text: """
  Module contains tools for processing files into DataFrames or other objects
  """
  
  from collections import abc, defaultdict
  import csv
  import datetime
  from io import StringIO
  import itertools
  import re
  import sys
  from textwrap import fill
  from typing import (
      Any,
      Dict,
      Iterable,
      Iterator,
      List,
      Optional,
      Sequence,
      Set,
      Type,
      cast,
  )
  import warnings
  
  import numpy as np
  
  import pandas._libs.lib as lib
  import pandas._libs.ops as libops
  import pandas._libs.parsers as parsers
  from pandas._libs.parsers import STR_NA_VALUES
  from pandas._libs.tslibs import parsing
  from pandas._typing import DtypeArg, FilePathOrBuffer, StorageOptions, Union
  from pandas.errors import (
      AbstractMethodError,
      EmptyDataError,
      ParserError,
      ParserWarning,
  )
  from pandas.util._decorators import Appender
  
  from pandas.core.dtypes.cast import astype_nansafe
  from pandas.core.dtypes.common import (
      ensure_object,
      ensure_str,
      is_bool_dtype,
      is_categorical_dtype,
      is_dict_like,
      is_dtype_equal,
      is_extension_array_dtype,
      is_file_like,
      is_float,
      is_integer,
      is_integer_dtype,
      is_list_like,
      is_object_dtype,
      is_scalar,
      is_string_dtype,
      pandas_dtype,
  )
  from pandas.core.dtypes.dtypes import CategoricalDtype
  from pandas.core.dtypes.missing import isna
  
  from pandas.core import algorithms, generic
  from pandas.core.arrays import Categorical
  from pandas.core.frame import DataFrame
  from pandas.core.indexes.api import (
      Index,
      MultiIndex,
      RangeIndex,
      ensure_index_from_sequences,
  )
  from pandas.core.series import Series
  from pandas.core.tools import datetimes as tools
  
  from pandas.io.common import IOHandles, get_handle, validate_header_arg
  from pandas.io.date_converters import generic_parser
  
  # BOM character (byte order mark)
  # This exists at the beginning of a file to indicate endianness
  # of a file (stream). Unfortunately, this marker screws up parsing,
  # so we need to remove it if we see it.
  _BOM = "\ufeff"
  
  _doc_read_csv_and_table = (
      r"""
  {summary}
  
  Also supports optionally iterating or breaking of the file
  into chunks.
  
  Additional help can be found in the online docs for
  `IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
  
  Parameters
  ----------
  filepath_or_buffer : str, path object or file-like object
      Any valid string path is acceptable. The string could be a URL. Valid
      URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
      expected. A local file could be: file://localhost/path/to/table.csv.
  
      If you want to pass in a path object, pandas accepts any ``os.PathLike``.
  
      By file-like object, we refer to objects with a ``read()`` method, such as
      a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
  sep : str, default {_default_sep}
      Delimiter to use. If sep is None, the C engine cannot automatically detect
      the separator, but the Python parsing engine can, meaning the latter will
      be used and automatically detect the separator by Python's builtin sniffer
      tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
      different from ``'\s+'`` will be interpreted as regular expressions and
      will also force the use of the Python parsing engine. Note that regex
      delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
  delimiter : str, default ``None``
      Alias for sep.
  header : int, list of int, default 'infer'
      Row number(s) to use as the column names, and the start of the
      data.  Default behavior is to infer the column names: if no names
      are passed the behavior is identical to ``header=0`` and column
      names are inferred from the first line of the file, if column
      names are passed explicitly then the behavior is identical to
      ``header=None``. Explicitly pass ``header=0`` to be able to
      replace existing names. The header can be a list of integers that
      specify row locations for a multi-index on the columns
      e.g. [0,1,3]. Intervening rows that are not specified will be
      skipped (e.g. 2 in this example is skipped). Note that this
      parameter ignores commented lines and empty lines if
      ``skip_blank_lines=True``, so ``header=0`` denotes the first line of
      data rather than the first line of the file.
  names : array-like, optional
      List of column names to use. If the file contains a header row,
      then you should explicitly pass ``header=0`` to override the column names.
      Duplicates in this list are not allowed.
  index_col : int, str, sequence of int / str, or False, default ``None``
    Column(s) to use as the row labels of the ``DataFrame``, either given as
    string name or column index. If a sequence of int / str is given, a
    MultiIndex is used.
  
    Note: ``index_col=False`` can be used to force pandas to *not* use the first
    column as the index, e.g. when you have a malformed file with delimiters at
    the end of each line.
  usecols : list-like or callable, optional
      Return a subset of the columns. If list-like, all elements must either
      be positional (i.e. integer indices into the document columns) or strings
      that correspond to column names provided either by the user in `names` or
      inferred from the document header row(s). For example, a valid list-like
      `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
      Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
      To instantiate a DataFrame from ``data`` with element order preserved use
      ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
      in ``['foo', 'bar']`` order or
      ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
      for ``['bar', 'foo']`` order.
  
      If callable, the callable function will be evaluated against the column
      names, returning names where the callable function evaluates to True. An
      example of a valid callable argument would be ``lambda x: x.upper() in
      ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
      parsing time and lower memory usage.
  squeeze : bool, default False
      If the parsed data only contains one column then return a Series.
  prefix : str, optional
      Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
  mangle_dupe_cols : bool, default True
      Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
      'X'...'X'. Passing in False will cause data to be overwritten if there
      are duplicate names in the columns.
  dtype : Type name or dict of column -> type, optional
      Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32,
      'c': 'Int64'}}
      Use `str` or `object` together with suitable `na_values` settings
      to preserve and not interpret dtype.
      If converters are specified, they will be applied INSTEAD
      of dtype conversion.
  engine : {{'c', 'python'}}, optional
      Parser engine to use. The C engine is faster while the python engine is
      currently more feature-complete.
  converters : dict, optional
      Dict of functions for converting values in certain columns. Keys can either
      be integers or column labels.
  true_values : list, optional
      Values to consider as True.
  false_values : list, optional
      Values to consider as False.
  skipinitialspace : bool, default False
      Skip spaces after delimiter.
  skiprows : list-like, int or callable, optional
      Line numbers to skip (0-indexed) or number of lines to skip (int)
      at the start of the file.
  
      If callable, the callable function will be evaluated against the row
      indices, returning True if the row should be skipped and False otherwise.
      An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
  skipfooter : int, default 0
      Number of lines at bottom of file to skip (Unsupported with engine='c').
  nrows : int, optional
      Number of rows of file to read. Useful for reading pieces of large files.
  na_values : scalar, str, list-like, or dict, optional
      Additional strings to recognize as NA/NaN. If dict passed, specific
      per-column NA values.  By default the following values are interpreted as
      NaN: '"""
      + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent="    ")
      + """'.
  keep_default_na : bool, default True
      Whether or not to include the default NaN values when parsing the data.
      Depending on whether `na_values` is passed in, the behavior is as follows:
  
      * If `keep_default_na` is True, and `na_values` are specified, `na_values`
        is appended to the default NaN values used for parsing.
      * If `keep_default_na` is True, and `na_values` are not specified, only
        the default NaN values are used for parsing.
      * If `keep_default_na` is False, and `na_values` are specified, only
        the NaN values specified `na_values` are used for parsing.
      * If `keep_default_na` is False, and `na_values` are not specified, no
        strings will be parsed as NaN.
  
      Note that if `na_filter` is passed in as False, the `keep_default_na` and
      `na_values` parameters will be ignored.
  na_filter : bool, default True
      Detect missing value markers (empty strings and the value of na_values). In
      data without any NAs, passing na_filter=False can improve the performance
      of reading a large file.
  verbose : bool, default False
      Indicate number of NA values placed in non-numeric columns.
  skip_blank_lines : bool, default True
      If True, skip over blank lines rather than interpreting as NaN values.
  parse_dates : bool or list of int or names or list of lists or dict, \
  default False
      The behavior is as follows:
  
      * boolean. If True -> try parsing the index.
      * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
        each as a separate date column.
      * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
        a single date column.
      * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
        result 'foo'
  
      If a column or index cannot be represented as an array of datetimes,
      say because of an unparsable value or a mixture of timezones, the column
      or index will be returned unaltered as an object data type. For
      non-standard datetime parsing, use ``pd.to_datetime`` after
      ``pd.read_csv``. To parse an index or column with a mixture of timezones,
      specify ``date_parser`` to be a partially-applied
      :func:`pandas.to_datetime` with ``utc=True``. See
      :ref:`io.csv.mixed_timezones` for more.
  
      Note: A fast-path exists for iso8601-formatted dates.
  infer_datetime_format : bool, default False
      If True and `parse_dates` is enabled, pandas will attempt to infer the
      format of the datetime strings in the columns, and if it can be inferred,
      switch to a faster method of parsing them. In some cases this can increase
      the parsing speed by 5-10x.
  keep_date_col : bool, default False
      If True and `parse_dates` specifies combining multiple columns then
      keep the original columns.
  date_parser : function, optional
      Function to use for converting a sequence of string columns to an array of
      datetime instances. The default uses ``dateutil.parser.parser`` to do the
      conversion. Pandas will try to call `date_parser` in three different ways,
      advancing to the next if an exception occurs: 1) Pass one or more arrays
      (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
      string values from the columns defined by `parse_dates` into a single array
      and pass that; and 3) call `date_parser` once for each row using one or
      more strings (corresponding to the columns defined by `parse_dates`) as
      arguments.
  dayfirst : bool, default False
      DD/MM format dates, international and European format.
  cache_dates : bool, default True
      If True, use a cache of unique, converted dates to apply the datetime
      conversion. May produce significant speed-up when parsing duplicate
      date strings, especially ones with timezone offsets.
  
      .. versionadded:: 0.25.0
  iterator : bool, default False
      Return TextFileReader object for iteration or getting chunks with
      ``get_chunk()``.
  
      .. versionchanged:: 1.2
  
         ``TextFileReader`` is a context manager.
  chunksize : int, optional
      Return TextFileReader object for iteration.
      See the `IO Tools docs
      <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
      for more information on ``iterator`` and ``chunksize``.
  
