readers.py

"""
Module contains tools for processing files into DataFrames or other objects
"""
from __future__ import annotations

from collections import abc
import csv
import sys
from textwrap import fill
from typing import Any
import warnings

import numpy as np

import pandas._libs.lib as lib
from pandas._libs.parsers import STR_NA_VALUES
from pandas._typing import (
    ArrayLike,
    DtypeArg,
    FilePathOrBuffer,
    StorageOptions,
)
from pandas.errors import (
    AbstractMethodError,
    ParserWarning,
)
from pandas.util._decorators import (
    Appender,
    deprecate_nonkeyword_arguments,
)
from pandas.util._validators import validate_bool_kwarg

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>`_ .

    .. versionchanged:: 1.2

       When ``encoding`` is ``None``, ``errors="replace"`` is passed to
       ``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``.
       This behavior was previously only the case for ``engine="python"``.

    .. versionchanged:: 1.3.0

       ``encoding_errors`` is a new argument. ``encoding`` has no longer an
       influence on how encoding errors are handled.

encoding_errors : str, optional, default "strict"
    How encoding errors are treated. `List of possible values
    <https://docs.python.org/3/library/codecs.html#error-handlers>`_ .

    .. versionadded:: 1.3.0

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 ``None``
    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 be dropped from the DataFrame that is
    returned.

    .. deprecated:: 1.3.0
       The ``on_bad_lines`` parameter should be used instead to specify behavior upon
       encountering a bad line instead.
warn_bad_lines : bool, default ``None``
    If error_bad_lines is False, and warn_bad_lines is True, a warning for each
    "bad line" will be output.

    .. deprecated:: 1.3.0
       The ``on_bad_lines`` parameter should be used instead to specify behavior upon
       encountering a bad line instead.
on_bad_lines : {{'error', 'warn', 'skip'}}, default 'error'
    Specifies what to do upon encountering a bad line (a line with too many fields).
    Allowed values are :

        - 'error', raise an Exception when a bad line is encountered.
        - 'warn', raise a warning when a bad line is encountered and skip that line.
        - 'skip', skip bad lines without raising or warning when they are encountered.

    .. versionadded:: 1.3.0

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": None,
    "warn_bad_lines": None,
    "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] = {"error_bad_lines": None, "warn_bad_lines": None}
_deprecated_args: set[str] = {"error_bad_lines", "warn_bad_lines"}


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 : str
        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)


@deprecate_nonkeyword_arguments(
    version=None, allowed_args=["filepath_or_buffer"], stacklevel=3
)
@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=lib.no_default,
    index_col=None,
    usecols=None,
    squeeze=False,
    prefix=lib.no_default,
    mangle_dupe_cols=True,
    # General Parsing Configuration
    dtype: DtypeArg | None = 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,
    encoding_errors: str | None = "strict",
    dialect=None,
    # Error Handling
    error_bad_lines=None,
    warn_bad_lines=None,
    # TODO (2.0): set on_bad_lines to "error".
    # See _refine_defaults_read comment for why we do this.
    on_bad_lines=None,
    # Internal
    delim_whitespace=False,
    low_memory=_c_parser_defaults["low_memory"],
    memory_map=False,
    float_precision=None,
    storage_options: StorageOptions = None,
):
    # locals() should never be modified
    kwds = locals().copy()
    del kwds["filepath_or_buffer"]
    del kwds["sep"]

    kwds_defaults = _refine_defaults_read(
        dialect,
        delimiter,
        delim_whitespace,
        engine,
        sep,
        error_bad_lines,
        warn_bad_lines,
        on_bad_lines,
        names,
        prefix,
        defaults={"delimiter": ","},
    )
    kwds.update(kwds_defaults)

    return _read(filepath_or_buffer, kwds)


