/pandas/io/parsers.py

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"""
Module contains tools for processing files into DataFrames or other objects
"""

from collections import abc, defaultdict
import csv
import datetime
from io import StringIO, TextIOWrapper
import itertools
import re
import sys
from textwrap import fill
from typing import Any, Dict, Iterable, List, Set
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 FilePathOrBuffer
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
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 (
    get_filepath_or_buffer,
    get_handle,
    infer_compression,
    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 handler (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 unparseable 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()``.
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``.
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` for the ordinary converter,
    `high` for the high-precision converter, and `round_trip` for the
    round-trip converter.

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.

    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.
    """
    if names is not None:
        if len(names) != len(set(names)):
            raise ValueError("Duplicate names are not allowed.")


def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
    """Generic reader of line files."""
    encoding = kwds.get("encoding", None)
    if encoding is not None:
        encoding = re.sub("_", "-", encoding).lower()
        kwds["encoding"] = encoding

    compression = kwds.get("compression", "infer")
    compression = infer_compression(filepath_or_buffer, compression)

    # TODO: get_filepath_or_buffer could return
    # Union[FilePathOrBuffer, s3fs.S3File, gcsfs.GCSFile]
    # though mypy handling of conditional imports is difficult.
    # See https://github.com/python/mypy/issues/1297
    fp_or_buf, _, compression, should_close = get_filepath_or_buffer(
        filepath_or_buffer, encoding, compression
    )
    kwds["compression"] = compression

    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(fp_or_buf, **kwds)

    if chunksize or iterator:
        return parser

    try:
        data = parser.read(nrows)
    finally:
        parser.close()

    if should_close:
        try:
            fp_or_buf.close()
        except ValueError:
            pass

    return data


_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()


def _make_parser_function(name, default_sep=","):
    def parser_f(
        filepath_or_buffer: FilePathOrBuffer,
        sep=default_sep,
        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=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,
    ):

        # 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:
            sep_override = delimiter is None and sep == default_sep
            kwds = dict(sep_override=sep_override)
        else:
            kwds = dict()

        # Alias sep -> delimiter.
        if delimiter is None:
            delimiter = sep

        if delim_whitespace and delimiter != default_sep:
            raise ValueError(
                "Specified a delimiter with both sep and "
                "delim_whitespace=True; you can only specify one."
            )

        if engine is not None:
            engine_specified = True
        else:
            engine = "c"
            engine_specified = False

        kwds.update(
            delimiter=delimiter,
            engine=engine,
            dialect=dialect,
            compression=compression,
            engine_specified=engine_specified,
            doublequote=doublequote,
            escapechar=escapechar,
            quotechar=quotechar,
            quoting=quoting,
            skipinitialspace=skipinitialspace,
            lineterminator=lineterminator,
            header=header,
            index_col=index_col,
            names=names,
            prefix=prefix,
            skiprows=skiprows,
            skipfooter=skipfooter,
            na_values=na_values,
            true_values=true_values,
            false_values=false_values,
            keep_default_na=keep_default_na,
            thousands=thousands,
            comment=comment,
            decimal=decimal,
            parse_dates=parse_dates,
            keep_date_col=keep_date_col,
            dayfirst=dayfirst,
            date_parser=date_parser,
            cache_dates=cache_dates,
            nrows=nrows,
            iterator=iterator,
            chunksize=chunksize,
            converters=converters,
            dtype=dtype,
            usecols=usecols,
            verbose=verbose,
            encoding=encoding,
            squeeze=squeeze,
            memory_map=memory_map,
            float_precision=float_precision,
            na_filter=na_filter,
            delim_whitespace=delim_whitespace,
            warn_bad_lines=warn_bad_lines,
            error_bad_lines=error_bad_lines,
            low_memory=low_memory,
            mangle_dupe_cols=mangle_dupe_cols,
            infer_datetime_format=infer_datetime_format,
            skip_blank_lines=skip_blank_lines,
        )

        return _read(filepath_or_buffer, kwds)

    parser_f.__name__ = name

    return parser_f


read_csv = _make_parser_function("read_csv", default_sep=",")
read_csv = Appender(
    _doc_read_csv_and_table.format(
        func_name="read_csv",
        summary="Read a comma-separated values (csv) file into DataFrame.",
        _default_sep="','",
    )
)(read_csv)

read_table = _make_parser_function("read_table", default_sep="\t")
read_table = Appender(
    _doc_read_csv_and_table.format(
        func_name="read_table",
        summary="Read general delimited file into DataFrame.",
        _default_sep=r"'\\t' (tab-stop)",
    )
)(read_table)