      .. versionchanged:: 1.2
  
         ``TextFileReader`` is a context manager.
  compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer'
      For on-the-fly decompression of on-disk data. If 'infer' and
      `filepath_or_buffer` is path-like, then detect compression from the
      following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
      decompression). If using 'zip', the ZIP file must contain only one data
      file to be read in. Set to None for no decompression.
  thousands : str, optional
      Thousands separator.
  decimal : str, default '.'
      Character to recognize as decimal point (e.g. use ',' for European data).
  lineterminator : str (length 1), optional
      Character to break file into lines. Only valid with C parser.
  quotechar : str (length 1), optional
      The character used to denote the start and end of a quoted item. Quoted
      items can include the delimiter and it will be ignored.
  quoting : int or csv.QUOTE_* instance, default 0
      Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
      QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
  doublequote : bool, default ``True``
     When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
     whether or not to interpret two consecutive quotechar elements INSIDE a
     field as a single ``quotechar`` element.
  escapechar : str (length 1), optional
      One-character string used to escape other characters.
  comment : str, optional
      Indicates remainder of line should not be parsed. If found at the beginning
      of a line, the line will be ignored altogether. This parameter must be a
      single character. Like empty lines (as long as ``skip_blank_lines=True``),
      fully commented lines are ignored by the parameter `header` but not by
      `skiprows`. For example, if ``comment='#'``, parsing
      ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
      treated as the header.
  encoding : str, optional
      Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
      standard encodings
      <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .
  dialect : str or csv.Dialect, optional
      If provided, this parameter will override values (default or not) for the
      following parameters: `delimiter`, `doublequote`, `escapechar`,
      `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
      override values, a ParserWarning will be issued. See csv.Dialect
      documentation for more details.
  error_bad_lines : bool, default True
      Lines with too many fields (e.g. a csv line with too many commas) will by
      default cause an exception to be raised, and no DataFrame will be returned.
      If False, then these "bad lines" will dropped from the DataFrame that is
      returned.
  warn_bad_lines : bool, default True
      If error_bad_lines is False, and warn_bad_lines is True, a warning for each
      "bad line" will be output.
  delim_whitespace : bool, default False
      Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
      used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
      is set to True, nothing should be passed in for the ``delimiter``
      parameter.
  low_memory : bool, default True
      Internally process the file in chunks, resulting in lower memory use
      while parsing, but possibly mixed type inference.  To ensure no mixed
      types either set False, or specify the type with the `dtype` parameter.
      Note that the entire file is read into a single DataFrame regardless,
      use the `chunksize` or `iterator` parameter to return the data in chunks.
      (Only valid with C parser).
  memory_map : bool, default False
      If a filepath is provided for `filepath_or_buffer`, map the file object
      directly onto memory and access the data directly from there. Using this
      option can improve performance because there is no longer any I/O overhead.
  float_precision : str, optional
      Specifies which converter the C engine should use for floating-point
      values. The options are ``None`` or 'high' for the ordinary converter,
      'legacy' for the original lower precision pandas converter, and
      'round_trip' for the round-trip converter.
  
      .. versionchanged:: 1.2
  
  {storage_options}
  
      .. versionadded:: 1.2
  
  Returns
  -------
  DataFrame or TextParser
      A comma-separated values (csv) file is returned as two-dimensional
      data structure with labeled axes.
  
  See Also
  --------
  DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
  read_csv : Read a comma-separated values (csv) file into DataFrame.
  read_fwf : Read a table of fixed-width formatted lines into DataFrame.
  
  Examples
  --------
  >>> pd.{func_name}('data.csv')  # doctest: +SKIP
  """
  )
  
  
  def validate_integer(name, val, min_val=0):
      """
      Checks whether the 'name' parameter for parsing is either
      an integer OR float that can SAFELY be cast to an integer
      without losing accuracy. Raises a ValueError if that is
      not the case.
  
      Parameters
      ----------
      name : string
          Parameter name (used for error reporting)
      val : int or float
          The value to check
      min_val : int
          Minimum allowed value (val < min_val will result in a ValueError)
      """
      msg = f"'{name:s}' must be an integer >={min_val:d}"
  
      if val is not None:
          if is_float(val):
              if int(val) != val:
                  raise ValueError(msg)
              val = int(val)
          elif not (is_integer(val) and val >= min_val):
              raise ValueError(msg)
  
      return val
  
  
  def _validate_names(names):
      """
      Raise ValueError if the `names` parameter contains duplicates or has an
      invalid data type.
  
      Parameters
      ----------
      names : array-like or None
          An array containing a list of the names used for the output DataFrame.
  
      Raises
      ------
      ValueError
          If names are not unique or are not ordered (e.g. set).
      """
      if names is not None:
          if len(names) != len(set(names)):
              raise ValueError("Duplicate names are not allowed.")
          if not (
              is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView)
          ):
              raise ValueError("Names should be an ordered collection.")
  
  
  def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
      """Generic reader of line files."""
      if kwds.get("date_parser", None) is not None:
          if isinstance(kwds["parse_dates"], bool):
              kwds["parse_dates"] = True
  
      # Extract some of the arguments (pass chunksize on).
      iterator = kwds.get("iterator", False)
      chunksize = validate_integer("chunksize", kwds.get("chunksize", None), 1)
      nrows = kwds.get("nrows", None)
  
      # Check for duplicates in names.
      _validate_names(kwds.get("names", None))
  
      # Create the parser.
      parser = TextFileReader(filepath_or_buffer, **kwds)
  
      if chunksize or iterator:
          return parser
  
      with parser:
          return parser.read(nrows)
  
  
  _parser_defaults = {
      "delimiter": None,
      "escapechar": None,
      "quotechar": '"',
      "quoting": csv.QUOTE_MINIMAL,
      "doublequote": True,
      "skipinitialspace": False,
      "lineterminator": None,
      "header": "infer",
      "index_col": None,
      "names": None,
      "prefix": None,
      "skiprows": None,
      "skipfooter": 0,
      "nrows": None,
      "na_values": None,
      "keep_default_na": True,
      "true_values": None,
      "false_values": None,
      "converters": None,
      "dtype": None,
      "cache_dates": True,
      "thousands": None,
      "comment": None,
      "decimal": ".",
      # 'engine': 'c',
      "parse_dates": False,
      "keep_date_col": False,
      "dayfirst": False,
      "date_parser": None,
      "usecols": None,
      # 'iterator': False,
      "chunksize": None,
      "verbose": False,
      "encoding": None,
      "squeeze": False,
      "compression": None,
      "mangle_dupe_cols": True,
      "infer_datetime_format": False,
      "skip_blank_lines": True,
  }
  
  
  _c_parser_defaults = {
      "delim_whitespace": False,
      "na_filter": True,
      "low_memory": True,
      "memory_map": False,
      "error_bad_lines": True,
      "warn_bad_lines": True,
      "float_precision": None,
  }
  
  _fwf_defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None}
  
  _c_unsupported = {"skipfooter"}
  _python_unsupported = {"low_memory", "float_precision"}
  
  _deprecated_defaults: Dict[str, Any] = {}
  _deprecated_args: Set[str] = set()
  
  
  @Appender(
      _doc_read_csv_and_table.format(
          func_name="read_csv",
          summary="Read a comma-separated values (csv) file into DataFrame.",
          _default_sep="','",
          storage_options=generic._shared_docs["storage_options"],
      )
  )
  def read_csv(
      filepath_or_buffer: FilePathOrBuffer,
      sep=lib.no_default,
      delimiter=None,
      # Column and Index Locations and Names
      header="infer",
      names=None,
      index_col=None,
      usecols=None,
      squeeze=False,
      prefix=None,
      mangle_dupe_cols=True,
      # General Parsing Configuration
      dtype: Optional[DtypeArg] = None,
      engine=None,
      converters=None,
      true_values=None,
      false_values=None,
      skipinitialspace=False,
      skiprows=None,
      skipfooter=0,
      nrows=None,
      # NA and Missing Data Handling
      na_values=None,
      keep_default_na=True,
      na_filter=True,
      verbose=False,
      skip_blank_lines=True,
      # Datetime Handling
      parse_dates=False,
      infer_datetime_format=False,
      keep_date_col=False,
      date_parser=None,
      dayfirst=False,
      cache_dates=True,
      # Iteration
      iterator=False,
      chunksize=None,
      # Quoting, Compression, and File Format
      compression="infer",
      thousands=None,
      decimal: str = ".",
      lineterminator=None,
      quotechar='"',
      quoting=csv.QUOTE_MINIMAL,
      doublequote=True,
      escapechar=None,
      comment=None,
      encoding=None,
      dialect=None,
      # Error Handling
      error_bad_lines=True,
      warn_bad_lines=True,
      # Internal
      delim_whitespace=False,
      low_memory=_c_parser_defaults["low_memory"],
      memory_map=False,
      float_precision=None,
      storage_options: StorageOptions = None,
  ):
      kwds = locals()
      del kwds["filepath_or_buffer"]
      del kwds["sep"]
  
      kwds_defaults = _refine_defaults_read(
          dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": ","}
      )
      kwds.update(kwds_defaults)
  
      return _read(filepath_or_buffer, kwds)
  
  
  @Appender(
      _doc_read_csv_and_table.format(
          func_name="read_table",
          summary="Read general delimited file into DataFrame.",
          _default_sep=r"'\\t' (tab-stop)",
          storage_options=generic._shared_docs["storage_options"],
      )
  )
  def read_table(
      filepath_or_buffer: FilePathOrBuffer,
      sep=lib.no_default,
      delimiter=None,
      # Column and Index Locations and Names
      header="infer",
      names=None,
      index_col=None,
      usecols=None,
      squeeze=False,
      prefix=None,
      mangle_dupe_cols=True,
      # General Parsing Configuration
      dtype: Optional[DtypeArg] = None,
      engine=None,
      converters=None,
      true_values=None,
      false_values=None,
      skipinitialspace=False,
      skiprows=None,
      skipfooter=0,
      nrows=None,
      # NA and Missing Data Handling
      na_values=None,
      keep_default_na=True,
      na_filter=True,
      verbose=False,
      skip_blank_lines=True,
      # Datetime Handling
      parse_dates=False,
      infer_datetime_format=False,
      keep_date_col=False,
      date_parser=None,
      dayfirst=False,
      cache_dates=True,
      # Iteration
      iterator=False,
      chunksize=None,
      # Quoting, Compression, and File Format
      compression="infer",
      thousands=None,
      decimal: str = ".",
      lineterminator=None,
      quotechar='"',
      quoting=csv.QUOTE_MINIMAL,
      doublequote=True,
      escapechar=None,
      comment=None,
      encoding=None,
      dialect=None,
      # Error Handling
      error_bad_lines=True,
      warn_bad_lines=True,
      # Internal
      delim_whitespace=False,
      low_memory=_c_parser_defaults["low_memory"],
      memory_map=False,
      float_precision=None,
  ):
      kwds = locals()
      del kwds["filepath_or_buffer"]
      del kwds["sep"]
  
      kwds_defaults = _refine_defaults_read(
          dialect, delimiter, delim_whitespace, engine, sep, defaults={"delimiter": "\t"}
      )
      kwds.update(kwds_defaults)
  
      return _read(filepath_or_buffer, kwds)
  
  
  def read_fwf(
      filepath_or_buffer: FilePathOrBuffer,
      colspecs="infer",
      widths=None,
      infer_nrows=100,
      **kwds,
  ):
      r"""
      Read a table of fixed-width formatted lines into DataFrame.
  
