n") - - def _next_iter_line(self, row_num): - """ - Wrapper around iterating through `self.data` (CSV source). - - When a CSV error is raised, we check for specific - error messages that allow us to customize the - error message displayed to the user. - - Parameters - ---------- - row_num : The row number of the line being parsed. - """ - try: - # assert for mypy, data is Iterator[str] or None, would error in next - assert self.data is not None - return next(self.data) - except csv.Error as e: - if self.warn_bad_lines or self.error_bad_lines: - msg = str(e) - - if "NULL byte" in msg or "line contains NUL" in msg: - msg = ( - "NULL byte detected. This byte " - "cannot be processed in Python's " - "native csv library at the moment, " - "so please pass in engine='c' instead" - ) - - if self.skipfooter > 0: - reason = ( - "Error could possibly be due to " - "parsing errors in the skipped footer rows " - "(the skipfooter keyword is only applied " - "after Python's csv library has parsed " - "all rows)." - ) - msg += ". " + reason - - self._alert_malformed(msg, row_num) - return None - - def _check_comments(self, lines): - if self.comment is None: - return lines - ret = [] - for line in lines: - rl = [] - for x in line: - if ( - not isinstance(x, str) - or self.comment not in x - or x in self.na_values - ): - rl.append(x) - else: - x = x[: x.find(self.comment)] - if len(x) > 0: - rl.append(x) - break - ret.append(rl) - return ret - - def _remove_empty_lines(self, lines): - """ - Iterate through the lines and remove any that are - either empty or contain only one whitespace value - - Parameters - ---------- - lines : array-like - The array of lines that we are to filter. - - Returns - ------- - filtered_lines : array-like - The same array of lines with the "empty" ones removed. - """ - ret = [] - for line in lines: - # Remove empty lines and lines with only one whitespace value - if ( - len(line) > 1 - or len(line) == 1 - and (not isinstance(line[0], str) or line[0].strip()) - ): - ret.append(line) - return ret - - def _check_thousands(self, lines): - if self.thousands is None: - return lines - - return self._search_replace_num_columns( - lines=lines, search=self.thousands, replace="" - ) - - def _search_replace_num_columns(self, lines, search, replace): - ret = [] - for line in lines: - rl = [] - for i, x in enumerate(line): - if ( - not isinstance(x, str) - or search not in x - or (self._no_thousands_columns and i in self._no_thousands_columns) - or not self.num.search(x.strip()) - ): - rl.append(x) - else: - rl.append(x.replace(search, replace)) - ret.append(rl) - return ret - - def _check_decimal(self, lines): - if self.decimal == _parser_defaults["decimal"]: - return lines - - return self._search_replace_num_columns( - lines=lines, search=self.decimal, replace="." - ) - - def _clear_buffer(self): - self.buf = [] - - _implicit_index = False - - def _get_index_name(self, columns): - """ - Try several cases to get lines: - - 0) There are headers on row 0 and row 1 and their - total summed lengths equals the length of the next line. - Treat row 0 as columns and row 1 as indices - 1) Look for implicit index: there are more columns - on row 1 than row 0. If this is true, assume that row - 1 lists index columns and row 0 lists normal columns. - 2) Get index from the columns if it was listed. - """ - orig_names = list(columns) - columns = list(columns) - - try: - line = self._next_line() - except StopIteration: - line = None - - try: - next_line = self._next_line() - except StopIteration: - next_line = None - - # implicitly index_col=0 b/c 1 fewer column names - implicit_first_cols = 0 - if line is not None: - # leave it 0, #2442 - # Case 1 - if self.index_col is not False: - implicit_first_cols = len(line) - self.num_original_columns - - # Case 0 - if next_line is not None: - if len(next_line) == len(line) + self.num_original_columns: - # column and index names on diff rows - self.index_col = list(range(len(line))) - self.buf = self.buf[1:] - - for c in reversed(line): - columns.insert(0, c) - - # Update list of original names to include all indices. - orig_names = list(columns) - self.num_original_columns = len(columns) - return line, orig_names, columns - - if implicit_first_cols > 0: - # Case 1 - self._implicit_index = True - if self.index_col is None: - self.index_col = list(range(implicit_first_cols)) - - index_name = None - - else: - # Case 2 - (index_name, columns_, self.index_col) = _clean_index_names( - columns, self.index_col, self.unnamed_cols - ) - - return index_name, orig_names, columns - - def _rows_to_cols(self, content): - col_len = self.num_original_columns - - if self._implicit_index: - col_len += len(self.index_col) - - max_len = max(len(row) for row in content) - - # Check that there are no rows with too many - # elements in their row (rows with too few - # elements are padded with NaN). - if max_len > col_len and self.index_col is not False and self.usecols is None: - - footers = self.skipfooter if self.skipfooter else 0 - bad_lines = [] - - iter_content = enumerate(content) - content_len = len(content) - content = [] - - for (i, l) in iter_content: - actual_len = len(l) - - if actual_len > col_len: - if self.error_bad_lines or self.warn_bad_lines: - row_num = self.pos - (content_len - i + footers) - bad_lines.append((row_num, actual_len)) - - if self.error_bad_lines: - break - else: - content.append(l) - - for row_num, actual_len in bad_lines: - msg = ( - f"Expected {col_len} fields in line {row_num + 1}, saw " - f"{actual_len}" - ) - if ( - self.delimiter - and len(self.delimiter) > 1 - and self.quoting != csv.QUOTE_NONE - ): - # see gh-13374 - reason = ( - "Error could possibly be due to quotes being " - "ignored when a multi-char delimiter is used." - ) - msg += ". " + reason - - self._alert_malformed(msg, row_num + 1) - - # see gh-13320 - zipped_content = list(lib.to_object_array(content, min_width=col_len).T) - - if self.usecols: - assert self._col_indices is not None - col_indices = self._col_indices - - if self._implicit_index: - zipped_content = [ - a - for i, a in enumerate(zipped_content) - if ( - i < len(self.index_col) - or i - len(self.index_col) in col_indices - ) - ] - else: - zipped_content = [ - a for i, a in enumerate(zipped_content) if i in col_indices - ] - return zipped_content - - def _get_lines(self, rows=None): - lines = self.buf - new_rows = None - - # already fetched some number - if rows is not None: - # we already have the lines in the buffer - if len(self.buf) >= rows: - new_rows, self.buf = self.buf[:rows], self.buf[rows:] - - # need some lines - else: - rows -= len(self.buf) - - if new_rows is None: - if isinstance(self.data, list): - if self.pos > len(self.data): - raise StopIteration - if rows is None: - new_rows = self.data[self.pos :] - new_pos = len(self.data) - else: - new_rows = self.data[self.pos : self.pos + rows] - new_pos = self.pos + rows - - # Check for stop rows. n.b.: self.skiprows is a set. - if self.skiprows: - new_rows = [ - row - for i, row in enumerate(new_rows) - if not self.skipfunc(i + self.pos) - ] - - lines.extend(new_rows) - self.pos = new_pos - - else: - new_rows = [] - try: - if rows is not None: - for _ in range(rows): - # assert for mypy, data is Iterator[str] or None, would - # error in next - assert self.data is not None - new_rows.append(next(self.data)) - lines.extend(new_rows) - else: - rows = 0 - - while True: - new_row = self._next_iter_line(row_num=self.pos + rows + 1) - rows += 1 - - if new_row is not None: - new_rows.append(new_row) - - except StopIteration: - if self.skiprows: - new_rows = [ - row - for i, row in enumerate(new_rows) - if not self.skipfunc(i + self.pos) - ] - lines.extend(new_rows) - if len(lines) == 0: - raise - self.pos += len(new_rows) - - self.buf = [] - else: - lines = new_rows - - if self.skipfooter: - lines = lines[: -self.skipfooter] - - lines = self._check_comments(lines) - if self.skip_blank_lines: - lines = self._remove_empty_lines(lines) - lines = self._check_thousands(lines) - return self._check_decimal(lines) - - -def _make_date_converter( - date_parser=None, dayfirst=False, infer_datetime_format=False, cache_dates=True -): - def converter(*date_cols): - if date_parser is None: - strs = parsing.concat_date_cols(date_cols) - - try: - return tools.to_datetime( - ensure_object(strs), - utc=None, - dayfirst=dayfirst, - errors="ignore", - infer_datetime_format=infer_datetime_format, - cache=cache_dates, - ).to_numpy() - - except ValueError: - return tools.to_datetime( - parsing.try_parse_dates(strs, dayfirst=dayfirst), cache=cache_dates - ) - else: - try: - result = tools.to_datetime( - date_parser(*date_cols), errors="ignore", cache=cache_dates - ) - if isinstance(result, datetime.datetime): - raise Exception("scalar parser") - return result - except Exception: - try: - return tools.to_datetime( - parsing.try_parse_dates( - parsing.concat_date_cols(date_cols), - parser=date_parser, - dayfirst=dayfirst, - ), - errors="ignore", - ) - except Exception: - return generic_parser(date_parser, *date_cols) - - return converter - - -def _process_date_conversion( - data_dict, - converter, - parse_spec, - index_col, - index_names, - columns, - keep_date_col=False, -): - def _isindex(colspec): - return (isinstance(index_col, list) and colspec in index_col) or ( - isinstance(index_names, list) and colspec in index_names - ) - - new_cols = [] - new_data = {} - - orig_names = columns - columns = list(columns) - - date_cols = set() - - if parse_spec is None or isinstance(parse_spec, bool): - return data_dict, columns - - if isinstance(parse_spec, list): - # list of column lists - for colspec in parse_spec: - if is_scalar(colspec): - if isinstance(colspec, int) and colspec not in data_dict: - colspec = orig_names[colspec] - if _isindex(colspec): - continue - data_dict[colspec] = converter(data_dict[colspec]) - else: - new_name, col, old_names = _try_convert_dates( - converter, colspec, data_dict, orig_names - ) - if new_name in data_dict: - raise ValueError(f"New date column already in dict {new_name}") - new_data[new_name] = col - new_cols.append(new_name) - date_cols.update(old_names) - - elif isinstance(parse_spec, dict): - # dict of new name to column list - for new_name, colspec in parse_spec.items(): - if new_name in data_dict: - raise ValueError(f"Date column {new_name} already in dict") - - _, col, old_names = _try_convert_dates( - converter, colspec, data_dict, orig_names - ) - - new_data[new_name] = col - new_cols.append(new_name) - date_cols.update(old_names) - - data_dict.update(new_data) - new_cols.extend(columns) - - if not keep_date_col: - for c in list(date_cols): - data_dict.pop(c) - new_cols.remove(c) - - return data_dict, new_cols - - -def _try_convert_dates(parser, colspec, data_dict, columns): - colset = set(columns) - colnames = [] - - for c in colspec: - if c in colset: - colnames.append(c) - elif isinstance(c, int) and c not in columns: - colnames.append(columns[c]) - else: - colnames.append(c) - - new_name = "_".join(str(x) for x in colnames) - to_parse = [data_dict[c] for c in colnames if c in data_dict] - - new_col = parser(*to_parse) - return new_name, new_col, colnames - - -def _clean_na_values(na_values, keep_default_na=True): - - if na_values is None: - if keep_default_na: - na_values = STR_NA_VALUES - else: - na_values = set() - # pandas\io\parsers.py:3387: error: Need type annotation for - # 'na_fvalues' (hint: "na_fvalues: Set[<type>] = ...") [var-annotated] - na_fvalues = set() # type: ignore[var-annotated] - elif isinstance(na_values, dict): - old_na_values = na_values.copy() - na_values = {} # Prevent aliasing. - - # Convert the values in the na_values dictionary - # into array-likes for further use. This is also - # where we append the default NaN values, provided - # that `keep_default_na=True`. - for k, v in old_na_values.items(): - if not is_list_like(v): - v = [v] - - if keep_default_na: - v = set(v) | STR_NA_VALUES - - na_values[k] = v - # pandas\io\parsers.py:3404: error: Incompatible types in assignment - # (expression has type "Dict[Any, Any]", variable has type "Set[Any]") - # [assignment] - na_fvalues = { # type: ignore[assignment] - k: _floatify_na_values(v) for k, v in na_values.items() - } - else: - if not is_list_like(na_values): - na_values = [na_values] - na_values = _stringify_na_values(na_values) - if keep_default_na: - na_values = na_values | STR_NA_VALUES - - na_fvalues = _floatify_na_values(na_values) - - return na_values, na_fvalues - - -def _clean_index_names(columns, index_col, unnamed_cols): - if not _is_index_col(index_col): - return None, columns, index_col - - columns = list(columns) - - # In case of no rows and multiindex columns we have to set index_names to - # list of Nones GH#38292 - if not columns: - return [None] * len(index_col), columns, index_col - - cp_cols = list(columns) - index_names = [] - - # don't mutate - index_col = list(index_col) - - for i, c in enumerate(index_col): - if isinstance(c, str): - index_names.append(c) - for j, name in enumerate(cp_cols): - if name == c: - index_col[i] = j - columns.remove(name) - break - else: - name = cp_cols[c] - columns.remove(name) - index_names.append(name) - - # Only clean index names that were placeholders. - for i, name in enumerate(index_names): - if isinstance(name, str) and name in unnamed_cols: - # pandas\io\parsers.py:3445: error: No overload variant of - # "__setitem__" of "list" matches argument types "int", "None" - # [call-overload] - index_names[i] = None # type: ignore[call-overload] - - return index_names, columns, index_col - - -def _get_empty_meta(columns, index_col, index_names, dtype: Optional[DtypeArg] = None): - columns = list(columns) - - # Convert `dtype` to a defaultdict of some kind. - # This will enable us to write `dtype[col_name]` - # without worrying about KeyError issues later on. - if not is_dict_like(dtype): - # if dtype == None, default will be object. - default_dtype = dtype or object - dtype = defaultdict(lambda: default_dtype) - else: - dtype = cast(dict, dtype) - dtype = defaultdict( - lambda: object, - {columns[k] if is_integer(k) else k: v for k, v in dtype.items()}, - ) - - # Even though we have no data, the "index" of the empty DataFrame - # could for example still be an empty MultiIndex. Thus, we need to - # check whether we have any index columns specified, via either: - # - # 1) index_col (column indices) - # 2) index_names (column names) - # - # Both must be non-null to ensure a successful construction. Otherwise, - # we have to create a generic empty Index. - if (index_col is None or index_col is False) or index_names is None: - index = Index([]) - else: - data = [Series([], dtype=dtype[name]) for name in index_names] - index = ensure_index_from_sequences(data, names=index_names) - index_col.sort() - - for i, n in enumerate(index_col): - columns.pop(n - i) - - col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns} - - return index, columns, col_dict - - -def _floatify_na_values(na_values): - # create float versions of the na_values - result = set() - for v in na_values: - try: - v = float(v) - if not np.isnan(v): - result.add(v) - except (TypeError, ValueError, OverflowError): - pass - return result - - -def _stringify_na_values(na_values): - """ return a stringified and numeric for these values """ - result = [] - for x in na_values: - result.append(str(x)) - result.append(x) - try: - v = float(x) - - # we are like 999 here - if v == int(v): - v = int(v) - result.append(f"{v}.0") - result.append(str(v)) - - # pandas\io\parsers.py:3522: error: Argument 1 to "append" of - # "list" has incompatible type "float"; expected "str" [arg-type] - result.append(v) # type: ignore[arg-type] - except (TypeError, ValueError, OverflowError): - pass - try: - # pandas\io\parsers.py:3526: error: Argument 1 to "append" of - # "list" has incompatible type "int"; expected "str" [arg-type] - result.append(int(x)) # type: ignore[arg-type] - except (TypeError, ValueError, OverflowError): - pass - return set(result) - - -def _get_na_values(col, na_values, na_fvalues, keep_default_na): - """ - Get the NaN values for a given column. - - Parameters - ---------- - col : str - The name of the column. - na_values : array-like, dict - The object listing the NaN values as strings. - na_fvalues : array-like, dict - The object listing the NaN values as floats. - keep_default_na : bool - If `na_values` is a dict, and the column is not mapped in the - dictionary, whether to return the default NaN values or the empty set. - - Returns - ------- - nan_tuple : A length-two tuple composed of - - 1) na_values : the string NaN values for that column. - 2) na_fvalues : the float NaN values for that column. - """ - if isinstance(na_values, dict): - if col in na_values: - return na_values[col], na_fvalues[col] - else: - if keep_default_na: - return STR_NA_VALUES, set() - - return set(), set() - else: - return na_values, na_fvalues - - -def _get_col_names(colspec, columns): - colset = set(columns) - colnames = [] - for c in colspec: - if c in colset: - colnames.append(c) - elif isinstance(c, int): - colnames.append(columns[c]) - return colnames - - -class FixedWidthReader(abc.Iterator): - """ - A reader of fixed-width lines. - """ - - def __init__(self, f, colspecs, delimiter, comment, skiprows=None, infer_nrows=100): - self.f = f - self.buffer = None - self.delimiter = "\r\n" + delimiter if delimiter else "\n\r\t " - self.comment = comment - if colspecs == "infer": - self.colspecs = self.detect_colspecs( - infer_nrows=infer_nrows, skiprows=skiprows - ) - else: - self.colspecs = colspecs - - if not isinstance(self.colspecs, (tuple, list)): - raise TypeError( - "column specifications must be a list or tuple, " - f"input was a {type(colspecs).