@deprecate_nonkeyword_arguments(
    version=None, allowed_args=["filepath_or_buffer"], stacklevel=3
)
@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=lib.no_default,
    index_col=None,
    usecols=None,
    squeeze=False,
    prefix=lib.no_default,
    mangle_dupe_cols=True,
    # General Parsing Configuration
    dtype: DtypeArg | None = 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=None,
    warn_bad_lines=None,
    # TODO (2.0): set on_bad_lines to "error".
    # See _refine_defaults_read comment for why we do this.
    on_bad_lines=None,
    encoding_errors: str | None = "strict",
    # Internal
    delim_whitespace=False,
    low_memory=_c_parser_defaults["low_memory"],
    memory_map=False,
    float_precision=None,
):
    # locals() should never be modified
    kwds = locals().copy()
    del kwds["filepath_or_buffer"]
    del kwds["sep"]

    kwds_defaults = _refine_defaults_read(
        dialect,
        delimiter,
        delim_whitespace,
        engine,
        sep,
        error_bad_lines,
        warn_bad_lines,
        on_bad_lines,
        names,
        prefix,
        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`.
    **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 = {}
        default: object | None

        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":
            for argname, default in _fwf_defaults.items():
                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=7)
            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):
    na_fvalues: set | dict
    if na_values is None:
        if keep_default_na:
            na_values = STR_NA_VALUES
        else:
            na_values = set()
        na_fvalues = set()
    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
        na_fvalues = {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: list[int | str | float] = []
    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))

            result.append(v)
        except (TypeError, ValueError, OverflowError):
            pass
        try:
            result.append(int(x))
        except (TypeError, ValueError, OverflowError):
            pass
    return set(result)


def _refine_defaults_read(
    dialect: str | csv.Dialect,
    delimiter: str | object,
    delim_whitespace: bool,
    engine: str,
    sep: str | object,
    error_bad_lines: bool | None,
    warn_bad_lines: bool | None,
    on_bad_lines: str | None,
    names: ArrayLike | None | object,
    prefix: str | None | 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.
    error_bad_lines : str or None
        Whether to error on a bad line or not.
    warn_bad_lines : str or None
        Whether to warn on a bad line or not.
    on_bad_lines : str or None
        An option for handling bad lines or a sentinel value(None).
    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.
    prefix : str, optional
        Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
    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``.
        If on_bad_lines is specified(not ``None``) and ``error_bad_lines``/
        ``warn_bad_lines`` is 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
        )

    if delimiter and (sep is not lib.no_default):
        raise ValueError("Specified a sep and a delimiter; you can only specify one.")

    if (
        names is not None
        and names is not lib.no_default
        and prefix is not None
        and prefix is not lib.no_default
    ):
        raise ValueError("Specified named and prefix; you can only specify one.")

    kwds["names"] = None if names is lib.no_default else names
    kwds["prefix"] = None if prefix is lib.no_default else prefix

    # 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

    # Ensure that on_bad_lines and error_bad_lines/warn_bad_lines
    # aren't specified at the same time. If so, raise. Otherwise,
    # alias on_bad_lines to "error" if error/warn_bad_lines not set
    # and on_bad_lines is not set. on_bad_lines is defaulted to None
    # so we can tell if it is set (this is why this hack exists).
    if on_bad_lines is not None:
        if error_bad_lines is not None or warn_bad_lines is not None:
            raise ValueError(
                "Both on_bad_lines and error_bad_lines/warn_bad_lines are set. "
                "Please only set on_bad_lines."
            )
        if on_bad_lines == "error":
            kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
        elif on_bad_lines == "warn":
            kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
        elif on_bad_lines == "skip":
            kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.SKIP
        else:
            raise ValueError(f"Argument {on_bad_lines} is invalid for on_bad_lines")
    else:
        if error_bad_lines is not None:
            # Must check is_bool, because other stuff(e.g. non-empty lists) eval to true
            validate_bool_kwarg(error_bad_lines, "error_bad_lines")
            if error_bad_lines:
                kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
            else:
                if warn_bad_lines is not None:
                    # This is the case where error_bad_lines is False
                    # We can only warn/skip if error_bad_lines is False
                    # None doesn't work because backwards-compatibility reasons
                    validate_bool_kwarg(warn_bad_lines, "warn_bad_lines")
                    if warn_bad_lines:
                        kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
                    else:
                        kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.SKIP
                else:
                    # Backwards compat, when only error_bad_lines = false, we warn
                    kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
        else:
            # Everything None -> Error
            kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR

    return kwds


def _extract_dialect(kwds: dict[str, Any]) -> csv.Dialect | None:
    """
    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'")