      Also supports optionally iterating or breaking of the file
      into chunks.
  
      Additional help can be found in the `online docs for IO Tools
      <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
  
      Parameters
      ----------
      filepath_or_buffer : str, path object or file-like object
          Any valid string path is acceptable. The string could be a URL. Valid
          URL schemes include http, ftp, s3, and file. For file URLs, a host is
          expected. A local file could be:
          ``file://localhost/path/to/table.csv``.
  
          If you want to pass in a path object, pandas accepts any
          ``os.PathLike``.
  
          By file-like object, we refer to objects with a ``read()`` method,
          such as a file handle (e.g. via builtin ``open`` function)
          or ``StringIO``.
      colspecs : list of tuple (int, int) or 'infer'. optional
          A list of tuples giving the extents of the fixed-width
          fields of each line as half-open intervals (i.e.,  [from, to[ ).
          String value 'infer' can be used to instruct the parser to try
          detecting the column specifications from the first 100 rows of
          the data which are not being skipped via skiprows (default='infer').
      widths : list of int, optional
          A list of field widths which can be used instead of 'colspecs' if
          the intervals are contiguous.
      infer_nrows : int, default 100
          The number of rows to consider when letting the parser determine the
          `colspecs`.
  
          .. versionadded:: 0.24.0
      **kwds : optional
          Optional keyword arguments can be passed to ``TextFileReader``.
  
      Returns
      -------
      DataFrame or TextParser
          A comma-separated values (csv) file is returned as two-dimensional
          data structure with labeled axes.
  
      See Also
      --------
      DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
      read_csv : Read a comma-separated values (csv) file into DataFrame.
  
      Examples
      --------
      >>> pd.read_fwf('data.csv')  # doctest: +SKIP
      """
      # Check input arguments.
      if colspecs is None and widths is None:
          raise ValueError("Must specify either colspecs or widths")
      elif colspecs not in (None, "infer") and widths is not None:
          raise ValueError("You must specify only one of 'widths' and 'colspecs'")
  
      # Compute 'colspecs' from 'widths', if specified.
      if widths is not None:
          colspecs, col = [], 0
          for w in widths:
              colspecs.append((col, col + w))
              col += w
  
      kwds["colspecs"] = colspecs
      kwds["infer_nrows"] = infer_nrows
      kwds["engine"] = "python-fwf"
      return _read(filepath_or_buffer, kwds)
  
  
  class TextFileReader(abc.Iterator):
      """
  
      Passed dialect overrides any of the related parser options
  
      """
  
      def __init__(self, f, engine=None, **kwds):
  
          self.f = f
  
          if engine is not None:
              engine_specified = True
          else:
              engine = "python"
              engine_specified = False
          self.engine = engine
          self._engine_specified = kwds.get("engine_specified", engine_specified)
  
          _validate_skipfooter(kwds)
  
          dialect = _extract_dialect(kwds)
          if dialect is not None:
              kwds = _merge_with_dialect_properties(dialect, kwds)
  
          if kwds.get("header", "infer") == "infer":
              kwds["header"] = 0 if kwds.get("names") is None else None
  
          self.orig_options = kwds
  
          # miscellanea
          self._currow = 0
  
          options = self._get_options_with_defaults(engine)
          options["storage_options"] = kwds.get("storage_options", None)
  
          self.chunksize = options.pop("chunksize", None)
          self.nrows = options.pop("nrows", None)
          self.squeeze = options.pop("squeeze", False)
  
          self._check_file_or_buffer(f, engine)
          self.options, self.engine = self._clean_options(options, engine)
  
          if "has_index_names" in kwds:
              self.options["has_index_names"] = kwds["has_index_names"]
  
          self._engine = self._make_engine(self.engine)
  
      def close(self):
          self._engine.close()
  
      def _get_options_with_defaults(self, engine):
          kwds = self.orig_options
  
          options = {}
  
          for argname, default in _parser_defaults.items():
              value = kwds.get(argname, default)
  
              # see gh-12935
              if argname == "mangle_dupe_cols" and not value:
                  raise ValueError("Setting mangle_dupe_cols=False is not supported yet")
              else:
                  options[argname] = value
  
          for argname, default in _c_parser_defaults.items():
              if argname in kwds:
                  value = kwds[argname]
  
                  if engine != "c" and value != default:
                      if "python" in engine and argname not in _python_unsupported:
                          pass
                      elif value == _deprecated_defaults.get(argname, default):
                          pass
                      else:
                          raise ValueError(
                              f"The {repr(argname)} option is not supported with the "
                              f"{repr(engine)} engine"
                          )
              else:
                  value = _deprecated_defaults.get(argname, default)
              options[argname] = value
  
          if engine == "python-fwf":
              # pandas\io\parsers.py:907: error: Incompatible types in assignment
              # (expression has type "object", variable has type "Union[int, str,
              # None]")  [assignment]
              for argname, default in _fwf_defaults.items():  # type: ignore[assignment]
                  options[argname] = kwds.get(argname, default)
  
          return options
  
      def _check_file_or_buffer(self, f, engine):
          # see gh-16530
          if is_file_like(f) and engine != "c" and not hasattr(f, "__next__"):
              # The C engine doesn't need the file-like to have the "__next__"
              # attribute. However, the Python engine explicitly calls
              # "__next__(...)" when iterating through such an object, meaning it
              # needs to have that attribute
              raise ValueError(
                  "The 'python' engine cannot iterate through this file buffer."
              )
  
      def _clean_options(self, options, engine):
          result = options.copy()
  
          fallback_reason = None
  
          # C engine not supported yet
          if engine == "c":
              if options["skipfooter"] > 0:
                  fallback_reason = "the 'c' engine does not support skipfooter"
                  engine = "python"
  
          sep = options["delimiter"]
          delim_whitespace = options["delim_whitespace"]
  
          if sep is None and not delim_whitespace:
              if engine == "c":
                  fallback_reason = (
                      "the 'c' engine does not support "
                      "sep=None with delim_whitespace=False"
                  )
                  engine = "python"
          elif sep is not None and len(sep) > 1:
              if engine == "c" and sep == r"\s+":
                  result["delim_whitespace"] = True
                  del result["delimiter"]
              elif engine not in ("python", "python-fwf"):
                  # wait until regex engine integrated
                  fallback_reason = (
                      "the 'c' engine does not support "
                      "regex separators (separators > 1 char and "
                      r"different from '\s+' are interpreted as regex)"
                  )
                  engine = "python"
          elif delim_whitespace:
              if "python" in engine:
                  result["delimiter"] = r"\s+"
          elif sep is not None:
              encodeable = True
              encoding = sys.getfilesystemencoding() or "utf-8"
              try:
                  if len(sep.encode(encoding)) > 1:
                      encodeable = False
              except UnicodeDecodeError:
                  encodeable = False
              if not encodeable and engine not in ("python", "python-fwf"):
                  fallback_reason = (
                      f"the separator encoded in {encoding} "
                      "is > 1 char long, and the 'c' engine "
                      "does not support such separators"
                  )
                  engine = "python"
  
          quotechar = options["quotechar"]
          if quotechar is not None and isinstance(quotechar, (str, bytes)):
              if (
                  len(quotechar) == 1
                  and ord(quotechar) > 127
                  and engine not in ("python", "python-fwf")
              ):
                  fallback_reason = (
                      "ord(quotechar) > 127, meaning the "
                      "quotechar is larger than one byte, "
                      "and the 'c' engine does not support such quotechars"
                  )
                  engine = "python"
  
          if fallback_reason and self._engine_specified:
              raise ValueError(fallback_reason)
  
          if engine == "c":
              for arg in _c_unsupported:
                  del result[arg]
  
          if "python" in engine:
              for arg in _python_unsupported:
                  if fallback_reason and result[arg] != _c_parser_defaults[arg]:
                      raise ValueError(
                          "Falling back to the 'python' engine because "
                          f"{fallback_reason}, but this causes {repr(arg)} to be "
                          "ignored as it is not supported by the 'python' engine."
                      )
                  del result[arg]
  
          if fallback_reason:
              warnings.warn(
                  (
                      "Falling back to the 'python' engine because "
                      f"{fallback_reason}; you can avoid this warning by specifying "
                      "engine='python'."
                  ),
                  ParserWarning,
                  stacklevel=5,
              )
  
          index_col = options["index_col"]
          names = options["names"]
          converters = options["converters"]
          na_values = options["na_values"]
          skiprows = options["skiprows"]
  
          validate_header_arg(options["header"])
  
          for arg in _deprecated_args:
              parser_default = _c_parser_defaults[arg]
              depr_default = _deprecated_defaults[arg]
              if result.get(arg, depr_default) != depr_default:
                  msg = (
                      f"The {arg} argument has been deprecated and will be "
                      "removed in a future version.\n\n"
                  )
                  warnings.warn(msg, FutureWarning, stacklevel=2)
              else:
                  result[arg] = parser_default
  
          if index_col is True:
              raise ValueError("The value of index_col couldn't be 'True'")
          if _is_index_col(index_col):
              if not isinstance(index_col, (list, tuple, np.ndarray)):
                  index_col = [index_col]
          result["index_col"] = index_col
  
          names = list(names) if names is not None else names
  
          # type conversion-related
          if converters is not None:
              if not isinstance(converters, dict):
                  raise TypeError(
                      "Type converters must be a dict or subclass, "
                      f"input was a {type(converters).__name__}"
                  )
          else:
              converters = {}
  