__name__}" - ) - - for colspec in self.colspecs: - if not ( - isinstance(colspec, (tuple, list)) - and len(colspec) == 2 - and isinstance(colspec[0], (int, np.integer, type(None))) - and isinstance(colspec[1], (int, np.integer, type(None))) - ): - raise TypeError( - "Each column specification must be " - "2 element tuple or list of integers" - ) - - def get_rows(self, infer_nrows, skiprows=None): - """ - Read rows from self.f, skipping as specified. - - We distinguish buffer_rows (the first <= infer_nrows - lines) from the rows returned to detect_colspecs - because it's simpler to leave the other locations - with skiprows logic alone than to modify them to - deal with the fact we skipped some rows here as - well. - - Parameters - ---------- - infer_nrows : int - Number of rows to read from self.f, not counting - rows that are skipped. - skiprows: set, optional - Indices of rows to skip. - - Returns - ------- - detect_rows : list of str - A list containing the rows to read. - - """ - if skiprows is None: - skiprows = set() - buffer_rows = [] - detect_rows = [] - for i, row in enumerate(self.f): - if i not in skiprows: - detect_rows.append(row) - buffer_rows.append(row) - if len(detect_rows) >= infer_nrows: - break - self.buffer = iter(buffer_rows) - return detect_rows - - def detect_colspecs(self, infer_nrows=100, skiprows=None): - # Regex escape the delimiters - delimiters = "".join(fr"\{x}" for x in self.delimiter) - pattern = re.compile(f"([^{delimiters}]+)") - rows = self.get_rows(infer_nrows, skiprows) - if not rows: - raise EmptyDataError("No rows from which to infer column width") - max_len = max(map(len, rows)) - mask = np.zeros(max_len + 1, dtype=int) - if self.comment is not None: - rows = [row.partition(self.comment)[0] for row in rows] - for row in rows: - for m in pattern.finditer(row): - mask[m.start() : m.end()] = 1 - shifted = np.roll(mask, 1) - shifted[0] = 0 - edges = np.where((mask ^ shifted) == 1)[0] - edge_pairs = list(zip(edges[::2], edges[1::2])) - return edge_pairs - - def __next__(self): - if self.buffer is not None: - try: - line = next(self.buffer) - except StopIteration: - self.buffer = None - line = next(self.f) - else: - line = next(self.f) - # Note: 'colspecs' is a sequence of half-open intervals. - return [line[fromm:to].strip(self.delimiter) for (fromm, to) in self.colspecs] - - -class FixedWidthFieldParser(PythonParser): - """ - Specialization that Converts fixed-width fields into DataFrames. - See PythonParser for details. - """ - - def __init__(self, f, **kwds): - # Support iterators, convert to a list. - self.colspecs = kwds.pop("colspecs") - self.infer_nrows = kwds.pop("infer_nrows") - PythonParser.__init__(self, f, **kwds) - - def _make_reader(self, f): - self.data = FixedWidthReader( - f, - self.colspecs, - self.delimiter, - self.comment, - self.skiprows, - self.infer_nrows, - ) - - def _remove_empty_lines(self, lines) -> List: - """ - Returns the list of lines without the empty ones. With fixed-width - fields, empty lines become arrays of empty strings. - - See PythonParser._remove_empty_lines. - """ - return [ - line - for line in lines - if any(not isinstance(e, str) or e.strip() for e in line) - ] - - -def _refine_defaults_read( - dialect: Union[str, csv.Dialect], - delimiter: Union[str, object], - delim_whitespace: bool, - engine: str, - sep: Union[str, object], - defaults: Dict[str, Any], -): - """Validate/refine default values of input parameters of read_csv, read_table. - - Parameters - ---------- - dialect : str or csv.Dialect - If provided, this parameter will override values (default or not) for the - following parameters: `delimiter`, `doublequote`, `escapechar`, - `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to - override values, a ParserWarning will be issued. See csv.Dialect - documentation for more details. - delimiter : str or object - Alias for sep. - delim_whitespace : bool - Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be - used as the sep. Equivalent to setting ``sep='\\s+'``. If this option - is set to True, nothing should be passed in for the ``delimiter`` - parameter. - engine : {{'c', 'python'}} - Parser engine to use. The C engine is faster while the python engine is - currently more feature-complete. - sep : str or object - A delimiter provided by the user (str) or a sentinel value, i.e. - pandas._libs.lib.no_default. - defaults: dict - Default values of input parameters. - - Returns - ------- - kwds : dict - Input parameters with correct values. - - Raises - ------ - ValueError : If a delimiter was specified with ``sep`` (or ``delimiter``) and - ``delim_whitespace=True``. - """ - # fix types for sep, delimiter to Union(str, Any) - delim_default = defaults["delimiter"] - kwds: Dict[str, Any] = {} - # gh-23761 - # - # When a dialect is passed, it overrides any of the overlapping - # parameters passed in directly. We don't want to warn if the - # default parameters were passed in (since it probably means - # that the user didn't pass them in explicitly in the first place). - # - # "delimiter" is the annoying corner case because we alias it to - # "sep" before doing comparison to the dialect values later on. - # Thus, we need a flag to indicate that we need to "override" - # the comparison to dialect values by checking if default values - # for BOTH "delimiter" and "sep" were provided. - if dialect is not None: - kwds["sep_override"] = delimiter is None and ( - sep is lib.no_default or sep == delim_default - ) - - # Alias sep -> delimiter. - if delimiter is None: - delimiter = sep - - if delim_whitespace and (delimiter is not lib.no_default): - raise ValueError( - "Specified a delimiter with both sep and " - "delim_whitespace=True; you can only specify one." - ) - - if delimiter is lib.no_default: - # assign default separator value - kwds["delimiter"] = delim_default - else: - kwds["delimiter"] = delimiter - - if engine is not None: - kwds["engine_specified"] = True - else: - kwds["engine"] = "c" - kwds["engine_specified"] = False - - return kwds - - -def _extract_dialect(kwds: Dict[str, Any]) -> Optional[csv.Dialect]: - """ - Extract concrete csv dialect instance. - - Returns - ------- - csv.Dialect or None - """ - if kwds.get("dialect") is None: - return None - - dialect = kwds["dialect"] - if dialect in csv.list_dialects(): - dialect = csv.get_dialect(dialect) - - _validate_dialect(dialect) - - return dialect - - -MANDATORY_DIALECT_ATTRS = ( - "delimiter", - "doublequote", - "escapechar", - "skipinitialspace", - "quotechar", - "quoting", -) - - -def _validate_dialect(dialect: csv.Dialect) -> None: - """ - Validate csv dialect instance. - - Raises - ------ - ValueError - If incorrect dialect is provided. - """ - for param in MANDATORY_DIALECT_ATTRS: - if not hasattr(dialect, param): - raise ValueError(f"Invalid dialect {dialect} provided") - - -def _merge_with_dialect_properties( - dialect: csv.Dialect, - defaults: Dict[str, Any], -) -> Dict[str, Any]: - """ - Merge default kwargs in TextFileReader with dialect parameters. - - Parameters - ---------- - dialect : csv.Dialect - Concrete csv dialect. See csv.Dialect documentation for more details. - defaults : dict - Keyword arguments passed to TextFileReader. - - Returns - ------- - kwds : dict - Updated keyword arguments, merged with dialect parameters. - """ - kwds = defaults.copy() - - for param in MANDATORY_DIALECT_ATTRS: - dialect_val = getattr(dialect, param) - - parser_default = _parser_defaults[param] - provided = kwds.get(param, parser_default) - - # Messages for conflicting values between the dialect - # instance and the actual parameters provided. - conflict_msgs = [] - - # Don't warn if the default parameter was passed in, - # even if it conflicts with the dialect (gh-23761). - if provided != parser_default and provided != dialect_val: - msg = ( - f"Conflicting values for '{param}': '{provided}' was " - f"provided, but the dialect specifies '{dialect_val}'. " - "Using the dialect-specified value." - ) - - # Annoying corner case for not warning about - # conflicts between dialect and delimiter parameter. - # Refer to the outer "_read_" function for more info. - if not (param == "delimiter" and kwds.pop("sep_override", False)): - conflict_msgs.append(msg) - - if conflict_msgs: - warnings.warn("\n\n".join(conflict_msgs), ParserWarning, stacklevel=2) - kwds[param] = dialect_val - return kwds - - -def _validate_skipfooter(kwds: Dict[str, Any]) -> None: - """ - Check whether skipfooter is compatible with other kwargs in TextFileReader. - - Parameters - ---------- - kwds : dict - Keyword arguments passed to TextFileReader. - - Raises - ------ - ValueError - If skipfooter is not compatible with other parameters. - """ - if kwds.get("skipfooter"): - if kwds.get("iterator") or kwds.get("chunksize"): - raise ValueError("'skipfooter' not supported for iteration") - if kwds.get("nrows"): - raise ValueError("'skipfooter' not supported with 'nrows'") diff --git a/pandas/io/parsers/__init__.py b/pandas/io/parsers/__init__.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/__init__.py @@ -0,0 +1,9 @@ +from pandas.io.parsers.readers import ( + TextFileReader, + TextParser, + read_csv, + read_fwf, + read_table, +) + +__all__ = ["TextFileReader", "TextParser", "read_csv", "read_fwf", "read_table"] diff --git a/pandas/io/parsers/base_parser.py b/pandas/io/parsers/base_parser.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/base_parser.py @@ -0,0 +1,1085 @@ +from collections import defaultdict +import csv +import datetime +import itertools +from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Union, cast +import warnings + +import numpy as np + +import pandas._libs.lib as lib +import pandas._libs.ops as libops +import pandas._libs.parsers as parsers +from pandas._libs.parsers import STR_NA_VALUES +from pandas._libs.tslibs import parsing +from pandas._typing import DtypeArg, FilePathOrBuffer +from pandas.errors import ParserError, ParserWarning + +from pandas.core.dtypes.cast import astype_nansafe +from pandas.core.dtypes.common import ( + ensure_object, + ensure_str, + is_bool_dtype, + is_categorical_dtype, + is_dict_like, + is_dtype_equal, + is_extension_array_dtype, + is_integer, + is_integer_dtype, + is_list_like, + is_object_dtype, + is_scalar, + is_string_dtype, + pandas_dtype, +) +from pandas.core.dtypes.dtypes import CategoricalDtype +from pandas.core.dtypes.missing import isna + +from pandas.core import algorithms +from pandas.core.arrays import Categorical +from pandas.core.indexes.api import Index, MultiIndex, ensure_index_from_sequences +from pandas.core.series import Series +from pandas.core.tools import datetimes as tools + +from pandas.io.common import IOHandles, get_handle +from pandas.io.date_converters import generic_parser + +parser_defaults = { + "delimiter": None, + "escapechar": None, + "quotechar": '"', + "quoting": csv.QUOTE_MINIMAL, + "doublequote": True, + "skipinitialspace": False, + "lineterminator": None, + "header": "infer", + "index_col": None, + "names": None, + "prefix": None, + "skiprows": None, + "skipfooter": 0, + "nrows": None, + "na_values": None, + "keep_default_na": True, + "true_values": None, + "false_values": None, + "converters": None, + "dtype": None, + "cache_dates": True, + "thousands": None, + "comment": None, + "decimal": ".", + # 'engine': 'c', + "parse_dates": False, + "keep_date_col": False, + "dayfirst": False, + "date_parser": None, + "usecols": None, + # 'iterator': False, + "chunksize": None, + "verbose": False, + "encoding": None, + "squeeze": False, + "compression": None, + "mangle_dupe_cols": True, + "infer_datetime_format": False, + "skip_blank_lines": True, +} + + +class ParserBase: + def __init__(self, kwds): + + self.names = kwds.get("names") + self.orig_names: Optional[List] = None + self.prefix = kwds.pop("prefix", None) + + self.index_col = kwds.get("index_col", None) + self.unnamed_cols: Set = set() + self.index_names: Optional[List] = None + self.col_names = None + + self.parse_dates = _validate_parse_dates_arg(kwds.pop("parse_dates", False)) + self.date_parser = kwds.pop("date_parser", None) + self.dayfirst = kwds.pop("dayfirst", False) + self.keep_date_col = kwds.pop("keep_date_col", False) + + self.na_values = kwds.get("na_values") + self.na_fvalues = kwds.get("na_fvalues") + self.na_filter = kwds.get("na_filter", False) + self.keep_default_na = kwds.get("keep_default_na", True) + + self.true_values = kwds.get("true_values") + self.false_values = kwds.get("false_values") + self.mangle_dupe_cols = kwds.get("mangle_dupe_cols", True) + self.infer_datetime_format = kwds.pop("infer_datetime_format", False) + self.cache_dates = kwds.pop("cache_dates", True) + + self._date_conv = _make_date_converter( + date_parser=self.date_parser, + dayfirst=self.dayfirst, + infer_datetime_format=self.infer_datetime_format, + cache_dates=self.cache_dates, + ) + + # validate header options for mi + self.header = kwds.get("header") + if isinstance(self.header, (list, tuple, np.ndarray)): + if not all(map(is_integer, self.header)): + raise ValueError("header must be integer or list of integers") + if any(i < 0 for i in self.header): + raise ValueError( + "cannot specify multi-index header with negative integers" + ) + if kwds.get("usecols"): + raise ValueError( + "cannot specify usecols when specifying a multi-index header" + ) + if kwds.get("names"): + raise ValueError( + "cannot specify names when specifying a multi-index header" + ) + + # validate index_col that only contains integers + if self.index_col is not None: + is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray)) + if not ( + is_sequence + and all(map(is_integer, self.index_col)) + or is_integer(self.index_col) + ): + raise ValueError( + "index_col must only contain row numbers " + "when specifying a multi-index header" + ) + elif self.header is not None: + # GH 27394 + if self.prefix is not None: + raise ValueError( + "Argument prefix must be None if argument header is not None" + ) + # GH 16338 + elif not is_integer(self.header): + raise ValueError("header must be integer or list of integers") + # GH 27779 + elif self.header < 0: + raise ValueError( + "Passing negative integer to header is invalid. " + "For no header, use header=None instead" + ) + + self._name_processed = False + + self._first_chunk = True + + self.handles: Optional[IOHandles] = None + + def _open_handles(self, src: FilePathOrBuffer, kwds: Dict[str, Any]) -> None: + """ + Let the readers open IOHanldes after they are done with their potential raises. + """ + self.handles = get_handle( + src, + "r", + encoding=kwds.get("encoding", None), + compression=kwds.get("compression", None), + memory_map=kwds.get("memory_map", False), + storage_options=kwds.get("storage_options", None), + ) + + def _validate_parse_dates_presence(self, columns: List[str]) -> None: + """ + Check if parse_dates are in columns. + + If user has provided names for parse_dates, check if those columns + are available. + + Parameters + ---------- + columns : list + List of names of the dataframe. + + Raises + ------ + ValueError + If column to parse_date is not in dataframe. + + """ + cols_needed: Iterable + if is_dict_like(self.parse_dates): + cols_needed = itertools.chain(*self.parse_dates.values()) + elif is_list_like(self.parse_dates): + # a column in parse_dates could be represented + # ColReference = Union[int, str] + # DateGroups = List[ColReference] + # ParseDates = Union[DateGroups, List[DateGroups], + # Dict[ColReference, DateGroups]] + cols_needed = itertools.chain.from_iterable( + col if is_list_like(col) else [col] for col in self.parse_dates + ) + else: + cols_needed = [] + + # get only columns that are references using names (str), not by index + missing_cols = ", ".join( + sorted( + { + col + for col in cols_needed + if isinstance(col, str) and col not in columns + } + ) + ) + if missing_cols: + raise ValueError( + f"Missing column provided to 'parse_dates': '{missing_cols}'" + ) + + def close(self): + if self.handles is not None: + self.handles.close() + + @property + def _has_complex_date_col(self): + return isinstance(self.parse_dates, dict) or ( + isinstance(self.parse_dates, list) + and len(self.parse_dates) > 0 + and isinstance(self.parse_dates[0], list) + ) + + def _should_parse_dates(self, i): + if isinstance(self.parse_dates, bool): + return self.parse_dates + else: + if self.index_names is not None: + name = self.index_names[i] + else: + name = None + j = i if self.index_col is None else self.index_col[i] + + if is_scalar(self.parse_dates): + return (j == self.parse_dates) or ( + name is not None and name == self.parse_dates + ) + else: + return (j in self.parse_dates) or ( + name is not None and name in self.parse_dates + ) + + def _extract_multi_indexer_columns( + self, header, index_names, col_names, passed_names=False + ): + """ + extract and return the names, index_names, col_names + header is a list-of-lists returned from the parsers + """ + if len(header) < 2: + return header[0], index_names, col_names, passed_names + + # the names are the tuples of the header that are not the index cols + # 0 is the name of the index, assuming index_col is a list of column + # numbers + ic = self.index_col + if ic is None: + ic = [] + + if not isinstance(ic, (list, tuple, np.ndarray)): + ic = [ic] + sic = set(ic) + + # clean the index_names + index_names = header.pop(-1) + index_names, _, _ = self._clean_index_names( + index_names, self.index_col, self.unnamed_cols + ) + + # extract the columns + field_count = len(header[0]) + + def extract(r): + return tuple(r[i] for i in range(field_count) if i not in sic) + + columns = list(zip(*(extract(r) for r in header))) + names = ic + columns + + # If we find unnamed columns all in a single + # level, then our header was too long. + for n in range(len(columns[0])): + if all(ensure_str(col[n]) in self.unnamed_cols for col in columns): + header = ",".join(str(x) for x in self.header) + raise ParserError( + f"Passed header=[{header}] are too many rows " + "for this multi_index of columns" + ) + + # Clean the column names (if we have an index_col). + if len(ic): + col_names = [ + r[0] if ((r[0] is not None) and r[0] not in self.unnamed_cols) else None + for r in header + ] + else: + col_names = [None] * len(header) + + passed_names = True + + return names, index_names, col_names, passed_names + + def _maybe_dedup_names(self, names): + # see gh-7160 and gh-9424: this helps to provide + # immediate alleviation of the duplicate names + # issue and appears to be satisfactory to users, + # but ultimately, not needing to butcher the names + # would be nice! + if self.mangle_dupe_cols: + names = list(names) # so we can index + # pandas\io\parsers.py:1559: error: Need type annotation for + # 'counts' [var-annotated] + counts = defaultdict(int) # type: ignore[var-annotated] + is_potential_mi = _is_potential_multi_index(names, self.index_col) + + for i, col in enumerate(names): + cur_count = counts[col] + + while cur_count > 0: + counts[col] = cur_count + 1 + + if is_potential_mi: + col = col[:-1] + (f"{col[-1]}.