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 handler (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_specified = kwds.get("engine_specified", engine_specified)

        if kwds.get("dialect") is not None:
            dialect = kwds["dialect"]
            if dialect in csv.list_dialects():
                dialect = csv.get_dialect(dialect)

            # Any valid dialect should have these attributes.
            # If any are missing, we will raise automatically.
            for param in (
                "delimiter",
                "doublequote",
                "escapechar",
                "skipinitialspace",
                "quotechar",
                "quoting",
            ):
                try:
                    dialect_val = getattr(dialect, param)
                except AttributeError as err:
                    raise ValueError(
                        f"Invalid dialect {kwds['dialect']} provided"
                    ) from err
                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

        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'")

        if kwds.get("header", "infer") == "infer":
            kwds["header"] = 0 if kwds.get("names") is None else None

        self.orig_options = kwds

        # miscellanea
        self.engine = engine
        self._engine = None
        self._currow = 0

        options = self._get_options_with_defaults(engine)

        self.chunksize = options.pop("chunksize", None)
        self.nrows = options.pop("nrows", None)
        self.squeeze = options.pop("squeeze", False)

        # might mutate self.engine
        self.engine = 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._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":
            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):
            next_attr = "__next__"

            # The C engine doesn't need the file-like to have the "next" or
            # "__next__" attribute. However, the Python engine explicitly calls
            # "next(...)" when iterating through such an object, meaning it
            # needs to have that attribute ("next" for Python 2.x, "__next__"
            # for Python 3.x)
            if engine != "c" and not hasattr(f, next_attr):
                msg = "The 'python' engine cannot iterate through this file buffer."
                raise ValueError(msg)

        return engine

    def _clean_options(self, options, engine):
        result = options.copy()

        engine_specified = self._engine_specified
        fallback_reason = None

        sep = options["delimiter"]
        delim_whitespace = options["delim_whitespace"]

        # C engine not supported yet
        if engine == "c":
            if options["skipfooter"] > 0:
                fallback_reason = "the 'c' engine does not support skipfooter"
                engine = "python"

        encoding = sys.getfilesystemencoding() or "utf-8"
        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
            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 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"])

        depr_warning = ""

        for arg in _deprecated_args:
            parser_default = _c_parser_defaults[arg]
            depr_default = _deprecated_defaults[arg]

            msg = (
                f"The {repr(arg)} argument has been deprecated and will be "
                "removed in a future version."
            )

            if result.get(arg, depr_default) != depr_default:
                depr_warning += msg + "\n\n"
            else:
                result[arg] = parser_default

        if depr_warning != "":
            warnings.warn(depr_warning, FutureWarning, stacklevel=2)

        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"):
        if engine == "c":
            self._engine = CParserWrapper(self.f, **self.options)
        else:
            if engine == "python":
                klass = PythonParser
            elif engine == "python-fwf":
                klass = FixedWidthFieldParser
            else:
                raise ValueError(
                    f"Unknown engine: {engine} (valid options "
                    'are "c", "python", or "python-fwf")'
                )
            self._engine = klass(self.f, **self.options)

    def _failover_to_python(self):
        raise AbstractMethodError(self)

    def read(self, nrows=None):
        nrows = _validate_integer("nrows", nrows)
        ret = self._engine.read(nrows)

        # May alter columns / col_dict
        index, columns, col_dict = self._create_index(ret)

        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 _create_index(self, ret):
        index, columns, col_dict = ret
        return index, columns, col_dict

    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 _is_index_col(col):
    return col is not None and col is not False


def _is_potential_multi_index(columns):
    """
    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

    Returns
    -------
    boolean : Whether or not columns could become a MultiIndex
    """
    return (
        len(columns)
        and not isinstance(columns, MultiIndex)
        and all(isinstance(c, tuple) for c in columns)
    )


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 = None
        self.prefix = kwds.pop("prefix", None)

        self.index_col = kwds.get("index_col", None)
        self.unnamed_cols = set()
        self.index_names = 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…