          # Converting values to NA
          keep_default_na = options["keep_default_na"]
          na_values, na_fvalues = _clean_na_values(na_values, keep_default_na)
  
          # handle skiprows; this is internally handled by the
          # c-engine, so only need for python parsers
          if engine != "c":
              if is_integer(skiprows):
                  skiprows = list(range(skiprows))
              if skiprows is None:
                  skiprows = set()
              elif not callable(skiprows):
                  skiprows = set(skiprows)
  
          # put stuff back
          result["names"] = names
          result["converters"] = converters
          result["na_values"] = na_values
          result["na_fvalues"] = na_fvalues
          result["skiprows"] = skiprows
  
          return result, engine
  
      def __next__(self):
          try:
              return self.get_chunk()
          except StopIteration:
              self.close()
              raise
  
      def _make_engine(self, engine="c"):
          mapping: Dict[str, Type[ParserBase]] = {
              "c": CParserWrapper,
              "python": PythonParser,
              "python-fwf": FixedWidthFieldParser,
          }
          if engine not in mapping:
              raise ValueError(
                  f"Unknown engine: {engine} (valid options are {mapping.keys()})"
              )
          # error: Too many arguments for "ParserBase"
          return mapping[engine](self.f, **self.options)  # type: ignore[call-arg]
  
      def _failover_to_python(self):
          raise AbstractMethodError(self)
  
      def read(self, nrows=None):
          nrows = validate_integer("nrows", nrows)
          index, columns, col_dict = self._engine.read(nrows)
  
          if index is None:
              if col_dict:
                  # Any column is actually fine:
                  new_rows = len(next(iter(col_dict.values())))
                  index = RangeIndex(self._currow, self._currow + new_rows)
              else:
                  new_rows = 0
          else:
              new_rows = len(index)
  
          df = DataFrame(col_dict, columns=columns, index=index)
  
          self._currow += new_rows
  
          if self.squeeze and len(df.columns) == 1:
              return df[df.columns[0]].copy()
          return df
  
      def get_chunk(self, size=None):
          if size is None:
              size = self.chunksize
          if self.nrows is not None:
              if self._currow >= self.nrows:
                  raise StopIteration
              size = min(size, self.nrows - self._currow)
          return self.read(nrows=size)
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
  
  def _is_index_col(col):
      return col is not None and col is not False
  
  
  def _is_potential_multi_index(
      columns, index_col: Optional[Union[bool, Sequence[int]]] = None
  ):
      """
      Check whether or not the `columns` parameter
      could be converted into a MultiIndex.
  
      Parameters
      ----------
      columns : array-like
          Object which may or may not be convertible into a MultiIndex
      index_col : None, bool or list, optional
          Column or columns to use as the (possibly hierarchical) index
  
      Returns
      -------
      boolean : Whether or not columns could become a MultiIndex
      """
      if index_col is None or isinstance(index_col, bool):
          index_col = []
  
      return (
          len(columns)
          and not isinstance(columns, MultiIndex)
          and all(isinstance(c, tuple) for c in columns if c not in list(index_col))
      )
  
  
  def _evaluate_usecols(usecols, names):
      """
      Check whether or not the 'usecols' parameter
      is a callable.  If so, enumerates the 'names'
      parameter and returns a set of indices for
      each entry in 'names' that evaluates to True.
      If not a callable, returns 'usecols'.
      """
      if callable(usecols):
          return {i for i, name in enumerate(names) if usecols(name)}
      return usecols
  
  
  def _validate_usecols_names(usecols, names):
      """
      Validates that all usecols are present in a given
      list of names. If not, raise a ValueError that
      shows what usecols are missing.
  
      Parameters
      ----------
      usecols : iterable of usecols
          The columns to validate are present in names.
      names : iterable of names
          The column names to check against.
  
      Returns
      -------
      usecols : iterable of usecols
          The `usecols` parameter if the validation succeeds.
  
      Raises
      ------
      ValueError : Columns were missing. Error message will list them.
      """
      missing = [c for c in usecols if c not in names]
      if len(missing) > 0:
          raise ValueError(
              f"Usecols do not match columns, columns expected but not found: {missing}"
          )
  
      return usecols
  
  
  def _validate_skipfooter_arg(skipfooter):
      """
      Validate the 'skipfooter' parameter.
  
      Checks whether 'skipfooter' is a non-negative integer.
      Raises a ValueError if that is not the case.
  
      Parameters
      ----------
      skipfooter : non-negative integer
          The number of rows to skip at the end of the file.
  
      Returns
      -------
      validated_skipfooter : non-negative integer
          The original input if the validation succeeds.
  
      Raises
      ------
      ValueError : 'skipfooter' was not a non-negative integer.
      """
      if not is_integer(skipfooter):
          raise ValueError("skipfooter must be an integer")
  
      if skipfooter < 0:
          raise ValueError("skipfooter cannot be negative")
  
      return skipfooter
  
  
  def _validate_usecols_arg(usecols):
      """
      Validate the 'usecols' parameter.
  
      Checks whether or not the 'usecols' parameter contains all integers
      (column selection by index), strings (column by name) or is a callable.
      Raises a ValueError if that is not the case.
  
      Parameters
      ----------
      usecols : list-like, callable, or None
          List of columns to use when parsing or a callable that can be used
          to filter a list of table columns.
  
      Returns
      -------
      usecols_tuple : tuple
          A tuple of (verified_usecols, usecols_dtype).
  
          'verified_usecols' is either a set if an array-like is passed in or
          'usecols' if a callable or None is passed in.
  
          'usecols_dtype` is the inferred dtype of 'usecols' if an array-like
          is passed in or None if a callable or None is passed in.
      """
      msg = (
          "'usecols' must either be list-like of all strings, all unicode, "
          "all integers or a callable."
      )
      if usecols is not None:
          if callable(usecols):
              return usecols, None
  
          if not is_list_like(usecols):
              # see gh-20529
              #
              # Ensure it is iterable container but not string.
              raise ValueError(msg)
  
          usecols_dtype = lib.infer_dtype(usecols, skipna=False)
  
          if usecols_dtype not in ("empty", "integer", "string"):
              raise ValueError(msg)
  
          usecols = set(usecols)
  
          return usecols, usecols_dtype
      return usecols, None
  
  
  def _validate_parse_dates_arg(parse_dates):
      """
      Check whether or not the 'parse_dates' parameter
      is a non-boolean scalar. Raises a ValueError if
      that is the case.
      """
      msg = (
          "Only booleans, lists, and dictionaries are accepted "
          "for the 'parse_dates' parameter"
      )
  
      if parse_dates is not None:
          if is_scalar(parse_dates):
              if not lib.is_bool(parse_dates):
                  raise TypeError(msg)
  
          elif not isinstance(parse_dates, (list, dict)):
              raise TypeError(msg)
  
      return parse_dates
  
  
  class ParserBase:
      def __init__(self, kwds):
  
          self.names = kwds.get("names")
          self.orig_names: Optional[List] = None
          self.prefix = kwds.pop("prefix", None)
  
          self.index_col = kwds.get("index_col", None)
          self.unnamed_cols: Set = set()
          self.index_names: Optional[List] = None
          self.col_names = None
  
          self.parse_dates = _validate_parse_dates_arg(kwds.pop("parse_dates", False))
          self.date_parser = kwds.pop("date_parser", None)
          self.dayfirst = kwds.pop("dayfirst", False)
          self.keep_date_col = kwds.pop("keep_date_col", False)
  
          self.na_values = kwds.get("na_values")
          self.na_fvalues = kwds.get("na_fvalues")
          self.na_filter = kwds.get("na_filter", False)
          self.keep_default_na = kwds.get("keep_default_na", True)
  
          self.true_values = kwds.get("true_values")
          self.false_values = kwds.get("false_values")
          self.mangle_dupe_cols = kwds.get("mangle_dupe_cols", True)
          self.infer_datetime_format = kwds.pop("infer_datetime_format", False)
          self.cache_dates = kwds.pop("cache_dates", True)
  
          self._date_conv = _make_date_converter(
              date_parser=self.date_parser,
              dayfirst=self.dayfirst,
              infer_datetime_format=self.infer_datetime_format,
              cache_dates=self.cache_dates,
          )
  
          # validate header options for mi
          self.header = kwds.get("header")
          if isinstance(self.header, (list, tuple, np.ndarray)):
              if not all(map(is_integer, self.header)):
                  raise ValueError("header must be integer or list of integers")
              if any(i < 0 for i in self.header):
                  raise ValueError(
                      "cannot specify multi-index header with negative integers"
                  )
              if kwds.get("usecols"):
                  raise ValueError(
                      "cannot specify usecols when specifying a multi-index header"
                  )
              if kwds.get("names"):
                  raise ValueError(
                      "cannot specify names when specifying a multi-index header"
                  )
  
              # validate index_col that only contains integers
              if self.index_col is not None:
                  is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray))
                  if not (
                      is_sequence
                      and all(map(is_integer, self.index_col))
                      or is_integer(self.index_col)
                  ):
                      raise ValueError(
                          "index_col must only contain row numbers "
                          "when specifying a multi-index header"
                      )
          elif self.header is not None:
              # GH 27394
              if self.prefix is not None:
                  raise ValueError(
                      "Argument prefix must be None if argument header is not None"
                  )
              # GH 16338
              elif not is_integer(self.header):
                  raise ValueError("header must be integer or list of integers")
              # GH 27779
              elif self.header < 0:
                  raise ValueError(
                      "Passing negative integer to header is invalid. "
                      "For no header, use header=None instead"
                  )
  
          self._name_processed = False
  
          self._first_chunk = True
  
          self.handles: Optional[IOHandles] = None
  
      def _open_handles(self, src: FilePathOrBuffer, kwds: Dict[str, Any]) -> None:
          """
          Let the readers open IOHanldes after they are done with their potential raises.
          """
          self.handles = get_handle(
              src,
              "r",
              encoding=kwds.get("encoding", None),
              compression=kwds.get("compression", None),
              memory_map=kwds.get("memory_map", False),
              storage_options=kwds.get("storage_options", None),
          )
  
      def _validate_parse_dates_presence(self, columns: List[str]) -> None:
          """
          Check if parse_dates are in columns.
  