{cur_count}",) + else: + col = f"{col}.{cur_count}" + cur_count = counts[col] + + names[i] = col + counts[col] = cur_count + 1 + + return names + + def _maybe_make_multi_index_columns(self, columns, col_names=None): + # possibly create a column mi here + if _is_potential_multi_index(columns): + columns = MultiIndex.from_tuples(columns, names=col_names) + return columns + + def _make_index(self, data, alldata, columns, indexnamerow=False): + if not is_index_col(self.index_col) or not self.index_col: + index = None + + elif not self._has_complex_date_col: + index = self._get_simple_index(alldata, columns) + index = self._agg_index(index) + elif self._has_complex_date_col: + if not self._name_processed: + (self.index_names, _, self.index_col) = self._clean_index_names( + list(columns), self.index_col, self.unnamed_cols + ) + self._name_processed = True + index = self._get_complex_date_index(data, columns) + index = self._agg_index(index, try_parse_dates=False) + + # add names for the index + if indexnamerow: + coffset = len(indexnamerow) - len(columns) + # pandas\io\parsers.py:1604: error: Item "None" of "Optional[Any]" + # has no attribute "set_names" [union-attr] + index = index.set_names(indexnamerow[:coffset]) # type: ignore[union-attr] + + # maybe create a mi on the columns + columns = self._maybe_make_multi_index_columns(columns, self.col_names) + + return index, columns + + _implicit_index = False + + def _get_simple_index(self, data, columns): + def ix(col): + if not isinstance(col, str): + return col + raise ValueError(f"Index {col} invalid") + + to_remove = [] + index = [] + for idx in self.index_col: + i = ix(idx) + to_remove.append(i) + index.append(data[i]) + + # remove index items from content and columns, don't pop in + # loop + for i in sorted(to_remove, reverse=True): + data.pop(i) + if not self._implicit_index: + columns.pop(i) + + return index + + def _get_complex_date_index(self, data, col_names): + def _get_name(icol): + if isinstance(icol, str): + return icol + + if col_names is None: + raise ValueError(f"Must supply column order to use {icol!s} as index") + + for i, c in enumerate(col_names): + if i == icol: + return c + + to_remove = [] + index = [] + for idx in self.index_col: + name = _get_name(idx) + to_remove.append(name) + index.append(data[name]) + + # remove index items from content and columns, don't pop in + # loop + for c in sorted(to_remove, reverse=True): + data.pop(c) + col_names.remove(c) + + return index + + def _agg_index(self, index, try_parse_dates=True) -> Index: + arrays = [] + + for i, arr in enumerate(index): + + if try_parse_dates and self._should_parse_dates(i): + arr = self._date_conv(arr) + + if self.na_filter: + col_na_values = self.na_values + col_na_fvalues = self.na_fvalues + else: + col_na_values = set() + col_na_fvalues = set() + + if isinstance(self.na_values, dict): + # pandas\io\parsers.py:1678: error: Value of type + # "Optional[Any]" is not indexable [index] + col_name = self.index_names[i] # type: ignore[index] + if col_name is not None: + col_na_values, col_na_fvalues = _get_na_values( + col_name, self.na_values, self.na_fvalues, self.keep_default_na + ) + + arr, _ = self._infer_types(arr, col_na_values | col_na_fvalues) + arrays.append(arr) + + names = self.index_names + index = ensure_index_from_sequences(arrays, names) + + return index + + def _convert_to_ndarrays( + self, dct, na_values, na_fvalues, verbose=False, converters=None, dtypes=None + ): + result = {} + for c, values in dct.items(): + conv_f = None if converters is None else converters.get(c, None) + if isinstance(dtypes, dict): + cast_type = dtypes.get(c, None) + else: + # single dtype or None + cast_type = dtypes + + if self.na_filter: + col_na_values, col_na_fvalues = _get_na_values( + c, na_values, na_fvalues, self.keep_default_na + ) + else: + col_na_values, col_na_fvalues = set(), set() + + if conv_f is not None: + # conv_f applied to data before inference + if cast_type is not None: + warnings.warn( + ( + "Both a converter and dtype were specified " + f"for column {c} - only the converter will be used" + ), + ParserWarning, + stacklevel=7, + ) + + try: + values = lib.map_infer(values, conv_f) + except ValueError: + mask = algorithms.isin(values, list(na_values)).view(np.uint8) + values = lib.map_infer_mask(values, conv_f, mask) + + cvals, na_count = self._infer_types( + values, set(col_na_values) | col_na_fvalues, try_num_bool=False + ) + else: + is_ea = is_extension_array_dtype(cast_type) + is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type) + # skip inference if specified dtype is object + # or casting to an EA + try_num_bool = not (cast_type and is_str_or_ea_dtype) + + # general type inference and conversion + cvals, na_count = self._infer_types( + values, set(col_na_values) | col_na_fvalues, try_num_bool + ) + + # type specified in dtype param or cast_type is an EA + if cast_type and ( + not is_dtype_equal(cvals, cast_type) + or is_extension_array_dtype(cast_type) + ): + if not is_ea and na_count > 0: + try: + if is_bool_dtype(cast_type): + raise ValueError( + f"Bool column has NA values in column {c}" + ) + except (AttributeError, TypeError): + # invalid input to is_bool_dtype + pass + cast_type = pandas_dtype(cast_type) + cvals = self._cast_types(cvals, cast_type, c) + + result[c] = cvals + if verbose and na_count: + print(f"Filled {na_count} NA values in column {c!s}") + return result + + def _infer_types(self, values, na_values, try_num_bool=True): + """ + Infer types of values, possibly casting + + Parameters + ---------- + values : ndarray + na_values : set + try_num_bool : bool, default try + try to cast values to numeric (first preference) or boolean + + Returns + ------- + converted : ndarray + na_count : int + """ + na_count = 0 + if issubclass(values.dtype.type, (np.number, np.bool_)): + mask = algorithms.isin(values, list(na_values)) + na_count = mask.sum() + if na_count > 0: + if is_integer_dtype(values): + values = values.astype(np.float64) + np.putmask(values, mask, np.nan) + return values, na_count + + if try_num_bool and is_object_dtype(values.dtype): + # exclude e.g DatetimeIndex here + try: + result = lib.maybe_convert_numeric(values, na_values, False) + except (ValueError, TypeError): + # e.g. encountering datetime string gets ValueError + # TypeError can be raised in floatify + result = values + na_count = parsers.sanitize_objects(result, na_values, False) + else: + na_count = isna(result).sum() + else: + result = values + if values.dtype == np.object_: + na_count = parsers.sanitize_objects(values, na_values, False) + + if result.dtype == np.object_ and try_num_bool: + result = libops.maybe_convert_bool( + np.asarray(values), + true_values=self.true_values, + false_values=self.false_values, + ) + + return result, na_count + + def _cast_types(self, values, cast_type, column): + """ + Cast values to specified type + + Parameters + ---------- + values : ndarray + cast_type : string or np.dtype + dtype to cast values to + column : string + column name - used only for error reporting + + Returns + ------- + converted : ndarray + """ + if is_categorical_dtype(cast_type): + known_cats = ( + isinstance(cast_type, CategoricalDtype) + and cast_type.categories is not None + ) + + if not is_object_dtype(values) and not known_cats: + # TODO: this is for consistency with + # c-parser which parses all categories + # as strings + values = astype_nansafe(values, str) + + cats = Index(values).unique().dropna() + values = Categorical._from_inferred_categories( + cats, cats.get_indexer(values), cast_type, true_values=self.true_values + ) + + # use the EA's implementation of casting + elif is_extension_array_dtype(cast_type): + # ensure cast_type is an actual dtype and not a string + cast_type = pandas_dtype(cast_type) + array_type = cast_type.construct_array_type() + try: + if is_bool_dtype(cast_type): + return array_type._from_sequence_of_strings( + values, + dtype=cast_type, + true_values=self.true_values, + false_values=self.false_values, + ) + else: + return array_type._from_sequence_of_strings(values, dtype=cast_type) + except NotImplementedError as err: + raise NotImplementedError( + f"Extension Array: {array_type} must implement " + "_from_sequence_of_strings in order to be used in parser methods" + ) from err + + else: + try: + values = astype_nansafe(values, cast_type, copy=True, skipna=True) + except ValueError as err: + raise ValueError( + f"Unable to convert column {column} to type {cast_type}" + ) from err + return values + + def _do_date_conversions(self, names, data): + # returns data, columns + + if self.parse_dates is not None: + data, names = _process_date_conversion( + data, + self._date_conv, + self.parse_dates, + self.index_col, + self.index_names, + names, + keep_date_col=self.keep_date_col, + ) + + return names, data + + def _evaluate_usecols(self, usecols, names): + """ + Check whether or not the 'usecols' parameter + is a callable. If so, enumerates the 'names' + parameter and returns a set of indices for + each entry in 'names' that evaluates to True. + If not a callable, returns 'usecols'. + """ + if callable(usecols): + return {i for i, name in enumerate(names) if usecols(name)} + return usecols + + def _validate_usecols_names(self, usecols, names): + """ + Validates that all usecols are present in a given + list of names. If not, raise a ValueError that + shows what usecols are missing. + + Parameters + ---------- + usecols : iterable of usecols + The columns to validate are present in names. + names : iterable of names + The column names to check against. + + Returns + ------- + usecols : iterable of usecols + The `usecols` parameter if the validation succeeds. + + Raises + ------ + ValueError : Columns were missing. Error message will list them. + """ + missing = [c for c in usecols if c not in names] + if len(missing) > 0: + raise ValueError( + f"Usecols do not match columns, columns expected but not found: " + f"{missing}" + ) + + return usecols + + def _validate_usecols_arg(self, usecols): + """ + Validate the 'usecols' parameter. + + Checks whether or not the 'usecols' parameter contains all integers + (column selection by index), strings (column by name) or is a callable. + Raises a ValueError if that is not the case. + + Parameters + ---------- + usecols : list-like, callable, or None + List of columns to use when parsing or a callable that can be used + to filter a list of table columns. + + Returns + ------- + usecols_tuple : tuple + A tuple of (verified_usecols, usecols_dtype). + + 'verified_usecols' is either a set if an array-like is passed in or + 'usecols' if a callable or None is passed in. + + 'usecols_dtype` is the inferred dtype of 'usecols' if an array-like + is passed in or None if a callable or None is passed in. + """ + msg = ( + "'usecols' must either be list-like of all strings, all unicode, " + "all integers or a callable." + ) + if usecols is not None: + if callable(usecols): + return usecols, None + + if not is_list_like(usecols): + # see gh-20529 + # + # Ensure it is iterable container but not string. + raise ValueError(msg) + + usecols_dtype = lib.infer_dtype(usecols, skipna=False) + + if usecols_dtype not in ("empty", "integer", "string"): + raise ValueError(msg) + + usecols = set(usecols) + + return usecols, usecols_dtype + return usecols, None + + def _clean_index_names(self, columns, index_col, unnamed_cols): + if not is_index_col(index_col): + return None, columns, index_col + + columns = list(columns) + + # In case of no rows and multiindex columns we have to set index_names to + # list of Nones GH#38292 + if not columns: + return [None] * len(index_col), columns, index_col + + cp_cols = list(columns) + index_names = [] + + # don't mutate + index_col = list(index_col) + + for i, c in enumerate(index_col): + if isinstance(c, str): + index_names.append(c) + for j, name in enumerate(cp_cols): + if name == c: + index_col[i] = j + columns.remove(name) + break + else: + name = cp_cols[c] + columns.remove(name) + index_names.append(name) + + # Only clean index names that were placeholders. + for i, name in enumerate(index_names): + if isinstance(name, str) and name in unnamed_cols: + # pandas\io\parsers.py:3445: error: No overload variant of + # "__setitem__" of "list" matches argument types "int", "None" + # [call-overload] + index_names[i] = None # type: ignore[call-overload] + + return index_names, columns, index_col + + def _get_empty_meta( + self, columns, index_col, index_names, dtype: Optional[DtypeArg] = None + ): + columns = list(columns) + + # Convert `dtype` to a defaultdict of some kind. + # This will enable us to write `dtype[col_name]` + # without worrying about KeyError issues later on. + if not is_dict_like(dtype): + # if dtype == None, default will be object. + default_dtype = dtype or object + dtype = defaultdict(lambda: default_dtype) + else: + dtype = cast(dict, dtype) + dtype = defaultdict( + lambda: object, + {columns[k] if is_integer(k) else k: v for k, v in dtype.items()}, + ) + + # Even though we have no data, the "index" of the empty DataFrame + # could for example still be an empty MultiIndex. Thus, we need to + # check whether we have any index columns specified, via either: + # + # 1) index_col (column indices) + # 2) index_names (column names) + # + # Both must be non-null to ensure a successful construction. Otherwise, + # we have to create a generic empty Index. + if (index_col is None or index_col is False) or index_names is None: + index = Index([]) + else: + data = [Series([], dtype=dtype[name]) for name in index_names] + index = ensure_index_from_sequences(data, names=index_names) + index_col.sort() + + for i, n in enumerate(index_col): + columns.pop(n - i) + + col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns} + + return index, columns, col_dict + + +def _make_date_converter( + date_parser=None, dayfirst=False, infer_datetime_format=False, cache_dates=True +): + def converter(*date_cols): + if date_parser is None: + strs = parsing.concat_date_cols(date_cols) + + try: + return tools.to_datetime( + ensure_object(strs), + utc=None, + dayfirst=dayfirst, + errors="ignore", + infer_datetime_format=infer_datetime_format, + cache=cache_dates, + ).to_numpy() + + except ValueError: + return tools.to_datetime( + parsing.try_parse_dates(strs, dayfirst=dayfirst), cache=cache_dates + ) + else: + try: + result = tools.to_datetime( + date_parser(*date_cols), errors="ignore", cache=cache_dates + ) + if isinstance(result, datetime.datetime): + raise Exception("scalar parser") + return result + except Exception: + try: + return tools.to_datetime( + parsing.try_parse_dates( + parsing.concat_date_cols(date_cols), + parser=date_parser, + dayfirst=dayfirst, + ), + errors="ignore", + ) + except Exception: + return generic_parser(date_parser, *date_cols) + + return converter + + +def _process_date_conversion( + data_dict, + converter, + parse_spec, + index_col, + index_names, + columns, + keep_date_col=False, +): + def _isindex(colspec): + return (isinstance(index_col, list) and colspec in index_col) or ( + isinstance(index_names, list) and colspec in index_names + ) + + new_cols = [] + new_data = {} + + orig_names = columns + columns = list(columns) + + date_cols = set() + + if parse_spec is None or isinstance(parse_spec, bool): + return data_dict, columns + + if isinstance(parse_spec, list): + # list of column lists + for colspec in parse_spec: + if is_scalar(colspec): + if isinstance(colspec, int) and colspec not in data_dict: + colspec = orig_names[colspec] + if _isindex(colspec): + continue + data_dict[colspec] = converter(data_dict[colspec]) + else: + new_name, col, old_names = _try_convert_dates( + converter, colspec, data_dict, orig_names + ) + if new_name in data_dict: + raise ValueError(f"New date column already in dict {new_name}") + new_data[new_name] = col + new_cols.append(new_name) + date_cols.update(old_names) + + elif isinstance(parse_spec, dict): + # dict of new name to column list + for new_name, colspec in parse_spec.items(): + if new_name in data_dict: + raise ValueError(f"Date column {new_name} already in dict") + + _, col, old_names = _try_convert_dates( + converter, colspec, data_dict, orig_names + ) + + new_data[new_name] = col + new_cols.append(new_name) + date_cols.update(old_names) + + data_dict.update(new_data) + new_cols.extend(columns) + + if not keep_date_col: + for c in list(date_cols): + data_dict.pop(c) + new_cols.remove(c) + + return data_dict, new_cols + + +def _try_convert_dates(parser, colspec, data_dict, columns): + colset = set(columns) + colnames = [] + + for c in colspec: + if c in colset: + colnames.append(c) + elif isinstance(c, int) and c not in columns: + colnames.append(columns[c]) + else: + colnames.append(c) + + new_name = "_".join(str(x) for x in colnames) + to_parse = [data_dict[c] for c in colnames if c in data_dict] + + new_col = parser(*to_parse) + return new_name, new_col, colnames + + +def _get_na_values(col, na_values, na_fvalues, keep_default_na): + """ + Get the NaN values for a given column. + + Parameters + ---------- + col : str + The name of the column. + na_values : array-like, dict + The object listing the NaN values as strings. + na_fvalues : array-like, dict + The object listing the NaN values as floats. + keep_default_na : bool + If `na_values` is a dict, and the column is not mapped in the + dictionary, whether to return the default NaN values or the empty set. + + Returns + ------- + nan_tuple : A length-two tuple composed of + + 1) na_values : the string NaN values for that column. + 2) na_fvalues : the float NaN values for that column. + """ + if isinstance(na_values, dict): + if col in na_values: + return na_values[col], na_fvalues[col] + else: + if keep_default_na: + return STR_NA_VALUES, set() + + return set(), set() + else: + return na_values, na_fvalues + + +# Seems to be unused +def _get_col_names(colspec, columns): + colset = set(columns) + colnames = [] + for c in colspec: + if c in colset: + colnames.append(c) + elif isinstance(c, int): + colnames.append(columns[c]) + return colnames + + +def _is_potential_multi_index( + columns, index_col: Optional[Union[bool, Sequence[int]]] = None +): + """ + Check whether or not the `columns` parameter + could be converted into a MultiIndex. + + Parameters + ---------- + columns : array-like + Object which may or may not be convertible into a MultiIndex + index_col : None, bool or list, optional + Column or columns to use as the (possibly hierarchical) index + + Returns + ------- + boolean : Whether or not columns could become a MultiIndex + """ + if index_col is None or isinstance(index_col, bool): + index_col = [] + + return ( + len(columns) + and not isinstance(columns, MultiIndex) + and all(isinstance(c, tuple) for c in columns if c not in list(index_col)) + ) + + +def _validate_parse_dates_arg(parse_dates): + """ + Check whether or not the 'parse_dates' parameter + is a non-boolean scalar. Raises a ValueError if + that is the case. + """ + msg = ( + "Only booleans, lists, and dictionaries are accepted " + "for the 'parse_dates' parameter" + ) + + if parse_dates is not None: + if is_scalar(parse_dates): + if not lib.is_bool(parse_dates): + raise TypeError(msg) + + elif not isinstance(parse_dates, (list, dict)): + raise TypeError(msg) + + return parse_dates + + +def is_index_col(col): + return col is not None and col is not False diff --git a/pandas/io/parsers/c_parser_wrapper.py b/pandas/io/parsers/c_parser_wrapper.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/c_parser_wrapper.py @@ -0,0 +1,328 @@ +import pandas._libs.parsers as parsers +from pandas._typing import FilePathOrBuffer + +from pandas.core.dtypes.common import is_integer + +from pandas.core.indexes.api import ensure_index_from_sequences + +from pandas.io.parsers.base_parser import ParserBase, is_index_col + + +class CParserWrapper(ParserBase): + def __init__(self, src: FilePathOrBuffer, **kwds): + self.kwds = kwds + kwds = kwds.copy() + + ParserBase.__init__(self, kwds) + + # #2442 + kwds["allow_leading_cols"] = self.index_col is not False + + # GH20529, validate usecol arg before TextReader + self.usecols, self.usecols_dtype = self._validate_usecols_arg(kwds["usecols"]) + kwds["usecols"] = self.usecols + + # open handles + self._open_handles(src, kwds) + assert self.handles is not None + for key in ("storage_options", "encoding", "memory_map", "compression"): + kwds.pop(key, None) + if self.handles.is_mmap and hasattr(self.handles.handle, "mmap"): + # pandas\io\parsers.py:1861: error: Item "IO[Any]" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "RawIOBase" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "BufferedIOBase" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "TextIOBase" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "TextIOWrapper" of + # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, + # TextIOWrapper, mmap]" has no attribute "mmap" [union-attr] + + # pandas\io\parsers.py:1861: error: Item "mmap" of "Union[IO[Any], + # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap]" has + # no attribute "mmap" [union-attr] + self.handles.handle = self.handles.handle.mmap # type: ignore[union-attr] + + try: + self._reader = parsers.TextReader(self.handles.handle, **kwds) + except Exception: + self.handles.close() + raise + self.unnamed_cols = self._reader.unnamed_cols + + passed_names = self.names is None + + if self._reader.header is None: + self.names = None + else: + if len(self._reader.header) > 1: + # we have a multi index in the columns + ( + self.names, + self.index_names, + self.col_names, + passed_names, + ) = self._extract_multi_indexer_columns( + self._reader.header, self.index_names, self.col_names, passed_names + ) + else: + self.names = list(self._reader.header[0]) + + if self.names is None: + if self.prefix: + self.names = [ + f"{self.prefix}{i}" for i in range(self._reader.table_width) + ] + else: + self.names = list(range(self._reader.table_width)) + + # gh-9755 + # + # need to set orig_names here first + # so that proper indexing can be done + # with _set_noconvert_columns + # + # once names has been filtered, we will + # then set orig_names again to names + self.orig_names = self.names[:] + + if self.usecols: + usecols = self._evaluate_usecols(self.usecols, self.orig_names) + + # GH 14671 + # assert for mypy, orig_names is List or None, None would error in issubset + assert self.orig_names is not None + if self.usecols_dtype == "string" and not set(usecols).issubset( + self.orig_names + ): + self._validate_usecols_names(usecols, self.orig_names) + + if len(self.names) > len(usecols): + self.names = [ + n + for i, n in enumerate(self.names) + if (i in usecols or n in usecols) + ] + + if len(self.names) < len(usecols): + self._validate_usecols_names(usecols, self.names) + + self._validate_parse_dates_presence(self.names) + self._set_noconvert_columns() + + self.orig_names = self.names + + if not self._has_complex_date_col: + if self._reader.leading_cols == 0 and is_index_col(self.index_col): + + self._name_processed = True + (index_names, self.names, self.index_col) = self._clean_index_names( + self.names, self.index_col, self.unnamed_cols + ) + + if self.index_names is None: + self.index_names = index_names + + if self._reader.header is None and not passed_names: + # pandas\io\parsers.py:1997: error: Argument 1 to "len" has + # incompatible type "Optional[Any]"; expected "Sized" + # [arg-type] + self.index_names = [None] * len( + self.index_names # type: ignore[arg-type] + ) + + self._implicit_index = self._reader.leading_cols > 0 + + def close(self) -> None: + super().close() + + # close additional handles opened by C parser + try: + self._reader.close() + except ValueError: + pass + + def _set_noconvert_columns(self): + """ + Set the columns that should not undergo dtype conversions. + + Currently, any column that is involved with date parsing will not + undergo such conversions. + """ + names = self.orig_names + if self.usecols_dtype == "integer": + # A set of integers will be converted to a list in + # the correct order every single time. + usecols = list(self.usecols) + usecols.sort() + elif callable(self.usecols) or self.usecols_dtype not in ("empty", None): + # The names attribute should have the correct columns + # in the proper order for indexing with parse_dates. + usecols = self.names[:] + else: + # Usecols is empty. + + # pandas\io\parsers.py:2030: error: Incompatible types in + # assignment (expression has type "None", variable has type + # "List[Any]") [assignment] + usecols = None # type: ignore[assignment] + + def _set(x): + if usecols is not None and is_integer(x): + x = usecols[x] + + if not is_integer(x): + # assert for mypy, names is List or None, None would error when calling + # .index() + assert names is not None + x = names.index(x) + + self._reader.set_noconvert(x) + + if isinstance(self.parse_dates, list): + for val in self.parse_dates: + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif isinstance(self.parse_dates, dict): + for val in self.parse_dates.values(): + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif self.parse_dates: + if isinstance(self.index_col, list): + for k in self.index_col: + _set(k) + elif self.index_col is not None: + _set(self.index_col) + + def set_error_bad_lines(self, status): + self._reader.set_error_bad_lines(int(status)) + + def read(self, nrows=None): + try: + data = self._reader.read(nrows) + except StopIteration: + if self._first_chunk: + self._first_chunk = False + names = self._maybe_dedup_names(self.orig_names) + index, columns, col_dict = self._get_empty_meta( + names, + self.index_col, + self.index_names, + dtype=self.kwds.get("dtype"), + ) + columns = self._maybe_make_multi_index_columns(columns, self.col_names) + + if self.usecols is not None: + columns = self._filter_usecols(columns) + + col_dict = {k: v for k, v in col_dict.items() if k in columns} + + return index, columns, col_dict + + else: + self.close() + raise + + # Done with first read, next time raise StopIteration + self._first_chunk = False + + names = self.names + + if self._reader.leading_cols: + if self._has_complex_date_col: + raise NotImplementedError("file structure not yet supported") + + # implicit index, no index names + arrays = [] + + for i in range(self._reader.leading_cols): + if self.index_col is None: + values = data.pop(i) + else: + values = data.pop(self.index_col[i]) + + values = self._maybe_parse_dates(values, i, try_parse_dates=True) + arrays.append(values) + + index = ensure_index_from_sequences(arrays) + + if self.usecols is not None: + names = self._filter_usecols(names) + + names = self._maybe_dedup_names(names) + + # rename dict keys + data = sorted(data.items()) + data = {k: v for k, (i, v) in zip(names, data)} + + names, data = self._do_date_conversions(names, data) + + else: + # rename dict keys + data = sorted(data.items()) + + # ugh, mutation + + # assert for mypy, orig_names is List or None, None would error in list(...) + assert self.orig_names is not None + names = list(self.orig_names) + names = self._maybe_dedup_names(names) + + if self.usecols is not None: + names = self._filter_usecols(names) + + # columns as list + alldata = [x[1] for x in data] + + data = {k: v for k, (i, v) in zip(names, data)} + + names, data = self._do_date_conversions(names, data) + index, names = self._make_index(data, alldata, names) + + # maybe create a mi on the columns + names = self._maybe_make_multi_index_columns(names, self.col_names) + + return index, names, data + + def _filter_usecols(self, names): + # hackish + usecols = self._evaluate_usecols(self.usecols, names) + if usecols is not None and len(names) != len(usecols): + names = [ + name for i, name in enumerate(names) if i in usecols or name in usecols + ] + return names + + def _get_index_names(self): + names = list(self._reader.header[0]) + idx_names = None + + if self._reader.leading_cols == 0 and self.index_col is not None: + (idx_names, names, self.index_col) = self._clean_index_names( + names, self.index_col, self.unnamed_cols + ) + + return names, idx_names + + def _maybe_parse_dates(self, values, index, try_parse_dates=True): + if try_parse_dates and self._should_parse_dates(index): + values = self._date_conv(values) + return values diff --git a/pandas/io/parsers/python_parser.py b/pandas/io/parsers/python_parser.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/python_parser.py @@ -0,0 +1,1263 @@ +from collections import abc, defaultdict +import csv +from io import StringIO +import re +import sys +from typing import Iterator, List, Optional, cast + +import numpy as np + +import pandas._libs.lib as lib +from pandas._typing import FilePathOrBuffer, Union +from pandas.errors import EmptyDataError, ParserError + +from pandas.core.dtypes.common import is_integer + +from pandas.io.parsers.base_parser import ParserBase, parser_defaults + +# BOM character (byte order mark) +# This exists at the beginning of a file to indicate endianness +# of a file (stream). Unfortunately, this marker screws up parsing, +# so we need to remove it if we see it. +_BOM = "\ufeff" + + +class PythonParser(ParserBase): + def __init__(self, f: Union[FilePathOrBuffer, List], **kwds): + """ + Workhorse function for processing nested list into DataFrame + """ + ParserBase.__init__(self, kwds) + + self.data: Optional[Iterator[str]] = None + self.buf: List = [] + self.pos = 0 + self.line_pos = 0 + + self.skiprows = kwds["skiprows"] + + if callable(self.skiprows): + self.skipfunc = self.skiprows + else: + self.skipfunc = lambda x: x in self.skiprows + + self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"]) + self.delimiter = kwds["delimiter"] + + self.quotechar = kwds["quotechar"] + if isinstance(self.quotechar, str): + self.quotechar = str(self.quotechar) + + self.escapechar = kwds["escapechar"] + self.doublequote = kwds["doublequote"] + self.skipinitialspace = kwds["skipinitialspace"] + self.lineterminator = kwds["lineterminator"] + self.quoting = kwds["quoting"] + self.usecols, _ = self._validate_usecols_arg(kwds["usecols"]) + self.skip_blank_lines = kwds["skip_blank_lines"] + + self.warn_bad_lines = kwds["warn_bad_lines"] + self.error_bad_lines = kwds["error_bad_lines"] + + self.names_passed = kwds["names"] or None + + self.has_index_names = False + if "has_index_names" in kwds: + self.has_index_names = kwds["has_index_names"] + + self.verbose = kwds["verbose"] + self.converters = kwds["converters"] + + self.dtype = kwds["dtype"] + self.thousands = kwds["thousands"] + self.decimal = kwds["decimal"] + + self.comment = kwds["comment"] + + # Set self.data to something that can read lines. + if isinstance(f, list): + # read_excel: f is a list + self.data = cast(Iterator[str], f) + else: + self._open_handles(f, kwds) + assert self.handles is not None + assert hasattr(self.handles.handle, "readline") + try: + self._make_reader(self.handles.handle) + except (csv.Error, UnicodeDecodeError): + self.close() + raise + + # Get columns in two steps: infer from data, then + # infer column indices from self.usecols if it is specified. + self._col_indices: Optional[List[int]] = None + try: + ( + self.columns, + self.num_original_columns, + self.unnamed_cols, + ) = self._infer_columns() + except (TypeError, ValueError): + self.close() + raise + + # Now self.columns has the set of columns that we will process. + # The original set is stored in self.original_columns. + if len(self.columns) > 1: + # we are processing a multi index column + ( + self.columns, + self.index_names, + self.col_names, + _, + ) = self._extract_multi_indexer_columns( + self.columns, self.index_names, self.col_names + ) + # Update list of original names to include all indices. + self.num_original_columns = len(self.columns) + else: + self.columns = self.columns[0] + + # get popped off for index + self.orig_names = list(self.columns) + + # needs to be cleaned/refactored + # multiple date column thing turning into a real spaghetti factory + + if not self._has_complex_date_col: + (index_names, self.orig_names, self.columns) = self._get_index_name( + self.columns + ) + self._name_processed = True + if self.index_names is None: + self.index_names = index_names + + if self._col_indices is None: + self._col_indices = list(range(len(self.columns))) + + self._validate_parse_dates_presence(self.columns) + if self.parse_dates: + self._no_thousands_columns = self._set_no_thousands_columns() + else: + self._no_thousands_columns = None + + if len(self.decimal) != 1: + raise ValueError("Only length-1 decimal markers supported") + + decimal = re.escape(self.decimal) + if self.thousands is None: + regex = fr"^[\-\+]?[0-9]*({decimal}[0-9]*)?([0-9]?(E|e)\-?[0-9]+)?$" + else: + thousands = re.escape(self.thousands) + regex = ( + fr"^[\-\+]?([0-9]+{thousands}|[0-9])*({decimal}[0-9]*)?" + fr"([0-9]?(E|e)\-?[0-9]+)?