          If user has provided names for parse_dates, check if those columns
          are available.
  
          Parameters
          ----------
          columns : list
              List of names of the dataframe.
  
          Raises
          ------
          ValueError
              If column to parse_date is not in dataframe.
  
          """
          cols_needed: Iterable
          if is_dict_like(self.parse_dates):
              cols_needed = itertools.chain(*self.parse_dates.values())
          elif is_list_like(self.parse_dates):
              # a column in parse_dates could be represented
              # ColReference = Union[int, str]
              # DateGroups = List[ColReference]
              # ParseDates = Union[DateGroups, List[DateGroups],
              #     Dict[ColReference, DateGroups]]
              cols_needed = itertools.chain.from_iterable(
                  col if is_list_like(col) else [col] for col in self.parse_dates
              )
          else:
              cols_needed = []
  
          # get only columns that are references using names (str), not by index
          missing_cols = ", ".join(
              sorted(
                  {
                      col
                      for col in cols_needed
                      if isinstance(col, str) and col not in columns
                  }
              )
          )
          if missing_cols:
              raise ValueError(
                  f"Missing column provided to 'parse_dates': '{missing_cols}'"
              )
  
      def close(self):
          if self.handles is not None:
              self.handles.close()
  
      @property
      def _has_complex_date_col(self):
          return isinstance(self.parse_dates, dict) or (
              isinstance(self.parse_dates, list)
              and len(self.parse_dates) > 0
              and isinstance(self.parse_dates[0], list)
          )
  
      def _should_parse_dates(self, i):
          if isinstance(self.parse_dates, bool):
              return self.parse_dates
          else:
              if self.index_names is not None:
                  name = self.index_names[i]
              else:
                  name = None
              j = i if self.index_col is None else self.index_col[i]
  
              if is_scalar(self.parse_dates):
                  return (j == self.parse_dates) or (
                      name is not None and name == self.parse_dates
                  )
              else:
                  return (j in self.parse_dates) or (
                      name is not None and name in self.parse_dates
                  )
  
      def _extract_multi_indexer_columns(
          self, header, index_names, col_names, passed_names=False
      ):
          """
          extract and return the names, index_names, col_names
          header is a list-of-lists returned from the parsers
          """
          if len(header) < 2:
              return header[0], index_names, col_names, passed_names
  
          # the names are the tuples of the header that are not the index cols
          # 0 is the name of the index, assuming index_col is a list of column
          # numbers
          ic = self.index_col
          if ic is None:
              ic = []
  
          if not isinstance(ic, (list, tuple, np.ndarray)):
              ic = [ic]
          sic = set(ic)
  
          # clean the index_names
          index_names = header.pop(-1)
          index_names, _, _ = _clean_index_names(
              index_names, self.index_col, self.unnamed_cols
          )
  
          # extract the columns
          field_count = len(header[0])
  
          def extract(r):
              return tuple(r[i] for i in range(field_count) if i not in sic)
  
          columns = list(zip(*(extract(r) for r in header)))
          names = ic + columns
  
          # If we find unnamed columns all in a single
          # level, then our header was too long.
          for n in range(len(columns[0])):
              if all(ensure_str(col[n]) in self.unnamed_cols for col in columns):
                  header = ",".join(str(x) for x in self.header)
                  raise ParserError(
                      f"Passed header=[{header}] are too many rows "
                      "for this multi_index of columns"
                  )
  
          # Clean the column names (if we have an index_col).
          if len(ic):
              col_names = [
                  r[0] if ((r[0] is not None) and r[0] not in self.unnamed_cols) else None
                  for r in header
              ]
          else:
              col_names = [None] * len(header)
  
          passed_names = True
  
          return names, index_names, col_names, passed_names
  
      def _maybe_dedup_names(self, names):
          # see gh-7160 and gh-9424: this helps to provide
          # immediate alleviation of the duplicate names
          # issue and appears to be satisfactory to users,
          # but ultimately, not needing to butcher the names
          # would be nice!
          if self.mangle_dupe_cols:
              names = list(names)  # so we can index
              # pandas\io\parsers.py:1559: error: Need type annotation for
              # 'counts'  [var-annotated]
              counts = defaultdict(int)  # type: ignore[var-annotated]
              is_potential_mi = _is_potential_multi_index(names, self.index_col)
  
              for i, col in enumerate(names):
                  cur_count = counts[col]
  
                  while cur_count > 0:
                      counts[col] = cur_count + 1
  
                      if is_potential_mi:
                          col = col[:-1] + (f"{col[-1]}.{cur_count}",)
                      else:
                          col = f"{col}.{cur_count}"
                      cur_count = counts[col]
  
                  names[i] = col
                  counts[col] = cur_count + 1
  
          return names
  
      def _maybe_make_multi_index_columns(self, columns, col_names=None):
          # possibly create a column mi here
          if _is_potential_multi_index(columns):
              columns = MultiIndex.from_tuples(columns, names=col_names)
          return columns
  
      def _make_index(self, data, alldata, columns, indexnamerow=False):
          if not _is_index_col(self.index_col) or not self.index_col:
              index = None
  
          elif not self._has_complex_date_col:
              index = self._get_simple_index(alldata, columns)
              index = self._agg_index(index)
          elif self._has_complex_date_col:
              if not self._name_processed:
                  (self.index_names, _, self.index_col) = _clean_index_names(
                      list(columns), self.index_col, self.unnamed_cols
                  )
                  self._name_processed = True
              index = self._get_complex_date_index(data, columns)
              index = self._agg_index(index, try_parse_dates=False)
  
          # add names for the index
          if indexnamerow:
              coffset = len(indexnamerow) - len(columns)
              # pandas\io\parsers.py:1604: error: Item "None" of "Optional[Any]"
              # has no attribute "set_names"  [union-attr]
              index = index.set_names(indexnamerow[:coffset])  # type: ignore[union-attr]
  
          # maybe create a mi on the columns
          columns = self._maybe_make_multi_index_columns(columns, self.col_names)
  
          return index, columns
  
      _implicit_index = False
  
      def _get_simple_index(self, data, columns):
          def ix(col):
              if not isinstance(col, str):
                  return col
              raise ValueError(f"Index {col} invalid")
  
          to_remove = []
          index = []
          for idx in self.index_col:
              i = ix(idx)
              to_remove.append(i)
              index.append(data[i])
  
          # remove index items from content and columns, don't pop in
          # loop
          for i in sorted(to_remove, reverse=True):
              data.pop(i)
              if not self._implicit_index:
                  columns.pop(i)
  
          return index
  
      def _get_complex_date_index(self, data, col_names):
          def _get_name(icol):
              if isinstance(icol, str):
                  return icol
  
              if col_names is None:
                  raise ValueError(f"Must supply column order to use {icol!s} as index")
  
              for i, c in enumerate(col_names):
                  if i == icol:
                      return c
  
          to_remove = []
          index = []
          for idx in self.index_col:
              name = _get_name(idx)
              to_remove.append(name)
              index.append(data[name])
  
          # remove index items from content and columns, don't pop in
          # loop
          for c in sorted(to_remove, reverse=True):
              data.pop(c)
              col_names.remove(c)
  
          return index
  
      def _agg_index(self, index, try_parse_dates=True) -> Index:
          arrays = []
  
          for i, arr in enumerate(index):
  
              if try_parse_dates and self._should_parse_dates(i):
                  arr = self._date_conv(arr)
  
              if self.na_filter:
                  col_na_values = self.na_values
                  col_na_fvalues = self.na_fvalues
              else:
                  col_na_values = set()
                  col_na_fvalues = set()
  
              if isinstance(self.na_values, dict):
                  # pandas\io\parsers.py:1678: error: Value of type
                  # "Optional[Any]" is not indexable  [index]
                  col_name = self.index_names[i]  # type: ignore[index]
                  if col_name is not None:
                      col_na_values, col_na_fvalues = _get_na_values(
                          col_name, self.na_values, self.na_fvalues, self.keep_default_na
                      )
  
              arr, _ = self._infer_types(arr, col_na_values | col_na_fvalues)
              arrays.append(arr)
  
          names = self.index_names
          index = ensure_index_from_sequences(arrays, names)
  
          return index
  
      def _convert_to_ndarrays(
          self, dct, na_values, na_fvalues, verbose=False, converters=None, dtypes=None
      ):
          result = {}
          for c, values in dct.items():
              conv_f = None if converters is None else converters.get(c, None)
              if isinstance(dtypes, dict):
                  cast_type = dtypes.get(c, None)
              else:
                  # single dtype or None
                  cast_type = dtypes
  
              if self.na_filter:
                  col_na_values, col_na_fvalues = _get_na_values(
                      c, na_values, na_fvalues, self.keep_default_na
                  )
              else:
                  col_na_values, col_na_fvalues = set(), set()
  
              if conv_f is not None:
                  # conv_f applied to data before inference
                  if cast_type is not None:
                      warnings.warn(
                          (
                              "Both a converter and dtype were specified "
                              f"for column {c} - only the converter will be used"
                          ),
                          ParserWarning,
                          stacklevel=7,
                      )
  
                  try:
                      values = lib.map_infer(values, conv_f)
                  except ValueError:
                      mask = algorithms.isin(values, list(na_values)).view(np.uint8)
                      values = lib.map_infer_mask(values, conv_f, mask)
  
                  cvals, na_count = self._infer_types(
                      values, set(col_na_values) | col_na_fvalues, try_num_bool=False
                  )
              else:
                  is_ea = is_extension_array_dtype(cast_type)
                  is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type)
                  # skip inference if specified dtype is object
                  # or casting to an EA
                  try_num_bool = not (cast_type and is_str_or_ea_dtype)
  
                  # general type inference and conversion
                  cvals, na_count = self._infer_types(
                      values, set(col_na_values) | col_na_fvalues, try_num_bool
                  )
  
                  # type specified in dtype param or cast_type is an EA
                  if cast_type and (
                      not is_dtype_equal(cvals, cast_type)
                      or is_extension_array_dtype(cast_type)
                  ):
                      if not is_ea and na_count > 0:
                          try:
                              if is_bool_dtype(cast_type):
                                  raise ValueError(
                                      f"Bool column has NA values in column {c}"
                                  )
                          except (AttributeError, TypeError):
                              # invalid input to is_bool_dtype
                              pass
                      cast_type = pandas_dtype(cast_type)
                      cvals = self._cast_types(cvals, cast_type, c)
  
              result[c] = cvals
              if verbose and na_count:
                  print(f"Filled {na_count} NA values in column {c!s}")
          return result
  
      def _infer_types(self, values, na_values, try_num_bool=True):
          """
          Infer types of values, possibly casting
  
          Parameters
          ----------
          values : ndarray
          na_values : set
          try_num_bool : bool, default try
             try to cast values to numeric (first preference) or boolean
  
          Returns
          -------
          converted : ndarray
          na_count : int
          """
          na_count = 0
          if issubclass(values.dtype.type, (np.number, np.bool_)):
              mask = algorithms.isin(values, list(na_values))
              na_count = mask.sum()
              if na_count > 0:
                  if is_integer_dtype(values):
                      values = values.astype(np.float64)
                  np.putmask(values, mask, np.nan)
              return values, na_count
  
          if try_num_bool and is_object_dtype(values.dtype):
              # exclude e.g DatetimeIndex here
              try:
                  result = lib.maybe_convert_numeric(values, na_values, False)
              except (ValueError, TypeError):
                  # e.g. encountering datetime string gets ValueError
                  #  TypeError can be raised in floatify
                  result = values
                  na_count = parsers.sanitize_objects(result, na_values, False)
              else:
                  na_count = isna(result).sum()
          else:
              result = values
              if values.dtype == np.object_:
                  na_count = parsers.sanitize_objects(values, na_values, False)
  
          if result.dtype == np.object_ and try_num_bool:
              result = libops.maybe_convert_bool(
                  np.asarray(values),
                  true_values=self.true_values,
                  false_values=self.false_values,
              )
  
          return result, na_count
  
      def _cast_types(self, values, cast_type, column):
          """
          Cast values to specified type
  
          Parameters
          ----------
          values : ndarray
          cast_type : string or np.dtype
             dtype to cast values to
          column : string
              column name - used only for error reporting
  
          Returns
          -------
          converted : ndarray
          """
          if is_categorical_dtype(cast_type):
              known_cats = (
                  isinstance(cast_type, CategoricalDtype)
                  and cast_type.categories is not None
              )
  
              if not is_object_dtype(values) and not known_cats:
                  # TODO: this is for consistency with
                  # c-parser which parses all categories
                  # as strings
                  values = astype_nansafe(values, str)
  
              cats = Index(values).unique().dropna()
              values = Categorical._from_inferred_categories(
                  cats, cats.get_indexer(values), cast_type, true_values=self.true_values
              )
  
          # use the EA's implementation of casting
          elif is_extension_array_dtype(cast_type):
              # ensure cast_type is an actual dtype and not a string
              cast_type = pandas_dtype(cast_type)
              array_type = cast_type.construct_array_type()
              try:
                  return array_type._from_sequence_of_strings(values, dtype=cast_type)
              except NotImplementedError as err:
                  raise NotImplementedError(
                      f"Extension Array: {array_type} must implement "
                      "_from_sequence_of_strings in order to be used in parser methods"
                  ) from err
  
          else:
              try:
                  values = astype_nansafe(values, cast_type, copy=True, skipna=True)
              except ValueError as err:
                  raise ValueError(
                      f"Unable to convert column {column} to type {cast_type}"
                  ) from err
          return values
  
      def _do_date_conversions(self, names, data):
          # returns data, columns
  
          if self.parse_dates is not None:
              data, names = _process_date_conversion(
                  data,
                  self._date_conv,
                  self.parse_dates,
                  self.index_col,
                  self.index_names,
                  names,
                  keep_date_col=self.keep_date_col,
              )
  
          return names, data
  
  
  class CParserWrapper(ParserBase):
      def __init__(self, src: FilePathOrBuffer, **kwds):
          self.kwds = kwds
          kwds = kwds.copy()
  
          ParserBase.__init__(self, kwds)
  
          # #2442
          kwds["allow_leading_cols"] = self.index_col is not False
  
          # GH20529, validate usecol arg before TextReader
          self.usecols, self.usecols_dtype = _validate_usecols_arg(kwds["usecols"])
          kwds["usecols"] = self.usecols
  
          # open handles
          self._open_handles(src, kwds)
          assert self.handles is not None
          for key in ("storage_options", "encoding", "memory_map", "compression"):
              kwds.pop(key, None)
          if self.handles.is_mmap and hasattr(self.handles.handle, "mmap"):
              # pandas\io\parsers.py:1861: error: Item "IO[Any]" of
              # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
              # TextIOWrapper, mmap]" has no attribute "mmap"  [union-attr]
  
              # pandas\io\parsers.py:1861: error: Item "RawIOBase" of
              # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
              # TextIOWrapper, mmap]" has no attribute "mmap"  [union-attr]
  
              # pandas\io\parsers.py:1861: error: Item "BufferedIOBase" of
              # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
              # TextIOWrapper, mmap]" has no attribute "mmap"  [union-attr]
  
              # pandas\io\parsers.py:1861: error: Item "TextIOBase" of
              # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
              # TextIOWrapper, mmap]" has no attribute "mmap"  [union-attr]
  
              # pandas\io\parsers.py:1861: error: Item "TextIOWrapper" of
              # "Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase,
              # TextIOWrapper, mmap]" has no attribute "mmap"  [union-attr]
  
              # pandas\io\parsers.py:1861: error: Item "mmap" of "Union[IO[Any],
              # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap]" has
              # no attribute "mmap"  [union-attr]
              self.handles.handle = self.handles.handle.mmap  # type: ignore[union-attr]
  
          try:
              self._reader = parsers.TextReader(self.handles.handle, **kwds)
          except Exception:
              self.handles.close()
              raise
          self.unnamed_cols = self._reader.unnamed_cols
  
          passed_names = self.names is None
  
          if self._reader.header is None:
              self.names = None
          else:
              if len(self._reader.header) > 1:
                  # we have a multi index in the columns
                  (
                      self.names,
                      self.index_names,
                      self.col_names,
                      passed_names,
                  ) = self._extract_multi_indexer_columns(
                      self._reader.header, self.index_names, self.col_names, passed_names
                  )
              else:
                  self.names = list(self._reader.header[0])
  
          if self.names is None:
              if self.prefix:
                  self.names = [
                      f"{self.prefix}{i}" for i in range(self._reader.table_width)
                  ]
              else:
                  self.names = list(range(self._reader.table_width))
  
          # gh-9755
          #
          # need to set orig_names here first
          # so that proper indexing can be done
          # with _set_noconvert_columns
          #
          # once names has been filtered, we will
          # then set orig_names again to names
          self.orig_names = self.names[:]
  
          if self.usecols:
              usecols = _evaluate_usecols(self.usecols, self.orig_names)
  
              # GH 14671
              # assert for mypy, orig_names is List or None, None would error in issubset
              assert self.orig_names is not None
              if self.usecols_dtype == "string" and not set(usecols).issubset(
                  self.orig_names
              ):
                  _validate_usecols_names(usecols, self.orig_names)
  
              if len(self.names) > len(usecols):
                  self.names = [
                      n
                      for i, n in enumerate(self.names)
                      if (i in usecols or n in usecols)
                  ]
  
              if len(self.names) < len(usecols):
                  _validate_usecols_names(usecols, self.names)
  
          self._validate_parse_dates_presence(self.names)
          self._set_noconvert_columns()
  
          self.orig_names = self.names
  
          if not self._has_complex_date_col:
              if self._reader.leading_cols == 0 and _is_index_col(self.index_col):
  
                  self._name_processed = True
                  (index_names, self.names, self.index_col) = _clean_index_names(
                      self.names, self.index_col, self.unnamed_cols
                  )
  
                  if self.index_names is None:
                      self.index_names = index_names
  
              if self._reader.header is None and not passed_names:
                  # pandas\io\parsers.py:1997: error: Argument 1 to "len" has
                  # incompatible type "Optional[Any]"; expected "Sized"
                  # [arg-type]
                  self.index_names = [None] * len(
                      self.index_names  # type: ignore[arg-type]
                  )
  
          self._implicit_index = self._reader.leading_cols > 0
  
      def close(self) -> None:
          super().close()
  
          # close additional handles opened by C parser
          try:
              self._reader.close()
          except ValueError:
              pass
  
      def _set_noconvert_columns(self):
          """
          Set the columns that should not undergo dtype conversions.
  
          Currently, any column that is involved with date parsing will not
          undergo such conversions.
          """
          names = self.orig_names
          if self.usecols_dtype == "integer":
              # A set of integers will be converted to a list in
              # the correct order every single time.
              usecols = list(self.usecols)
              usecols.sort()
          elif callable(self.usecols) or self.usecols_dtype not in ("empty", None):
              # The names attribute should have the correct columns
              # in the proper order for indexing with parse_dates.
              usecols = self.names[:]
          else:
              # Usecols is empty.
  