$" + ) + self.num = re.compile(regex) + + def _set_no_thousands_columns(self): + # Create a set of column ids that are not to be stripped of thousands + # operators. + noconvert_columns = set() + + def _set(x): + if is_integer(x): + noconvert_columns.add(x) + else: + assert self._col_indices is not None + col_indices = self._col_indices + noconvert_columns.add(col_indices[self.columns.index(x)]) + + if isinstance(self.parse_dates, list): + for val in self.parse_dates: + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif isinstance(self.parse_dates, dict): + for val in self.parse_dates.values(): + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif self.parse_dates: + if isinstance(self.index_col, list): + for k in self.index_col: + _set(k) + elif self.index_col is not None: + _set(self.index_col) + + return noconvert_columns + + def _make_reader(self, f): + sep = self.delimiter + + if sep is None or len(sep) == 1: + if self.lineterminator: + raise ValueError( + "Custom line terminators not supported in python parser (yet)" + ) + + class MyDialect(csv.Dialect): + delimiter = self.delimiter + quotechar = self.quotechar + escapechar = self.escapechar + doublequote = self.doublequote + skipinitialspace = self.skipinitialspace + quoting = self.quoting + lineterminator = "\n" + + dia = MyDialect + + if sep is not None: + dia.delimiter = sep + else: + # attempt to sniff the delimiter from the first valid line, + # i.e. no comment line and not in skiprows + line = f.readline() + lines = self._check_comments([[line]])[0] + while self.skipfunc(self.pos) or not lines: + self.pos += 1 + line = f.readline() + lines = self._check_comments([[line]])[0] + + # since `line` was a string, lines will be a list containing + # only a single string + line = lines[0] + + self.pos += 1 + self.line_pos += 1 + sniffed = csv.Sniffer().sniff(line) + dia.delimiter = sniffed.delimiter + + # Note: encoding is irrelevant here + line_rdr = csv.reader(StringIO(line), dialect=dia) + self.buf.extend(list(line_rdr)) + + # Note: encoding is irrelevant here + reader = csv.reader(f, dialect=dia, strict=True) + + else: + + def _read(): + line = f.readline() + pat = re.compile(sep) + + yield pat.split(line.strip()) + + for line in f: + yield pat.split(line.strip()) + + reader = _read() + + # pandas\io\parsers.py:2427: error: Incompatible types in assignment + # (expression has type "_reader", variable has type "Union[IO[Any], + # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap, None]") + # [assignment] + self.data = reader # type: ignore[assignment] + + def read(self, rows=None): + try: + content = self._get_lines(rows) + except StopIteration: + if self._first_chunk: + content = [] + else: + self.close() + raise + + # done with first read, next time raise StopIteration + self._first_chunk = False + + # pandas\io\parsers.py:2480: error: Argument 1 to "list" has + # incompatible type "Optional[Any]"; expected "Iterable[Any]" + # [arg-type] + columns = list(self.orig_names) # type: ignore[arg-type] + if not len(content): # pragma: no cover + # DataFrame with the right metadata, even though it's length 0 + names = self._maybe_dedup_names(self.orig_names) + index, columns, col_dict = self._get_empty_meta( + names, self.index_col, self.index_names, self.dtype + ) + columns = self._maybe_make_multi_index_columns(columns, self.col_names) + return index, columns, col_dict + + # handle new style for names in index + count_empty_content_vals = count_empty_vals(content[0]) + indexnamerow = None + if self.has_index_names and count_empty_content_vals == len(columns): + indexnamerow = content[0] + content = content[1:] + + alldata = self._rows_to_cols(content) + data, columns = self._exclude_implicit_index(alldata) + + columns, data = self._do_date_conversions(columns, data) + + data = self._convert_data(data) + index, columns = self._make_index(data, alldata, columns, indexnamerow) + + return index, columns, data + + def _exclude_implicit_index(self, alldata): + names = self._maybe_dedup_names(self.orig_names) + + offset = 0 + if self._implicit_index: + offset = len(self.index_col) + + if self._col_indices is not None and len(names) != len(self._col_indices): + names = [names[i] for i in sorted(self._col_indices)] + + return {name: alldata[i + offset] for i, name in enumerate(names)}, names + + # legacy + def get_chunk(self, size=None): + if size is None: + # pandas\io\parsers.py:2528: error: "PythonParser" has no attribute + # "chunksize" [attr-defined] + size = self.chunksize # type: ignore[attr-defined] + return self.read(rows=size) + + def _convert_data(self, data): + # apply converters + def _clean_mapping(mapping): + """converts col numbers to names""" + clean = {} + for col, v in mapping.items(): + # pandas\io\parsers.py:2537: error: Unsupported right operand + # type for in ("Optional[Any]") [operator] + if ( + isinstance(col, int) + and col not in self.orig_names # type: ignore[operator] + ): + # pandas\io\parsers.py:2538: error: Value of type + # "Optional[Any]" is not indexable [index] + col = self.orig_names[col] # type: ignore[index] + clean[col] = v + return clean + + clean_conv = _clean_mapping(self.converters) + if not isinstance(self.dtype, dict): + # handles single dtype applied to all columns + clean_dtypes = self.dtype + else: + clean_dtypes = _clean_mapping(self.dtype) + + # Apply NA values. + clean_na_values = {} + clean_na_fvalues = {} + + if isinstance(self.na_values, dict): + for col in self.na_values: + na_value = self.na_values[col] + na_fvalue = self.na_fvalues[col] + + # pandas\io\parsers.py:2558: error: Unsupported right operand + # type for in ("Optional[Any]") [operator] + if ( + isinstance(col, int) + and col not in self.orig_names # type: ignore[operator] + ): + # pandas\io\parsers.py:2559: error: Value of type + # "Optional[Any]" is not indexable [index] + col = self.orig_names[col] # type: ignore[index] + + clean_na_values[col] = na_value + clean_na_fvalues[col] = na_fvalue + else: + clean_na_values = self.na_values + clean_na_fvalues = self.na_fvalues + + return self._convert_to_ndarrays( + data, + clean_na_values, + clean_na_fvalues, + self.verbose, + clean_conv, + clean_dtypes, + ) + + def _infer_columns(self): + names = self.names + num_original_columns = 0 + clear_buffer = True + # pandas\io\parsers.py:2580: error: Need type annotation for + # 'unnamed_cols' (hint: "unnamed_cols: Set[<type>] = ...") + # [var-annotated] + unnamed_cols = set() # type: ignore[var-annotated] + + if self.header is not None: + header = self.header + + if isinstance(header, (list, tuple, np.ndarray)): + have_mi_columns = len(header) > 1 + # we have a mi columns, so read an extra line + if have_mi_columns: + header = list(header) + [header[-1] + 1] + else: + have_mi_columns = False + header = [header] + + # pandas\io\parsers.py:2594: error: Need type annotation for + # 'columns' (hint: "columns: List[<type>] = ...") [var-annotated] + columns = [] # type: ignore[var-annotated] + for level, hr in enumerate(header): + try: + line = self._buffered_line() + + while self.line_pos <= hr: + line = self._next_line() + + except StopIteration as err: + if self.line_pos < hr: + raise ValueError( + f"Passed header={hr} but only {self.line_pos + 1} lines in " + "file" + ) from err + + # We have an empty file, so check + # if columns are provided. That will + # serve as the 'line' for parsing + if have_mi_columns and hr > 0: + if clear_buffer: + self._clear_buffer() + columns.append([None] * len(columns[-1])) + return columns, num_original_columns, unnamed_cols + + if not self.names: + raise EmptyDataError("No columns to parse from file") from err + + line = self.names[:] + + this_columns = [] + this_unnamed_cols = [] + + for i, c in enumerate(line): + if c == "": + if have_mi_columns: + col_name = f"Unnamed: {i}_level_{level}" + else: + col_name = f"Unnamed: {i}" + + this_unnamed_cols.append(i) + this_columns.append(col_name) + else: + this_columns.append(c) + + if not have_mi_columns and self.mangle_dupe_cols: + # pandas\io\parsers.py:2639: error: Need type annotation + # for 'counts' [var-annotated] + counts = defaultdict(int) # type: ignore[var-annotated] + + for i, col in enumerate(this_columns): + cur_count = counts[col] + + while cur_count > 0: + counts[col] = cur_count + 1 + col = f"{col}.{cur_count}" + cur_count = counts[col] + + this_columns[i] = col + counts[col] = cur_count + 1 + elif have_mi_columns: + + # if we have grabbed an extra line, but its not in our + # format so save in the buffer, and create an blank extra + # line for the rest of the parsing code + if hr == header[-1]: + lc = len(this_columns) + ic = len(self.index_col) if self.index_col is not None else 0 + unnamed_count = len(this_unnamed_cols) + + if lc != unnamed_count and lc - ic > unnamed_count: + clear_buffer = False + # pandas\io\parsers.py:2663: error: List item 0 has + # incompatible type "None"; expected "str" + # [list-item] + this_columns = [None] * lc # type: ignore[list-item] + self.buf = [self.buf[-1]] + + # pandas\io\parsers.py:2666: error: Argument 1 to "append" of + # "list" has incompatible type "List[str]"; expected + # "List[None]" [arg-type] + columns.append(this_columns) # type: ignore[arg-type] + unnamed_cols.update({this_columns[i] for i in this_unnamed_cols}) + + if len(columns) == 1: + num_original_columns = len(this_columns) + + if clear_buffer: + self._clear_buffer() + + if names is not None: + if len(names) > len(columns[0]): + raise ValueError( + "Number of passed names did not match " + "number of header fields in the file" + ) + if len(columns) > 1: + raise TypeError("Cannot pass names with multi-index columns") + + if self.usecols is not None: + # Set _use_cols. We don't store columns because they are + # overwritten. + self._handle_usecols(columns, names) + else: + num_original_columns = len(names) + columns = [names] + else: + columns = self._handle_usecols(columns, columns[0]) + else: + try: + line = self._buffered_line() + + except StopIteration as err: + if not names: + raise EmptyDataError("No columns to parse from file") from err + + line = names[:] + + ncols = len(line) + num_original_columns = ncols + + if not names: + if self.prefix: + # pandas\io\parsers.py:2711: error: List comprehension has + # incompatible type List[str]; expected List[None] [misc] + columns = [ + [ + f"{self.prefix}{i}" # type: ignore[misc] + for i in range(ncols) + ] + ] + else: + # pandas\io\parsers.py:2713: error: Argument 1 to "list" + # has incompatible type "range"; expected "Iterable[None]" + # [arg-type] + columns = [list(range(ncols))] # type: ignore[arg-type] + columns = self._handle_usecols(columns, columns[0]) + else: + if self.usecols is None or len(names) >= num_original_columns: + columns = self._handle_usecols([names], names) + num_original_columns = len(names) + else: + if not callable(self.usecols) and len(names) != len(self.usecols): + raise ValueError( + "Number of passed names did not match number of " + "header fields in the file" + ) + # Ignore output but set used columns. + self._handle_usecols([names], names) + columns = [names] + num_original_columns = ncols + + return columns, num_original_columns, unnamed_cols + + def _handle_usecols(self, columns, usecols_key): + """ + Sets self._col_indices + + usecols_key is used if there are string usecols. + """ + if self.usecols is not None: + if callable(self.usecols): + col_indices = self._evaluate_usecols(self.usecols, usecols_key) + elif any(isinstance(u, str) for u in self.usecols): + if len(columns) > 1: + raise ValueError( + "If using multiple headers, usecols must be integers." + ) + col_indices = [] + + for col in self.usecols: + if isinstance(col, str): + try: + col_indices.append(usecols_key.index(col)) + except ValueError: + self._validate_usecols_names(self.usecols, usecols_key) + else: + col_indices.append(col) + else: + col_indices = self.usecols + + columns = [ + [n for i, n in enumerate(column) if i in col_indices] + for column in columns + ] + self._col_indices = sorted(col_indices) + return columns + + def _buffered_line(self): + """ + Return a line from buffer, filling buffer if required. + """ + if len(self.buf) > 0: + return self.buf[0] + else: + return self._next_line() + + def _check_for_bom(self, first_row): + """ + Checks whether the file begins with the BOM character. + If it does, remove it. In addition, if there is quoting + in the field subsequent to the BOM, remove it as well + because it technically takes place at the beginning of + the name, not the middle of it. + """ + # first_row will be a list, so we need to check + # that that list is not empty before proceeding. + if not first_row: + return first_row + + # The first element of this row is the one that could have the + # BOM that we want to remove. Check that the first element is a + # string before proceeding. + if not isinstance(first_row[0], str): + return first_row + + # Check that the string is not empty, as that would + # obviously not have a BOM at the start of it. + if not first_row[0]: + return first_row + + # Since the string is non-empty, check that it does + # in fact begin with a BOM. + first_elt = first_row[0][0] + if first_elt != _BOM: + return first_row + + first_row_bom = first_row[0] + + if len(first_row_bom) > 1 and first_row_bom[1] == self.quotechar: + start = 2 + quote = first_row_bom[1] + end = first_row_bom[2:].index(quote) + 2 + + # Extract the data between the quotation marks + new_row = first_row_bom[start:end] + + # Extract any remaining data after the second + # quotation mark. + if len(first_row_bom) > end + 1: + new_row += first_row_bom[end + 1 :] + + else: + + # No quotation so just remove BOM from first element + new_row = first_row_bom[1:] + return [new_row] + first_row[1:] + + def _is_line_empty(self, line): + """ + Check if a line is empty or not. + + Parameters + ---------- + line : str, array-like + The line of data to check. + + Returns + ------- + boolean : Whether or not the line is empty. + """ + return not line or all(not x for x in line) + + def _next_line(self): + if isinstance(self.data, list): + while self.skipfunc(self.pos): + self.pos += 1 + + while True: + try: + line = self._check_comments([self.data[self.pos]])[0] + self.pos += 1 + # either uncommented or blank to begin with + if not self.skip_blank_lines and ( + self._is_line_empty(self.data[self.pos - 1]) or line + ): + break + elif self.skip_blank_lines: + ret = self._remove_empty_lines([line]) + if ret: + line = ret[0] + break + except IndexError: + raise StopIteration + else: + while self.skipfunc(self.pos): + self.pos += 1 + # assert for mypy, data is Iterator[str] or None, would error in next + assert self.data is not None + next(self.data) + + while True: + orig_line = self._next_iter_line(row_num=self.pos + 1) + self.pos += 1 + + if orig_line is not None: + line = self._check_comments([orig_line])[0] + + if self.skip_blank_lines: + ret = self._remove_empty_lines([line]) + + if ret: + line = ret[0] + break + elif self._is_line_empty(orig_line) or line: + break + + # This was the first line of the file, + # which could contain the BOM at the + # beginning of it. + if self.pos == 1: + line = self._check_for_bom(line) + + self.line_pos += 1 + self.buf.append(line) + return line + + def _alert_malformed(self, msg, row_num): + """ + Alert a user about a malformed row. + + If `self.error_bad_lines` is True, the alert will be `ParserError`. + If `self.warn_bad_lines` is True, the alert will be printed out. + + Parameters + ---------- + msg : The error message to display. + row_num : The row number where the parsing error occurred. + Because this row number is displayed, we 1-index, + even though we 0-index internally. + """ + if self.error_bad_lines: + raise ParserError(msg) + elif self.warn_bad_lines: + base = f"Skipping line {row_num}: " + sys.stderr.write(base + msg + "\n") + + def _next_iter_line(self, row_num): + """ + Wrapper around iterating through `self.data` (CSV source). + + When a CSV error is raised, we check for specific + error messages that allow us to customize the + error message displayed to the user. + + Parameters + ---------- + row_num : The row number of the line being parsed. + """ + try: + # assert for mypy, data is Iterator[str] or None, would error in next + assert self.data is not None + return next(self.data) + except csv.Error as e: + if self.warn_bad_lines or self.error_bad_lines: + msg = str(e) + + if "NULL byte" in msg or "line contains NUL" in msg: + msg = ( + "NULL byte detected. This byte " + "cannot be processed in Python's " + "native csv library at the moment, " + "so please pass in engine='c' instead" + ) + + if self.skipfooter > 0: + reason = ( + "Error could possibly be due to " + "parsing errors in the skipped footer rows " + "(the skipfooter keyword is only applied " + "after Python's csv library has parsed " + "all rows)." + ) + msg += ". " + reason + + self._alert_malformed(msg, row_num) + return None + + def _check_comments(self, lines): + if self.comment is None: + return lines + ret = [] + for line in lines: + rl = [] + for x in line: + if ( + not isinstance(x, str) + or self.comment not in x + or x in self.na_values + ): + rl.append(x) + else: + x = x[: x.find(self.comment)] + if len(x) > 0: + rl.append(x) + break + ret.append(rl) + return ret + + def _remove_empty_lines(self, lines): + """ + Iterate through the lines and remove any that are + either empty or contain only one whitespace value + + Parameters + ---------- + lines : array-like + The array of lines that we are to filter. + + Returns + ------- + filtered_lines : array-like + The same array of lines with the "empty" ones removed. + """ + ret = [] + for line in lines: + # Remove empty lines and lines with only one whitespace value + if ( + len(line) > 1 + or len(line) == 1 + and (not isinstance(line[0], str) or line[0].strip()) + ): + ret.append(line) + return ret + + def _check_thousands(self, lines): + if self.thousands is None: + return lines + + return self._search_replace_num_columns( + lines=lines, search=self.thousands, replace="" + ) + + def _search_replace_num_columns(self, lines, search, replace): + ret = [] + for line in lines: + rl = [] + for i, x in enumerate(line): + if ( + not isinstance(x, str) + or search not in x + or (self._no_thousands_columns and i in self._no_thousands_columns) + or not self.num.search(x.strip()) + ): + rl.append(x) + else: + rl.append(x.replace(search, replace)) + ret.append(rl) + return ret + + def _check_decimal(self, lines): + if self.decimal == parser_defaults["decimal"]: + return lines + + return self._search_replace_num_columns( + lines=lines, search=self.decimal, replace="." + ) + + def _clear_buffer(self): + self.buf = [] + + _implicit_index = False + + def _get_index_name(self, columns): + """ + Try several cases to get lines: + + 0) There are headers on row 0 and row 1 and their + total summed lengths equals the length of the next line. + Treat row 0 as columns and row 1 as indices + 1) Look for implicit index: there are more columns + on row 1 than row 0. If this is true, assume that row + 1 lists index columns and row 0 lists normal columns. + 2) Get index from the columns if it was listed. + """ + orig_names = list(columns) + columns = list(columns) + + try: + line = self._next_line() + except StopIteration: + line = None + + try: + next_line = self._next_line() + except StopIteration: + next_line = None + + # implicitly index_col=0 b/c 1 fewer column names + implicit_first_cols = 0 + if line is not None: + # leave it 0, #2442 + # Case 1 + if self.index_col is not False: + implicit_first_cols = len(line) - self.num_original_columns + + # Case 0 + if next_line is not None: + if len(next_line) == len(line) + self.num_original_columns: + # column and index names on diff rows + self.index_col = list(range(len(line))) + self.buf = self.buf[1:] + + for c in reversed(line): + columns.insert(0, c) + + # Update list of original names to include all indices. + orig_names = list(columns) + self.num_original_columns = len(columns) + return line, orig_names, columns + + if implicit_first_cols > 0: + # Case 1 + self._implicit_index = True + if self.index_col is None: + self.index_col = list(range(implicit_first_cols)) + + index_name = None + + else: + # Case 2 + (index_name, columns_, self.index_col) = self._clean_index_names( + columns, self.index_col, self.unnamed_cols + ) + + return index_name, orig_names, columns + + def _rows_to_cols(self, content): + col_len = self.num_original_columns + + if self._implicit_index: + col_len += len(self.index_col) + + max_len = max(len(row) for row in content) + + # Check that there are no rows with too many + # elements in their row (rows with too few + # elements are padded with NaN). + if max_len > col_len and self.index_col is not False and self.usecols is None: + + footers = self.skipfooter if self.skipfooter else 0 + bad_lines = [] + + iter_content = enumerate(content) + content_len = len(content) + content = [] + + for (i, l) in iter_content: + actual_len = len(l) + + if actual_len > col_len: + if self.error_bad_lines or self.warn_bad_lines: + row_num = self.pos - (content_len - i + footers) + bad_lines.append((row_num, actual_len)) + + if self.error_bad_lines: + break + else: + content.append(l) + + for row_num, actual_len in bad_lines: + msg = ( + f"Expected {col_len} fields in line {row_num + 1}, saw " + f"{actual_len}" + ) + if ( + self.delimiter + and len(self.delimiter) > 1 + and self.quoting != csv.QUOTE_NONE + ): + # see gh-13374 + reason = ( + "Error could possibly be due to quotes being " + "ignored when a multi-char delimiter is used." + ) + msg += ". " + reason + + self._alert_malformed(msg, row_num + 1) + + # see gh-13320 + zipped_content = list(lib.to_object_array(content, min_width=col_len).T) + + if self.usecols: + assert self._col_indices is not None + col_indices = self._col_indices + + if self._implicit_index: + zipped_content = [ + a + for i, a in enumerate(zipped_content) + if ( + i < len(self.index_col) + or i - len(self.index_col) in col_indices + ) + ] + else: + zipped_content = [ + a for i, a in enumerate(zipped_content) if i in col_indices + ] + return zipped_content + + def _get_lines(self, rows=None): + lines = self.buf + new_rows = None + + # already fetched some number + if rows is not None: + # we already have the lines in the buffer + if len(self.buf) >= rows: + new_rows, self.buf = self.buf[:rows], self.buf[rows:] + + # need some lines + else: + rows -= len(self.buf) + + if new_rows is None: + if isinstance(self.data, list): + if self.pos > len(self.data): + raise StopIteration + if rows is None: + new_rows = self.data[self.pos :] + new_pos = len(self.data) + else: + new_rows = self.data[self.pos : self.pos + rows] + new_pos = self.pos + rows + + # Check for stop rows. n.b.: self.skiprows is a set. + if self.skiprows: + new_rows = [ + row + for i, row in enumerate(new_rows) + if not self.skipfunc(i + self.pos) + ] + + lines.extend(new_rows) + self.pos = new_pos + + else: + new_rows = [] + try: + if rows is not None: + for _ in range(rows): + # assert for mypy, data is Iterator[str] or None, would + # error in next + assert self.data is not None + new_rows.append(next(self.data)) + lines.extend(new_rows) + else: + rows = 0 + + while True: + new_row = self._next_iter_line(row_num=self.pos + rows + 1) + rows += 1 + + if new_row is not None: + new_rows.append(new_row) + + except StopIteration: + if self.skiprows: + new_rows = [ + row + for i, row in enumerate(new_rows) + if not self.skipfunc(i + self.pos) + ] + lines.extend(new_rows) + if len(lines) == 0: + raise + self.pos += len(new_rows) + + self.buf = [] + else: + lines = new_rows + + if self.skipfooter: + lines = lines[: -self.skipfooter] + + lines = self._check_comments(lines) + if self.skip_blank_lines: + lines = self._remove_empty_lines(lines) + lines = self._check_thousands(lines) + return self._check_decimal(lines) + + +class FixedWidthReader(abc.Iterator): + """ + A reader of fixed-width lines. + """ + + def __init__(self, f, colspecs, delimiter, comment, skiprows=None, infer_nrows=100): + self.f = f + self.buffer = None + self.delimiter = "\r\n" + delimiter if delimiter else "\n\r\t " + self.comment = comment + if colspecs == "infer": + self.colspecs = self.detect_colspecs( + infer_nrows=infer_nrows, skiprows=skiprows + ) + else: + self.colspecs = colspecs + + if not isinstance(self.colspecs, (tuple, list)): + raise TypeError( + "column specifications must be a list or tuple, " + f"input was a {type(colspecs).__name__}" + ) + + for colspec in self.colspecs: + if not ( + isinstance(colspec, (tuple, list)) + and len(colspec) == 2 + and isinstance(colspec[0], (int, np.integer, type(None))) + and isinstance(colspec[1], (int, np.integer, type(None))) + ): + raise TypeError( + "Each column specification must be " + "2 element tuple or list of integers" + ) + + def get_rows(self, infer_nrows, skiprows=None): + """ + Read rows from self.f, skipping as specified. + + We distinguish buffer_rows (the first <= infer_nrows + lines) from the rows returned to detect_colspecs + because it's simpler to leave the other locations + with skiprows logic alone than to modify them to + deal with the fact we skipped some rows here as + well. + + Parameters + ---------- + infer_nrows : int + Number of rows to read from self.f, not counting + rows that are skipped. + skiprows: set, optional + Indices of rows to skip. + + Returns + ------- + detect_rows : list of str + A list containing the rows to read. + + """ + if skiprows is None: + skiprows = set() + buffer_rows = [] + detect_rows = [] + for i, row in enumerate(self.f): + if i not in skiprows: + detect_rows.append(row) + buffer_rows.append(row) + if len(detect_rows) >= infer_nrows: + break + self.buffer = iter(buffer_rows) + return detect_rows + + def detect_colspecs(self, infer_nrows=100, skiprows=None): + # Regex escape the delimiters + delimiters = "".join(fr"\{x}" for x in self.delimiter) + pattern = re.compile(f"([^{delimiters}]+)") + rows = self.get_rows(infer_nrows, skiprows) + if not rows: + raise EmptyDataError("No rows from which to infer column width") + max_len = max(map(len, rows)) + mask = np.zeros(max_len + 1, dtype=int) + if self.comment is not None: + rows = [row.partition(self.comment)[0] for row in rows] + for row in rows: + for m in pattern.finditer(row): + mask[m.start() : m.end()] = 1 + shifted = np.roll(mask, 1) + shifted[0] = 0 + edges = np.where((mask ^ shifted) == 1)[0] + edge_pairs = list(zip(edges[::2], edges[1::2])) + return edge_pairs + + def __next__(self): + if self.buffer is not None: + try: + line = next(self.buffer) + except StopIteration: + self.buffer = None + line = next(self.f) + else: + line = next(self.f) + # Note: 'colspecs' is a sequence of half-open intervals. + return [line[fromm:to].strip(self.delimiter) for (fromm, to) in self.colspecs] + + +class FixedWidthFieldParser(PythonParser): + """ + Specialization that Converts fixed-width fields into DataFrames. + See PythonParser for details. + """ + + def __init__(self, f, **kwds): + # Support iterators, convert to a list. + self.colspecs = kwds.pop("colspecs") + self.infer_nrows = kwds.pop("infer_nrows") + PythonParser.__init__(self, f, **kwds) + + def _make_reader(self, f): + self.data = FixedWidthReader( + f, + self.colspecs, + self.delimiter, + self.comment, + self.skiprows, + self.infer_nrows, + ) + + def _remove_empty_lines(self, lines) -> List: + """ + Returns the list of lines without the empty ones. With fixed-width + fields, empty lines become arrays of empty strings. + + See PythonParser._remove_empty_lines. + """ + return [ + line + for line in lines + if any(not isinstance(e, str) or e.strip() for e in line) + ] + + +def count_empty_vals(vals) -> int: + return sum(1 for v in vals if v == "" or v is None) + + +def _validate_skipfooter_arg(skipfooter): + """ + Validate the 'skipfooter' parameter. + + Checks whether 'skipfooter' is a non-negative integer. + Raises a ValueError if that is not the case. + + Parameters + ---------- + skipfooter : non-negative integer + The number of rows to skip at the end of the file. + + Returns + ------- + validated_skipfooter : non-negative integer + The original input if the validation succeeds. + + Raises + ------ + ValueError : 'skipfooter' was not a non-negative integer. + """ + if not is_integer(skipfooter): + raise ValueError("skipfooter must be an integer") + + if skipfooter < 0: + raise ValueError("skipfooter cannot be negative") + + return skipfooter diff --git a/pandas/io/parsers/readers.py b/pandas/io/parsers/readers.py new file mode 100644 --- /dev/null +++ b/pandas/io/parsers/readers.py @@ -0,0 +1,1339 @@ +""" +Module contains tools for processing files into DataFrames or other objects +""" + +from collections import abc +import csv +import sys +from textwrap import fill +from typing import Any, Dict, Optional, Set, Type +import warnings + +import numpy as np + +import pandas._libs.lib as lib +from pandas._libs.parsers import STR_NA_VALUES +from pandas._typing import DtypeArg, FilePathOrBuffer, StorageOptions, Union +from pandas.errors import AbstractMethodError, ParserWarning +from pandas.util._decorators import Appender + +from pandas.core.dtypes.common import is_file_like, is_float, is_integer, is_list_like + +from pandas.core import generic +from pandas.core.frame import DataFrame +from pandas.core.indexes.api import RangeIndex + +from pandas.io.common import validate_header_arg +from pandas.io.parsers.base_parser import ParserBase, is_index_col, parser_defaults +from pandas.io.parsers.c_parser_wrapper import CParserWrapper +from pandas.io.parsers.python_parser import FixedWidthFieldParser, PythonParser + +_doc_read_csv_and_table = ( + r""" +{summary} + +Also supports optionally iterating or breaking of the file +into chunks. + +Additional help can be found in the online docs for +`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. + +Parameters +---------- +filepath_or_buffer : str, path object or file-like object + Any valid string path is acceptable. The string could be a URL. Valid + URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is + expected. A local file could be: file://localhost/path/to/table.csv. + + If you want to pass in a path object, pandas accepts any ``os.PathLike``. + + By file-like object, we refer to objects with a ``read()`` method, such as + a file handle (e.g. via builtin ``open`` function) or ``StringIO``. +sep : str, default {_default_sep} + Delimiter to use. If sep is None, the C engine cannot automatically detect + the separator, but the Python parsing engine can, meaning the latter will + be used and automatically detect the separator by Python's builtin sniffer + tool, ``csv.Sniffer``. In addition, separators longer than 1 character and + different from ``'\s+'`` will be interpreted as regular expressions and + will also force the use of the Python parsing engine. Note that regex + delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``. +delimiter : str, default ``None`` + Alias for sep. +header : int, list of int, default 'infer' + Row number(s) to use as the column names, and the start of the + data. Default behavior is to infer the column names: if no names + are passed the behavior is identical to ``header=0`` and column + names are inferred from the first line of the file, if column + names are passed explicitly then the behavior is identical to + ``header=None``. Explicitly pass ``header=0`` to be able to + replace existing names. The header can be a list of integers that + specify row locations for a multi-index on the columns + e.g. [0,1,3]. Intervening rows that are not specified will be + skipped (e.g. 2 in this example is skipped). Note that this + parameter ignores commented lines and empty lines if + ``skip_blank_lines=True``, so ``header=0`` denotes the first line of + data rather than the first line of the file. +names : array-like, optional + List of column names to use. If the file contains a header row, + then you should explicitly pass ``header=0`` to override the column names. + Duplicates in this list are not allowed. +index_col : int, str, sequence of int / str, or False, default ``None`` + Column(s) to use as the row labels of the ``DataFrame``, either given as + string name or column index. If a sequence of int / str is given, a + MultiIndex is used. + + Note: ``index_col=False`` can be used to force pandas to *not* use the first + column as the index, e.g. when you have a malformed file with delimiters at + the end of each line. +usecols : list-like or callable, optional + Return a subset of the columns. If list-like, all elements must either + be positional (i.e. integer indices into the document columns) or strings + that correspond to column names provided either by the user in `names` or + inferred from the document header row(s). For example, a valid list-like + `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``. + Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``. + To instantiate a DataFrame from ``data`` with element order preserved use + ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns + in ``['foo', 'bar']`` order or + ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]`` + for ``['bar', 'foo']`` order. + + If callable, the callable function will be evaluated against the column + names, returning names where the callable function evaluates to True. An + example of a valid callable argument would be ``lambda x: x.upper() in + ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster + parsing time and lower memory usage. +squeeze : bool, default False + If the parsed data only contains one column then return a Series. +prefix : str, optional + Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... +mangle_dupe_cols : bool, default True + Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than + 'X'...'X'. Passing in False will cause data to be overwritten if there + are duplicate names in the columns. +dtype : Type name or dict of column -> type, optional + Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32, + 'c': 'Int64'}} + Use `str` or `object` together with suitable `na_values` settings + to preserve and not interpret dtype. + If converters are specified, they will be applied INSTEAD + of dtype conversion. +engine : {{'c', 'python'}}, optional + Parser engine to use. The C engine is faster while the python engine is + currently more feature-complete. +converters : dict, optional + Dict of functions for converting values in certain columns. Keys can either + be integers or column labels. +true_values : list, optional + Values to consider as True. +false_values : list, optional + Values to consider as False. +skipinitialspace : bool, default False + Skip spaces after delimiter. +skiprows : list-like, int or callable, optional + Line numbers to skip (0-indexed) or number of lines to skip (int) + at the start of the file. + + If callable, the callable function will be evaluated against the row + indices, returning True if the row should be skipped and False otherwise. + An example of a valid callable argument would be ``lambda x: x in [0, 2]``. +skipfooter : int, default 0 + Number of lines at bottom of file to skip (Unsupported with engine='c'). +nrows : int, optional + Number of rows of file to read. Useful for reading pieces of large files. +na_values : scalar, str, list-like, or dict, optional + Additional strings to recognize as NA/NaN. If dict passed, specific + per-column NA values. By default the following values are interpreted as + NaN: '""" + + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ") + + """'. +keep_default_na : bool, default True + Whether or not to include the default NaN values when parsing the data. + Depending on whether `na_values` is passed in, the behavior is as follows: + + * If `keep_default_na` is True, and `na_values` are specified, `na_values` + is appended to the default NaN values used for parsing. + * If `keep_default_na` is True, and `na_values` are not specified, only + the default NaN values are used for parsing. + * If `keep_default_na` is False, and `na_values` are specified, only + the NaN values specified `na_values` are used for parsing. + * If `keep_default_na` is False, and `na_values` are not specified, no + strings will be parsed as NaN. + + Note that if `na_filter` is passed in as False, the `keep_default_na` and + `na_values` parameters will be ignored. +na_filter : bool, default True + Detect missing value markers (empty strings and the value of na_values). In + data without any NAs, passing na_filter=False can improve the performance + of reading a large file. +verbose : bool, default False + Indicate number of NA values placed in non-numeric columns. +skip_blank_lines : bool, default True + If True, skip over blank lines rather than interpreting as NaN values. +parse_dates : bool or list of int or names or list of lists or dict, \ +default False + The behavior is as follows: + + * boolean. If True -> try parsing the index. + * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 + each as a separate