              # pandas\io\parsers.py:2030: error: Incompatible types in
              # assignment (expression has type "None", variable has type
              # "List[Any]")  [assignment]
              usecols = None  # type: ignore[assignment]
  
          def _set(x):
              if usecols is not None and is_integer(x):
                  x = usecols[x]
  
              if not is_integer(x):
                  # assert for mypy, names is List or None, None would error when calling
                  # .index()
                  assert names is not None
                  x = names.index(x)
  
              self._reader.set_noconvert(x)
  
          if isinstance(self.parse_dates, list):
              for val in self.parse_dates:
                  if isinstance(val, list):
                      for k in val:
                          _set(k)
                  else:
                      _set(val)
  
          elif isinstance(self.parse_dates, dict):
              for val in self.parse_dates.values():
                  if isinstance(val, list):
                      for k in val:
                          _set(k)
                  else:
                      _set(val)
  
          elif self.parse_dates:
              if isinstance(self.index_col, list):
                  for k in self.index_col:
                      _set(k)
              elif self.index_col is not None:
                  _set(self.index_col)
  
      def set_error_bad_lines(self, status):
          self._reader.set_error_bad_lines(int(status))
  
      def read(self, nrows=None):
          try:
              data = self._reader.read(nrows)
          except StopIteration:
              if self._first_chunk:
                  self._first_chunk = False
                  names = self._maybe_dedup_names(self.orig_names)
                  index, columns, col_dict = _get_empty_meta(
                      names,
                      self.index_col,
                      self.index_names,
                      dtype=self.kwds.get("dtype"),
                  )
                  columns = self._maybe_make_multi_index_columns(columns, self.col_names)
  
                  if self.usecols is not None:
                      columns = self._filter_usecols(columns)
  
                  col_dict = {k: v for k, v in col_dict.items() if k in columns}
  
                  return index, columns, col_dict
  
              else:
                  self.close()
                  raise
  
          # Done with first read, next time raise StopIteration
          self._first_chunk = False
  
          names = self.names
  
          if self._reader.leading_cols:
              if self._has_complex_date_col:
                  raise NotImplementedError("file structure not yet supported")
  
              # implicit index, no index names
              arrays = []
  
              for i in range(self._reader.leading_cols):
                  if self.index_col is None:
                      values = data.pop(i)
                  else:
                      values = data.pop(self.index_col[i])
  
                  values = self._maybe_parse_dates(values, i, try_parse_dates=True)
                  arrays.append(values)
  
              index = ensure_index_from_sequences(arrays)
  
              if self.usecols is not None:
                  names = self._filter_usecols(names)
  
              names = self._maybe_dedup_names(names)
  
              # rename dict keys
              data = sorted(data.items())
              data = {k: v for k, (i, v) in zip(names, data)}
  
              names, data = self._do_date_conversions(names, data)
  
          else:
              # rename dict keys
              data = sorted(data.items())
  
              # ugh, mutation
  
              # assert for mypy, orig_names is List or None, None would error in list(...)
              assert self.orig_names is not None
              names = list(self.orig_names)
              names = self._maybe_dedup_names(names)
  
              if self.usecols is not None:
                  names = self._filter_usecols(names)
  
              # columns as list
              alldata = [x[1] for x in data]
  
              data = {k: v for k, (i, v) in zip(names, data)}
  
              names, data = self._do_date_conversions(names, data)
              index, names = self._make_index(data, alldata, names)
  
          # maybe create a mi on the columns
          names = self._maybe_make_multi_index_columns(names, self.col_names)
  
          return index, names, data
  
      def _filter_usecols(self, names):
          # hackish
          usecols = _evaluate_usecols(self.usecols, names)
          if usecols is not None and len(names) != len(usecols):
              names = [
                  name for i, name in enumerate(names) if i in usecols or name in usecols
              ]
          return names
  
      def _get_index_names(self):
          names = list(self._reader.header[0])
          idx_names = None
  
          if self._reader.leading_cols == 0 and self.index_col is not None:
              (idx_names, names, self.index_col) = _clean_index_names(
                  names, self.index_col, self.unnamed_cols
              )
  
          return names, idx_names
  
      def _maybe_parse_dates(self, values, index, try_parse_dates=True):
          if try_parse_dates and self._should_parse_dates(index):
              values = self._date_conv(values)
          return values
  
  
  def TextParser(*args, **kwds):
      """
      Converts lists of lists/tuples into DataFrames with proper type inference
      and optional (e.g. string to datetime) conversion. Also enables iterating
      lazily over chunks of large files
  
      Parameters
      ----------
      data : file-like object or list
      delimiter : separator character to use
      dialect : str or csv.Dialect instance, optional
          Ignored if delimiter is longer than 1 character
      names : sequence, default
      header : int, default 0
          Row to use to parse column labels. Defaults to the first row. Prior
          rows will be discarded
      index_col : int or list, optional
          Column or columns to use as the (possibly hierarchical) index
      has_index_names: bool, default False
          True if the cols defined in index_col have an index name and are
          not in the header.
      na_values : scalar, str, list-like, or dict, optional
          Additional strings to recognize as NA/NaN.
      keep_default_na : bool, default True
      thousands : str, optional
          Thousands separator
      comment : str, optional
          Comment out remainder of line
      parse_dates : bool, default False
      keep_date_col : bool, default False
      date_parser : function, optional
      skiprows : list of integers
          Row numbers to skip
      skipfooter : int
          Number of line at bottom of file to skip
      converters : dict, optional
          Dict of functions for converting values in certain columns. Keys can
          either be integers or column labels, values are functions that take one
          input argument, the cell (not column) content, and return the
          transformed content.
      encoding : str, optional
          Encoding to use for UTF when reading/writing (ex. 'utf-8')
      squeeze : bool, default False
          returns Series if only one column.
      infer_datetime_format: bool, default False
          If True and `parse_dates` is True for a column, try to infer the
          datetime format based on the first datetime string. If the format
          can be inferred, there often will be a large parsing speed-up.
      float_precision : str, optional
          Specifies which converter the C engine should use for floating-point
          values. The options are `None` or `high` for the ordinary converter,
          `legacy` for the original lower precision pandas converter, and
          `round_trip` for the round-trip converter.
  
          .. versionchanged:: 1.2
      """
      kwds["engine"] = "python"
      return TextFileReader(*args, **kwds)
  
  
  def count_empty_vals(vals) -> int:
      return sum(1 for v in vals if v == "" or v is None)
  
  
  class PythonParser(ParserBase):
      def __init__(self, f: Union[FilePathOrBuffer, List], **kwds):
          """
          Workhorse function for processing nested list into DataFrame
          """
          ParserBase.__init__(self, kwds)
  
          self.data: Optional[Iterator[str]] = None
          self.buf: List = []
          self.pos = 0
          self.line_pos = 0
  
          self.skiprows = kwds["skiprows"]
  
          if callable(self.skiprows):
              self.skipfunc = self.skiprows
          else:
              self.skipfunc = lambda x: x in self.skiprows
  
          self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"])
          self.delimiter = kwds["delimiter"]
  
          self.quotechar = kwds["quotechar"]
          if isinstance(self.quotechar, str):
              self.quotechar = str(self.quotechar)
  
          self.escapechar = kwds["escapechar"]
          self.doublequote = kwds["doublequote"]
          self.skipinitialspace = kwds["skipinitialspace"]
          self.lineterminator = kwds["lineterminator"]
          self.quoting = kwds["quoting"]
          self.usecols, _ = _validate_usecols_arg(kwds["usecols"])
          self.skip_blank_lines = kwds["skip_blank_lines"]
  
          self.warn_bad_lines = kwds["warn_bad_lines"]
          self.error_bad_lines = kwds["error_bad_lines"]
  
          self.names_passed = kwds["names"] or None
  
          self.has_index_names = False
          if "has_index_names" in kwds:
              self.has_index_names = kwds["has_index_names"]
  
          self.verbose = kwds["verbose"]
          self.converters = kwds["converters"]
  
          self.dtype = kwds["dtype"]
          self.thousands = kwds["thousands"]
          self.decimal = kwds["decimal"]
  
          self.comment = kwds["comment"]
  
          # Set self.data to something that can read lines.
          if isinstance(f, list):
              # read_excel: f is a list
              self.data = cast(Iterator[str], f)
          else:
              self._open_handles(f, kwds)
              assert self.handles is not None
              assert hasattr(self.handles.handle, "readline")
              self._make_reader(self.handles.handle)
  
          # Get columns in two steps: infer from data, then
          # infer column indices from self.usecols if it is specified.
          self._col_indices = None
          try:
              (
                  self.columns,
                  self.num_original_columns,
                  self.unnamed_cols,
              ) = self._infer_columns()
          except (TypeError, ValueError):
              self.close()
              raise
  
          # Now self.columns has the set of columns that we will process.
          # The original set is stored in self.original_columns.
          if len(self.columns) > 1:
              # we are processing a multi index column
              (
                  self.columns,
                  self.index_names,
                  self.col_names,
                  _,
              ) = self._extract_multi_indexer_columns(
                  self.columns, self.index_names, self.col_names
              )
              # Update list of original names to include all indices.
              self.num_original_columns = len(self.columns)
          else:
              self.columns = self.columns[0]
  
          # get popped off for index
          self.orig_names = list(self.columns)
  
          # needs to be cleaned/refactored
          # multiple date column thing turning into a real spaghetti factory
  
          if not self._has_complex_date_col:
              (index_names, self.orig_names, self.columns) = self._get_index_name(
                  self.columns
              )
              self._name_processed = True
              if self.index_names is None:
                  self.index_names = index_names
  
          self._validate_parse_dates_presence(self.columns)
          if self.parse_dates:
              self._no_thousands_columns = self._set_no_thousands_columns()
          else:
              self._no_thousands_columns = None
  
          if len(self.decimal) != 1:
              raise ValueError("Only length-1 decimal markers supported")
  
          decimal = re.escape(self.decimal)
          if self.thousands is None:
              regex = fr"^\-?[0-9]*({decimal}[0-9]*)?([0-9](E|e)\-?[0-9]*)?$"
          else:
              thousands = re.escape(self.thousands)
              regex = (
                  fr"^\-?([0-9]+{thousands}|[0-9])*({decimal}[0-9]*)?"
                  fr"([0-9](E|e)\-?[0-9]*)?$"
              )
          self.num = re.compile(regex)
  
      def _set_no_thousands_columns(self):
          # Create a set of column ids that are not to be stripped of thousands
          # operators.
          noconvert_columns = set()
  
          def _set(x):
              if is_integer(x):
                  noconvert_columns.add(x)
              else:
                  noconvert_columns.add(self.columns.index(x))
  
          if isinstance(self.parse_dates, list):
              for val in self.parse_dates:
                  if isinstance(val, list):
                      for k in val:
                          _set(k)
                  else:
                      _set(val)
  
          elif isinstance(self.parse_dates, dict):
              for val in self.parse_dates.values():
                  if isinstance(val, list):
                      for k in val:
                          _set(k)
                  else:
                      _set(val)
  
          elif self.parse_dates:
              if isinstance(self.index_col, list):
                  for k in self.index_col:
                      _set(k)
              elif self.index_col is not None:
                  _set(self.index_col)
  
          return noconvert_columns
  
      def _make_reader(self, f):
          sep = self.delimiter
  
          if sep is None or len(sep) == 1:
              if self.lineterminator:
                  raise ValueError(
                      "Custom line terminators not supported in python parser (yet)"
                  )
  
              class MyDialect(csv.Dialect):
                  delimiter = self.delimiter
                  quotechar = self.quotechar
                  escapechar = self.escapechar
                  doublequote = self.doublequote
                  skipinitialspace = self.skipinitialspace
                  quoting = self.quoting
                  lineterminator = "\n"
  
              dia = MyDialect
  
              if sep is not None:
                  dia.delimiter = sep
              else:
                  # attempt to sniff the delimiter from the first valid line,
                  # i.e. no comment line and not in skiprows
                  line = f.readline()
                  lines = self._check_comments([[line]])[0]
                  while self.skipfunc(self.pos) or not lines:
                      self.pos += 1
                      line = f.readline()
                      lines = self._check_comments([[line]])[0]
  