date column. + * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as + a single date column. + * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call + result 'foo' + + If a column or index cannot be represented as an array of datetimes, + say because of an unparsable value or a mixture of timezones, the column + or index will be returned unaltered as an object data type. For + non-standard datetime parsing, use ``pd.to_datetime`` after + ``pd.read_csv``. To parse an index or column with a mixture of timezones, + specify ``date_parser`` to be a partially-applied + :func:`pandas.to_datetime` with ``utc=True``. See + :ref:`io.csv.mixed_timezones` for more. + + Note: A fast-path exists for iso8601-formatted dates. +infer_datetime_format : bool, default False + If True and `parse_dates` is enabled, pandas will attempt to infer the + format of the datetime strings in the columns, and if it can be inferred, + switch to a faster method of parsing them. In some cases this can increase + the parsing speed by 5-10x. +keep_date_col : bool, default False + If True and `parse_dates` specifies combining multiple columns then + keep the original columns. +date_parser : function, optional + Function to use for converting a sequence of string columns to an array of + datetime instances. The default uses ``dateutil.parser.parser`` to do the + conversion. Pandas will try to call `date_parser` in three different ways, + advancing to the next if an exception occurs: 1) Pass one or more arrays + (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the + string values from the columns defined by `parse_dates` into a single array + and pass that; and 3) call `date_parser` once for each row using one or + more strings (corresponding to the columns defined by `parse_dates`) as + arguments. +dayfirst : bool, default False + DD/MM format dates, international and European format. +cache_dates : bool, default True + If True, use a cache of unique, converted dates to apply the datetime + conversion. May produce significant speed-up when parsing duplicate + date strings, especially ones with timezone offsets. + + .. versionadded:: 0.25.0 +iterator : bool, default False + Return TextFileReader object for iteration or getting chunks with + ``get_chunk()``. + + .. versionchanged:: 1.2 + + ``TextFileReader`` is a context manager. +chunksize : int, optional + Return TextFileReader object for iteration. + See the `IO Tools docs + <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ + for more information on ``iterator`` and ``chunksize``. + + .. versionchanged:: 1.2 + + ``TextFileReader`` is a context manager. +compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' + For on-the-fly decompression of on-disk data. If 'infer' and + `filepath_or_buffer` is path-like, then detect compression from the + following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no + decompression). If using 'zip', the ZIP file must contain only one data + file to be read in. Set to None for no decompression. +thousands : str, optional + Thousands separator. +decimal : str, default '.' + Character to recognize as decimal point (e.g. use ',' for European data). +lineterminator : str (length 1), optional + Character to break file into lines. Only valid with C parser. +quotechar : str (length 1), optional + The character used to denote the start and end of a quoted item. Quoted + items can include the delimiter and it will be ignored. +quoting : int or csv.QUOTE_* instance, default 0 + Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of + QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). +doublequote : bool, default ``True`` + When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate + whether or not to interpret two consecutive quotechar elements INSIDE a + field as a single ``quotechar`` element. +escapechar : str (length 1), optional + One-character string used to escape other characters. +comment : str, optional + Indicates remainder of line should not be parsed. If found at the beginning + of a line, the line will be ignored altogether. This parameter must be a + single character. Like empty lines (as long as ``skip_blank_lines=True``), + fully commented lines are ignored by the parameter `header` but not by + `skiprows`. For example, if ``comment='#'``, parsing + ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being + treated as the header. +encoding : str, optional + Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python + standard encodings + <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ . +dialect : str or csv.Dialect, optional + If provided, this parameter will override values (default or not) for the + following parameters: `delimiter`, `doublequote`, `escapechar`, + `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to + override values, a ParserWarning will be issued. See csv.Dialect + documentation for more details. +error_bad_lines : bool, default True + Lines with too many fields (e.g. a csv line with too many commas) will by + default cause an exception to be raised, and no DataFrame will be returned. + If False, then these "bad lines" will dropped from the DataFrame that is + returned. +warn_bad_lines : bool, default True + If error_bad_lines is False, and warn_bad_lines is True, a warning for each + "bad line" will be output. +delim_whitespace : bool, default False + Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be + used as the sep. Equivalent to setting ``sep='\\s+'``. If this option + is set to True, nothing should be passed in for the ``delimiter`` + parameter. +low_memory : bool, default True + Internally process the file in chunks, resulting in lower memory use + while parsing, but possibly mixed type inference. To ensure no mixed + types either set False, or specify the type with the `dtype` parameter. + Note that the entire file is read into a single DataFrame regardless, + use the `chunksize` or `iterator` parameter to return the data in chunks. + (Only valid with C parser). +memory_map : bool, default False + If a filepath is provided for `filepath_or_buffer`, map the file object + directly onto memory and access the data directly from there. Using this + option can improve performance because there is no longer any I/O overhead. +float_precision : str, optional + Specifies which converter the C engine should use for floating-point + values. The options are ``None`` or 'high' for the ordinary converter, + 'legacy' for the original lower precision pandas converter, and + 'round_trip' for the round-trip converter. + + .. versionchanged:: 1.2 + +{storage_options} + + .. versionadded:: 1.2 + +Returns +------- +DataFrame or TextParser + A comma-separated values (csv) file is returned as two-dimensional + data structure with labeled axes. + +See Also +-------- +DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. +read_csv : Read a comma-separated values (csv) file into DataFrame. +read_fwf : Read a table of fixed-width formatted lines into DataFrame. + +Examples +-------- +>>> pd.{func_name}('data.csv') # doctest: +SKIP +""" +) + + +_c_parser_defaults = { + "delim_whitespace": False, + "na_filter": True, + "low_memory": True, + "memory_map": False, + "error_bad_lines": True, + "warn_bad_lines": True, + "float_precision": None, +} + +_fwf_defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None} + +_c_unsupported = {"skipfooter"} +_python_unsupported = {"low_memory", "float_precision"} + +_deprecated_defaults: Dict[str, Any] = {} +_deprecated_args: Set[str] = set() + + +def validate_integer(name, val, min_val=0): + """ + Checks whether the 'name' parameter for parsing is either + an integer OR float that can SAFELY be cast to an integer + without losing accuracy. Raises a ValueError if that is + not the case. + + Parameters + ---------- + name : string + Parameter name (used for error reporting) + val : int or float + The value to check + min_val : int + Minimum allowed value (val < min_val will result in a ValueError) + """ + msg = f"'{name:s}' must be an integer >={min_val:d}" + + if val is not None: + if is_float(val): + if int(val) != val: + raise ValueError(msg) + val = int(val) + elif not (is_integer(val) and val >= min_val): + raise ValueError(msg) + + return val + + +def _validate_names(names): + """ + Raise ValueError if the `names` parameter contains duplicates or has an + invalid data type. + + Parameters + ---------- + names : array-like or None + An array containing a list of the names used for the output DataFrame. + + Raises + ------ + ValueError + If names are not unique or are not ordered (e.g. set). + """ + if names is not None: + if len(names) != len(set(names)): + raise ValueError("Duplicate names are not allowed.") + if not ( + is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView) + ): + raise ValueError("Names should be an ordered collection.") + + +def _read(filepath_or_buffer: FilePathOrBuffer, kwds): + """Generic reader of line files.""" + if kwds.get("date_parser", None) is not None: + if isinstance(kwds["parse_dates"], bool): + kwds["parse_dates"] = True + + # Extract some of the arguments (pass chunksize on). + iterator = kwds.get("iterator", False) + chunksize = validate_integer("chunksize", kwds.get("chunksize", None), 1) + nrows = kwds.get("nrows", None) + + # Check for duplicates in names. + _validate_names(kwds.get("names", None)) + + # Create the parser. + parser = TextFileReader(filepath_or_buffer, **kwds) + + if chunksize or iterator: + return parser + + with parser: + return parser.read(nrows) + + +@Appender( + _doc_read_csv_and_table.format( + func_name="read_csv", + summary="Read a comma-separated values (csv) file into DataFrame.", + _default_sep="','", + storage_options=generic._shared_docs["storage_options"], + ) +) +def read_csv( + filepath_or_buffer: FilePathOrBuffer, + sep=lib.no_default, + delimiter=None, + # Column and Index Locations and Names + header="infer", + names=None, + index_col=None, + usecols=None, + squeeze=False, + prefix=None, + mangle_dupe_cols=True, + # General Parsing Configuration + dtype: Optional[DtypeArg] = None, + engine=None, + converters=None, + true_values=None, + false_values=None, + skipinitialspace=False, + skiprows=None, + skipfooter=0, + nrows=None, + # NA and Missing Data Handling + na_values=None, + keep_default_na=True, + na_filter=True, + verbose=False, + skip_blank_lines=True, + # Datetime Handling + parse_dates=False, + infer_datetime_format=False, + keep_date_col=False, + date_parser=None, + dayfirst=False, + cache_dates=True, + # Iteration + iterator=False, + chunksize=None, + # Quoting, Compression, and File Format + compression="infer", + thousands=None, + decimal: str = ".", + lineterminator=None, + quotechar='"', + quoting=csv.QUOTE_MINIMAL, + doublequote=True, + escapechar=None, + comment=None, + encoding=None, + dialect=None, + # Error Handling + error_bad_lines=True, + warn_bad_lines=True, + # Internal + delim_whitespace=False, + low_memory=_c_parser_defaults["low_memory"], + memory_map=False, + float_precision=None, + storage_options: StorageOptions = None, +): + kwds = locals() + del kwds["filepath_or_buffer"] + del kwds["sep"] + + kwds_defaults = _refine_defaults_read( + dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": ","} + ) + kwds.update(kwds_defaults) + + return _read(filepath_or_buffer, kwds) + + +@Appender( + _doc_read_csv_and_table.format( + func_name="read_table", + summary="Read general delimited file into DataFrame.", + _default_sep=r"'\\t' (tab-stop)", + storage_options=generic._shared_docs["storage_options"], + ) +) +def read_table( + filepath_or_buffer: FilePathOrBuffer, + sep=lib.no_default, + delimiter=None, + # Column and Index Locations and Names + header="infer", + names=None, + index_col=None, + usecols=None, + squeeze=False, + prefix=None, + mangle_dupe_cols=True, + # General Parsing Configuration + dtype: Optional[DtypeArg] = None, + engine=None, + converters=None, + true_values=None, + false_values=None, + skipinitialspace=False, + skiprows=None, + skipfooter=0, + nrows=None, + # NA and Missing Data Handling + na_values=None, + keep_default_na=True, + na_filter=True, + verbose=False, + skip_blank_lines=True, + # Datetime Handling + parse_dates=False, + infer_datetime_format=False, + keep_date_col=False, + date_parser=None, + dayfirst=False, + cache_dates=True, + # Iteration + iterator=False, + chunksize=None, + # Quoting, Compression, and File Format + compression="infer", + thousands=None, + decimal: str = ".", + lineterminator=None, + quotechar='"', + quoting=csv.QUOTE_MINIMAL, + doublequote=True, + escapechar=None, + comment=None, + encoding=None, + dialect=None, + # Error Handling + error_bad_lines=True, + warn_bad_lines=True, + # Internal + delim_whitespace=False, + low_memory=_c_parser_defaults["low_memory"], + memory_map=False, + float_precision=None, +): + kwds = locals() + del kwds["filepath_or_buffer"] + del kwds["sep"] + + kwds_defaults = _refine_defaults_read( + dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": "\t"} + ) + kwds.update(kwds_defaults) + + return _read(filepath_or_buffer, kwds) + + +def read_fwf( + filepath_or_buffer: FilePathOrBuffer, + colspecs="infer", + widths=None, + infer_nrows=100, + **kwds, +): + r""" + Read a table of fixed-width formatted lines into DataFrame. + + Also supports optionally iterating or breaking of the file + into chunks. + + Additional help can be found in the `online docs for IO Tools + <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. + + Parameters + ---------- + filepath_or_buffer : str, path object or file-like object + Any valid string path is acceptable. The string could be a URL. Valid + URL schemes include http, ftp, s3, and file. For file URLs, a host is + expected. A local file could be: + ``file://localhost/path/to/table.csv``. + + If you want to pass in a path object, pandas accepts any + ``os.PathLike``. + + By file-like object, we refer to objects with a ``read()`` method, + such as a file handle (e.g. via builtin ``open`` function) + or ``StringIO``. + colspecs : list of tuple (int, int) or 'infer'. optional + A list of tuples giving the extents of the fixed-width + fields of each line as half-open intervals (i.e., [from, to[ ). + String value 'infer' can be used to instruct the parser to try + detecting the column specifications from the first 100 rows of + the data which are not being skipped via skiprows (default='infer'). + widths : list of int, optional + A list of field widths which can be used instead of 'colspecs' if + the intervals are contiguous. + infer_nrows : int, default 100 + The number of rows to consider when letting the parser determine the + `colspecs`. + + .. versionadded:: 0.24.0 + **kwds : optional + Optional keyword arguments can be passed to ``TextFileReader``. + + Returns + ------- + DataFrame or TextParser + A comma-separated values (csv) file is returned as two-dimensional + data structure with labeled axes. + + See Also + -------- + DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. + read_csv : Read a comma-separated values (csv) file into DataFrame. + + Examples + -------- + >>> pd.read_fwf('data.csv') # doctest: +SKIP + """ + # Check input arguments. + if colspecs is None and widths is None: + raise ValueError("Must specify either colspecs or widths") + elif colspecs not in (None, "infer") and widths is not None: + raise ValueError("You must specify only one of 'widths' and 'colspecs'") + + # Compute 'colspecs' from 'widths', if specified. + if widths is not None: + colspecs, col = [], 0 + for w in widths: + colspecs.append((col, col + w)) + col += w + + kwds["colspecs"] = colspecs + kwds["infer_nrows"] = infer_nrows + kwds["engine"] = "python-fwf" + return _read(filepath_or_buffer, kwds) + + +class TextFileReader(abc.Iterator): + """ + + Passed dialect overrides any of the related parser options + + """ + + def __init__(self, f, engine=None, **kwds): + + self.f = f + + if engine is not None: + engine_specified = True + else: + engine = "python" + engine_specified = False + self.engine = engine + self._engine_specified = kwds.get("engine_specified", engine_specified) + + _validate_skipfooter(kwds) + + dialect = _extract_dialect(kwds) + if dialect is not None: + kwds = _merge_with_dialect_properties(dialect, kwds) + + if kwds.get("header", "infer") == "infer": + kwds["header"] = 0 if kwds.get("names") is None else None + + self.orig_options = kwds + + # miscellanea + self._currow = 0 + + options = self._get_options_with_defaults(engine) + options["storage_options"] = kwds.get("storage_options", None) + + self.chunksize = options.pop("chunksize", None) + self.nrows = options.pop("nrows", None) + self.squeeze = options.pop("squeeze", False) + + self._check_file_or_buffer(f, engine) + self.options, self.engine = self._clean_options(options, engine) + + if "has_index_names" in kwds: + self.options["has_index_names"] = kwds["has_index_names"] + + self._engine = self._make_engine(self.engine) + + def close(self): + self._engine.close() + + def _get_options_with_defaults(self, engine): + kwds = self.orig_options + + options = {} + + for argname, default in parser_defaults.items(): + value = kwds.get(argname, default) + + # see gh-12935 + if argname == "mangle_dupe_cols" and not value: + raise ValueError("Setting mangle_dupe_cols=False is not supported yet") + else: + options[argname] = value + + for argname, default in _c_parser_defaults.items(): + if argname in kwds: + value = kwds[argname] + + if engine != "c" and value != default: + if "python" in engine and argname not in _python_unsupported: + pass + elif value == _deprecated_defaults.get(argname, default): + pass + else: + raise ValueError( + f"The {repr(argname)} option is not supported with the " + f"{repr(engine)} engine" + ) + else: + value = _deprecated_defaults.get(argname, default) + options[argname] = value + + if engine == "python-fwf": + # pandas\io\parsers.py:907: error: Incompatible types in assignment + # (expression has type "object", variable has type "Union[int, str, + # None]") [assignment] + for argname, default in _fwf_defaults.items(): # type: ignore[assignment] + options[argname] = kwds.get(argname, default) + + return options + + def _check_file_or_buffer(self, f, engine): + # see gh-16530 + if is_file_like(f) and engine != "c" and not hasattr(f, "__next__"): + # The C engine doesn't need the file-like to have the "__next__" + # attribute. However, the Python engine explicitly calls + # "__next__(...)" when iterating through such an object, meaning it + # needs to have that attribute + raise ValueError( + "The 'python' engine cannot iterate through this file buffer." + ) + + def _clean_options(self, options, engine): + result = options.copy() + + fallback_reason = None + + # C engine not supported yet + if engine == "c": + if options["skipfooter"] > 0: + fallback_reason = "the 'c' engine does not support skipfooter" + engine = "python" + + sep = options["delimiter"] + delim_whitespace = options["delim_whitespace"] + + if sep is None and not delim_whitespace: + if engine == "c": + fallback_reason = ( + "the 'c' engine does not support " + "sep=None with delim_whitespace=False" + ) + engine = "python" + elif sep is not None and len(sep) > 1: + if engine == "c" and sep == r"\s+": + result["delim_whitespace"] = True + del result["delimiter"] + elif engine not in ("python", "python-fwf"): + # wait until regex engine integrated + fallback_reason = ( + "the 'c' engine does not support " + "regex separators (separators > 1 char and " + r"different from '\s+' are interpreted as regex)" + ) + engine = "python" + elif delim_whitespace: + if "python" in engine: + result["delimiter"] = r"\s+" + elif sep is not None: + encodeable = True + encoding = sys.getfilesystemencoding() or "utf-8" + try: + if len(sep.encode(encoding)) > 1: + encodeable = False + except UnicodeDecodeError: + encodeable = False + if not encodeable and engine not in ("python", "python-fwf"): + fallback_reason = ( + f"the separator encoded in {encoding} " + "is > 1 char long, and the 'c' engine " + "does not support such separators" + ) + engine = "python" + + quotechar = options["quotechar"] + if quotechar is not None and isinstance(quotechar, (str, bytes)): + if ( + len(quotechar) == 1 + and ord(quotechar) > 127 + and engine not in ("python", "python-fwf") + ): + fallback_reason = ( + "ord(quotechar) > 127, meaning the " + "quotechar is larger than one byte, " + "and the 'c' engine does not support such quotechars" + ) + engine = "python" + + if fallback_reason and self._engine_specified: + raise ValueError(fallback_reason) + + if engine == "c": + for arg in _c_unsupported: + del result[arg] + + if "python" in engine: + for arg in _python_unsupported: + if fallback_reason and result[arg] != _c_parser_defaults[arg]: + raise ValueError( + "Falling back to the 'python' engine because " + f"{fallback_reason}, but this causes {repr(arg)} to be " + "ignored as it is not supported by the 'python' engine." + ) + del result[arg] + + if fallback_reason: + warnings.warn( + ( + "Falling back to the 'python' engine because " + f"{fallback_reason}; you can avoid this warning by specifying " + "engine='python'." + ), + ParserWarning, + stacklevel=5, + ) + + index_col = options["index_col"] + names = options["names"] + converters = options["converters"] + na_values = options["na_values"] + skiprows = options["skiprows"] + + validate_header_arg(options["header"]) + + for arg in _deprecated_args: + parser_default = _c_parser_defaults[arg] + depr_default = _deprecated_defaults[arg] + if result.get(arg, depr_default) != depr_default: + msg = ( + f"The {arg} argument has been deprecated and will be " + "removed in a future version.\n\n" + ) + warnings.warn(msg, FutureWarning, stacklevel=2) + else: + result[arg] = parser_default + + if index_col is True: + raise ValueError("The value of index_col couldn't be 'True'") + if is_index_col(index_col): + if not isinstance(index_col, (list, tuple, np.ndarray)): + index_col = [index_col] + result["index_col"] = index_col + + names = list(names) if names is not None else names + + # type conversion-related + if converters is not None: + if not isinstance(converters, dict): + raise TypeError( + "Type converters must be a dict or subclass, " + f"input was a {type(converters).__name__}" + ) + else: + converters = {} + + # Converting values to NA + keep_default_na = options["keep_default_na"] + na_values, na_fvalues = _clean_na_values(na_values, keep_default_na) + + # handle skiprows; this is internally handled by the + # c-engine, so only need for python parsers + if engine != "c": + if is_integer(skiprows): + skiprows = list(range(skiprows)) + if skiprows is None: + skiprows = set() + elif not callable(skiprows): + skiprows = set(skiprows) + + # put stuff back + result["names"] = names + result["converters"] = converters + result["na_values"] = na_values + result["na_fvalues"] = na_fvalues + result["skiprows"] = skiprows + + return result, engine + + def __next__(self): + try: + return self.get_chunk() + except StopIteration: + self.close() + raise + + def _make_engine(self, engine="c"): + mapping: Dict[str, Type[ParserBase]] = { + "c": CParserWrapper, + "python": PythonParser, + "python-fwf": FixedWidthFieldParser, + } + if engine not in mapping: + raise ValueError( + f"Unknown engine: {engine} (valid options are {mapping.keys()})" + ) + # error: Too many arguments for "ParserBase" + return mapping[engine](self.f, **self.options) # type: ignore[call-arg] + + def _failover_to_python(self): + raise AbstractMethodError(self) + + def read(self, nrows=None): + nrows = validate_integer("nrows", nrows) + index, columns, col_dict = self._engine.read(nrows) + + if index is None: + if col_dict: + # Any column is actually fine: + new_rows = len(next(iter(col_dict.values()))) + index = RangeIndex(self._currow, self._currow + new_rows) + else: + new_rows = 0 + else: + new_rows = len(index) + + df = DataFrame(col_dict, columns=columns, index=index) + + self._currow += new_rows + + if self.squeeze and len(df.columns) == 1: + return df[df.columns[0]].copy() + return df + + def get_chunk(self, size=None): + if size is None: + size = self.chunksize + if self.nrows is not None: + if self._currow >= self.nrows: + raise StopIteration + size = min(size, self.nrows - self._currow) + return self.read(nrows=size) + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.close() + + +def TextParser(*args, **kwds): + """ + Converts lists of lists/tuples into DataFrames with proper type inference + and optional (e.g. string to datetime) conversion. Also enables iterating + lazily over chunks of large files + + Parameters + ---------- + data : file-like object or list + delimiter : separator character to use + dialect : str or csv.Dialect instance, optional + Ignored if delimiter is longer than 1 character + names : sequence, default + header : int, default 0 + Row to use to parse column labels. Defaults to the first row. Prior + rows will be discarded + index_col : int or list, optional + Column or columns to use as the (possibly hierarchical) index + has_index_names: bool, default False + True if the cols defined in index_col have an index name and are + not in the header. + na_values : scalar, str, list-like, or dict, optional + Additional strings to recognize as NA/NaN. + keep_default_na : bool, default True + thousands : str, optional + Thousands separator + comment : str, optional + Comment out remainder of line + parse_dates : bool, default False + keep_date_col : bool, default False + date_parser : function, optional + skiprows : list of integers + Row numbers to skip + skipfooter : int + Number of line at bottom of file to skip + converters : dict, optional + Dict of functions for converting values in certain columns. Keys can + either be integers or column labels, values are functions that take one + input argument, the cell (not column) content, and return the + transformed content. + encoding : str, optional + Encoding to use for UTF when reading/writing (ex. 'utf-8') + squeeze : bool, default False + returns Series if only one column. + infer_datetime_format: bool, default False + If True and `parse_dates` is True for a column, try to infer the + datetime format based on the first datetime string. If the format + can be inferred, there often will be a large parsing speed-up. + float_precision : str, optional + Specifies which converter the C engine should use for floating-point + values. The options are `None` or `high` for the ordinary converter, + `legacy` for the original lower precision pandas converter, and + `round_trip` for the round-trip converter. + + .. versionchanged:: 1.2 + """ + kwds["engine"] = "python" + return TextFileReader(*args, **kwds) + + +def _clean_na_values(na_values, keep_default_na=True): + + if na_values is None: + if keep_default_na: + na_values = STR_NA_VALUES + else: + na_values = set() + # pandas\io\parsers.py:3387: error: Need type annotation for + # 'na_fvalues' (hint: "na_fvalues: Set[<type>] = ...") [var-annotated] + na_fvalues = set() # type: ignore[var-annotated] + elif isinstance(na_values, dict): + old_na_values = na_values.copy() + na_values = {} # Prevent aliasing. + + # Convert the values in the na_values dictionary + # into array-likes for further use. This is also + # where we append the default NaN values, provided + # that `keep_default_na=True`. + for k, v in old_na_values.items(): + if not is_list_like(v): + v = [v] + + if keep_default_na: + v = set(v) | STR_NA_VALUES + + na_values[k] = v + # pandas\io\parsers.py:3404: error: Incompatible types in assignment + # (expression has type "Dict[Any, Any]", variable has type "Set[Any]") + # [assignment] + na_fvalues = { # type: ignore[assignment] + k: _floatify_na_values(v) for k, v in na_values.items() + } + else: + if not is_list_like(na_values): + na_values = [na_values] + na_values = _stringify_na_values(na_values) + if keep_default_na: + na_values = na_values | STR_NA_VALUES + + na_fvalues = _floatify_na_values(na_values) + + return na_values, na_fvalues + + +def _floatify_na_values(na_values): + # create float versions of the na_values + result = set() + for v in na_values: + try: + v = float(v) + if not np.isnan(v): + result.add(v) + except (TypeError, ValueError, OverflowError): + pass + return result + + +def _stringify_na_values(na_values): + """ return a stringified and numeric for these values """ + result = [] + for x in na_values: + result.append(str(x)) + result.append(x) + try: + v = float(x) + + # we are like 999 here + if v == int(v): + v = int(v) + result.append(f"{v}.0") + result.append(str(v)) + + # pandas\io\parsers.py:3522: error: Argument 1 to "append" of + # "list" has incompatible type "float"; expected "str" [arg-type] + result.append(v) # type: ignore[arg-type] + except (TypeError, ValueError, OverflowError): + pass + try: + # pandas\io\parsers.py:3526: error: Argument 1 to "append" of + # "list" has incompatible type "int"; expected "str" [arg-type] + result.append(int(x)) # type: ignore[arg-type] + except (TypeError, ValueError, OverflowError): + pass + return set(result) + + +def _refine_defaults_read( + dialect: Union[str, csv.Dialect], + delimiter: Union[str, object], + delim_whitespace: bool, + engine: str, + sep: Union[str, object], + defaults: Dict[str, Any], +): + """Validate/refine default values of input parameters of read_csv, read_table. + + Parameters + ---------- + dialect : str or csv.Dialect + If provided, this parameter will override values (default or not) for the + following parameters: `delimiter`, `doublequote`, `escapechar`, + `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to + override values, a ParserWarning will be issued. See csv.Dialect + documentation for more details. + delimiter : str or object + Alias for sep. + delim_whitespace : bool + Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be + used as the sep. Equivalent to setting ``sep='\\s+'``. If this option + is set to True, nothing should be passed in for the ``delimiter`` + parameter. + engine : {{'c', 'python'}} + Parser engine to use. The C engine is faster while the python engine is + currently more feature-complete. + sep : str or object + A delimiter provided by the user (str) or a sentinel value, i.e. + pandas._libs.lib.no_default. + defaults: dict + Default values of input parameters. + + Returns + ------- + kwds : dict + Input parameters with correct values. + + Raises + ------ + ValueError : If a delimiter was specified with ``sep`` (or ``delimiter``) and + ``delim_whitespace=True``. + """ + # fix types for sep, delimiter to Union(str, Any) + delim_default = defaults["delimiter"] + kwds: Dict[str, Any] = {} + # gh-23761 + # + # When a dialect is passed, it overrides any of the overlapping + # parameters passed in directly. We don't want to warn if the + # default parameters were passed in (since it probably means + # that the user didn't pass them in explicitly in the first place). + # + # "delimiter" is the annoying corner case because we alias it to + # "sep" before doing comparison to the dialect values later on. + # Thus, we need a flag to indicate that we need to "override" + # the comparison to dialect values by checking if default values + # for BOTH "delimiter" and "sep" were provided. + if dialect is not None: + kwds["sep_override"] = delimiter is None and ( + sep is lib.no_default or sep == delim_default + ) + + # Alias sep -> delimiter. + if delimiter is None: + delimiter = sep + + if delim_whitespace and (delimiter is not lib.no_default): + raise ValueError( + "Specified a delimiter with both sep and " + "delim_whitespace=True; you can only specify one." + ) + + if delimiter is lib.no_default: + # assign default separator value + kwds["delimiter"] = delim_default + else: + kwds["delimiter"] = delimiter + + if engine is not None: + kwds["engine_specified"] = True + else: + kwds["engine"] = "c" + kwds["engine_specified"] = False + + return kwds + + +def _extract_dialect(kwds: Dict[str, Any]) -> Optional[csv.Dialect]: + """ + Extract concrete csv dialect instance. + + Returns + ------- + csv.Dialect or None + """ + if kwds.get("dialect") is None: + return None + + dialect = kwds["dialect"] + if dialect in csv.list_dialects(): + dialect = csv.get_dialect(dialect) + + _validate_dialect(dialect) + + return dialect + + +MANDATORY_DIALECT_ATTRS = ( + "delimiter", + "doublequote", + "escapechar", + "skipinitialspace", + "quotechar", + "quoting", +) + + +def _validate_dialect(dialect: csv.Dialect) -> None: + """ + Validate csv dialect instance. + + Raises + ------ + ValueError + If incorrect dialect is provided. + """ + for param in MANDATORY_DIALECT_ATTRS: + if not hasattr(dialect, param): + raise ValueError(f"Invalid dialect {dialect} provided") + + +def _merge_with_dialect_properties( + dialect: csv.Dialect, + defaults: Dict[str, Any], +) -> Dict[str, Any]: + """ + Merge default kwargs in TextFileReader with dialect parameters. + + Parameters + ---------- + dialect : csv.Dialect + Concrete csv dialect. See csv.Dialect documentation for more details. + defaults : dict + Keyword arguments passed to TextFileReader. + + Returns + ------- + kwds : dict + Updated keyword arguments, merged with dialect parameters. + """ + kwds = defaults.copy() + + for param in MANDATORY_DIALECT_ATTRS: + dialect_val = getattr(dialect, param) + + parser_default = parser_defaults[param] + provided = kwds.get(param, parser_default) + + # Messages for conflicting values between the dialect + # instance and the actual parameters provided. + conflict_msgs = [] + + # Don't warn if the default parameter was passed in, + # even if it conflicts with the dialect (gh-23761). + if provided != parser_default and provided != dialect_val: + msg = ( + f"Conflicting values for '{param}': '{provided}' was " + f"provided, but the dialect specifies '{dialect_val}'. " + "Using the dialect-specified value." + ) + + # Annoying corner case for not warning about + # conflicts between dialect and delimiter parameter. + # Refer to the outer "_read_" function for more info. + if not (param == "delimiter" and kwds.pop("sep_override", False)): + conflict_msgs.append(msg) + + if conflict_msgs: + warnings.warn("\n\n".join(conflict_msgs), ParserWarning, stacklevel=2) + kwds[param] = dialect_val + return kwds + + +def _validate_skipfooter(kwds: Dict[str, Any]) -> None: + """ + Check whether skipfooter is compatible with other kwargs in TextFileReader. + + Parameters + ---------- + kwds : dict + Keyword arguments passed to TextFileReader. + + Raises + ------ + ValueError + If skipfooter is not compatible with other parameters. + """ + if kwds.get("skipfooter"): + if kwds.get("iterator") or kwds.get("chunksize"): + raise ValueError("'skipfooter' not supported for iteration") + if kwds.get("nrows"): + raise ValueError("'skipfooter' not supported with 'nrows'")

TST/REF: splitting pandas/io/parsers.py into multiple files `pandas/io/parsers.py` is close to 4k LOC (3986 on 1.3 master). Would it be reasonable to split it into multiple files (located, say, in a `pandas/io/parsers` directory)? AFAICT the file contains 4 logical pieces: - `read_...` methods (~700 LOC) - `ParserBase` and related module-level methods ( ~1100 LOC) - `CParser` and related module-level methods (~400LOC) - `PythonParser`, its derived classes and related module-level methods (~1800 LOC) Once #38370 goes in these would be joined by - `PyarrowParser` (~100 LOC right now) IMO this kind of refactor would make the module somewhat easier to grok

sure seems reasonable

2021-01-17T00:38:48Z

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