                  # since `line` was a string, lines will be a list containing
                  # only a single string
                  line = lines[0]
  
                  self.pos += 1
                  self.line_pos += 1
                  sniffed = csv.Sniffer().sniff(line)
                  dia.delimiter = sniffed.delimiter
  
                  # Note: encoding is irrelevant here
                  line_rdr = csv.reader(StringIO(line), dialect=dia)
                  self.buf.extend(list(line_rdr))
  
              # Note: encoding is irrelevant here
              reader = csv.reader(f, dialect=dia, strict=True)
  
          else:
  
              def _read():
                  line = f.readline()
                  pat = re.compile(sep)
  
                  yield pat.split(line.strip())
  
                  for line in f:
                      yield pat.split(line.strip())
  
              reader = _read()
  
          # pandas\io\parsers.py:2427: error: Incompatible types in assignment
          # (expression has type "_reader", variable has type "Union[IO[Any],
          # RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap, None]")
          # [assignment]
          self.data = reader  # type: ignore[assignment]
  
      def read(self, rows=None):
          try:
              content = self._get_lines(rows)
          except StopIteration:
              if self._first_chunk:
                  content = []
              else:
                  self.close()
                  raise
  
          # done with first read, next time raise StopIteration
          self._first_chunk = False
  
          # pandas\io\parsers.py:2480: error: Argument 1 to "list" has
          # incompatible type "Optional[Any]"; expected "Iterable[Any]"
          # [arg-type]
          columns = list(self.orig_names)  # type: ignore[arg-type]
          if not len(content):  # pragma: no cover
              # DataFrame with the right metadata, even though it's length 0
              names = self._maybe_dedup_names(self.orig_names)
              index, columns, col_dict = _get_empty_meta(
                  names, self.index_col, self.index_names, self.dtype
              )
              columns = self._maybe_make_multi_index_columns(columns, self.col_names)
              return index, columns, col_dict
  
          # handle new style for names in index
          count_empty_content_vals = count_empty_vals(content[0])
          indexnamerow = None
          if self.has_index_names and count_empty_content_vals == len(columns):
              indexnamerow = content[0]
              content = content[1:]
  
          alldata = self._rows_to_cols(content)
          data, columns = self._exclude_implicit_index(alldata)
  
          columns, data = self._do_date_conversions(columns, data)
  
          data = self._convert_data(data)
          index, columns = self._make_index(data, alldata, columns, indexnamerow)
  
          return index, columns, data
  
      def _exclude_implicit_index(self, alldata):
          names = self._maybe_dedup_names(self.orig_names)
  
          offset = 0
          if self._implicit_index:
              offset = len(self.index_col)
  
          if self._col_indices is not None and len(names) != len(self._col_indices):
              names = [names[i] for i in sorted(self._col_indices)]
  
          return {name: alldata[i + offset] for i, name in enumerate(names)}, names
  
      # legacy
      def get_chunk(self, size=None):
          if size is None:
              # pandas\io\parsers.py:2528: error: "PythonParser" has no attribute
              # "chunksize"  [attr-defined]
              size = self.chunksize  # type: ignore[attr-defined]
          return self.read(rows=size)
  
      def _convert_data(self, data):
          # apply converters
          def _clean_mapping(mapping):
              """converts col numbers to names"""
              clean = {}
              for col, v in mapping.items():
                  # pandas\io\parsers.py:2537: error: Unsupported right operand
                  # type for in ("Optional[Any]")  [operator]
                  if (
                      isinstance(col, int)
                      and col not in self.orig_names  # type: ignore[operator]
                  ):
                      # pandas\io\parsers.py:2538: error: Value of type
                      # "Optional[Any]" is not indexable  [index]
                      col = self.orig_names[col]  # type: ignore[index]
                  clean[col] = v
              return clean
  
          clean_conv = _clean_mapping(self.converters)
          if not isinstance(self.dtype, dict):
              # handles single dtype applied to all columns
              clean_dtypes = self.dtype
          else:
              clean_dtypes = _clean_mapping(self.dtype)
  
          # Apply NA values.
          clean_na_values = {}
          clean_na_fvalues = {}
  
          if isinstance(self.na_values, dict):
              for col in self.na_values:
                  na_value = self.na_values[col]
                  na_fvalue = self.na_fvalues[col]
  
                  # pandas\io\parsers.py:2558: error: Unsupported right operand
                  # type for in ("Optional[Any]")  [operator]
                  if (
                      isinstance(col, int)
                      and col not in self.orig_names  # type: ignore[operator]
                  ):
                      # pandas\io\parsers.py:2559: error: Value of type
                      # "Optional[Any]" is not indexable  [index]
                      col = self.orig_names[col]  # type: ignore[index]
  
                  clean_na_values[col] = na_value
                  clean_na_fvalues[col] = na_fvalue
          else:
              clean_na_values = self.na_values
              clean_na_fvalues = self.na_fvalues
  
          return self._convert_to_ndarrays(
              data,
              clean_na_values,
              clean_na_fvalues,
              self.verbose,
              clean_conv,
              clean_dtypes,
          )
  
      def _infer_columns(self):
          names = self.names
          num_original_columns = 0
          clear_buffer = True
          # pandas\io\parsers.py:2580: error: Need type annotation for
          # 'unnamed_cols' (hint: "unnamed_cols: Set[<type>] = ...")
          # [var-annotated]
          unnamed_cols = set()  # type: ignore[var-annotated]
  
          if self.header is not None:
              header = self.header
  
              if isinstance(header, (list, tuple, np.ndarray)):
                  have_mi_columns = len(header) > 1
                  # we have a mi columns, so read an extra line
                  if have_mi_columns:
                      header = list(header) + [header[-1] + 1]
              else:
                  have_mi_columns = False
                  header = [header]
  
              # pandas\io\parsers.py:2594: error: Need type annotation for
              # 'columns' (hint: "columns: List[<type>] = ...")  [var-annotated]
              columns = []  # type: ignore[var-annotated]
              for level, hr in enumerate(header):
                  try:
                      line = self._buffered_line()
  
                      while self.line_pos <= hr:
                          line = self._next_line()
  
                  except StopIteration as err:
                      if self.line_pos < hr:
                          raise ValueError(
                              f"Passed header={hr} but only {self.line_pos + 1} lines in "
                              "file"
                          ) from err
  
                      # We have an empty file, so check
                      # if columns are provided. That will
                      # serve as the 'line' for parsing
                      if have_mi_columns and hr > 0:
                          if clear_buffer:
                              self._clear_buffer()
                          columns.append([None] * len(columns[-1]))
                          return columns, num_original_columns, unnamed_cols
  
                      if not self.names:
                          raise EmptyDataError("No columns to parse from file") from err
  
                      line = self.names[:]
  
                  this_columns = []
                  this_unnamed_cols = []
  
                  for i, c in enumerate(line):
                      if c == "":
                          if have_mi_columns:
                              col_name = f"Unnamed: {i}_level_{level}"
                          else:
                              col_name = f"Unnamed: {i}"
  
                          this_unnamed_cols.append(i)
                          this_columns.append(col_name)
                      else:
                          this_columns.append(c)
  
                  if not have_mi_columns and self.mangle_dupe_cols:
                      # pandas\io\parsers.py:2639: error: Need type annotation
                      # for 'counts'  [var-annotated]
                      counts = defaultdict(int)  # type: ignore[var-annotated]
  
                      for i, col in enumerate(this_columns):
                          cur_count = counts[col]
  
                          while cur_count > 0:
                              counts[col] = cur_count + 1
                              col = f"{col}.{cur_count}"
                              cur_count = counts[col]
  
                          this_columns[i] = col
                          counts[col] = cur_count + 1
                  elif have_mi_columns:
  
                      # if we have grabbed an extra line, but its not in our
                      # format so save in the buffer, and create an blank extra
                      # line for the rest of the parsing code
                      if hr == header[-1]:
                          lc = len(this_columns)
                          ic = len(self.index_col) if self.index_col is not None else 0
                          unnamed_count = len(this_unnamed_cols)
  
                          if lc != unnamed_count and lc - ic > unnamed_count:
                              clear_buffer = False
                              # pandas\io\parsers.py:2663: error: List item 0 has
                              # incompatible type "None"; expected "str"
                              # [list-item]
                              this_columns = [None] * lc  # type: ignore[list-item]
                              self.buf = [self.buf[-1]]
  
                  # pandas\io\parsers.py:2666: error: Argument 1 to "append" of
                  # "list" has incompatible type "List[str]"; expected
                  # "List[None]"  [arg-type]
                  columns.append(this_columns)  # type: ignore[arg-type]
                  unnamed_cols.update({this_columns[i] for i in this_unnamed_cols})
  
                  if len(columns) == 1:
                      num_original_columns = len(this_columns)
  
              if clear_buffer:
                  self._clear_buffer()
  
              if names is not None:
                  if len(names) > len(columns[0]):
                      raise ValueError(
                          "Number of passed names did not match "
                          "number of header fields in the file"
                      )
                  if len(columns) > 1:
                      raise TypeError("Cannot pass names with multi-index columns")
  
                  if self.usecols is not None:
                      # Set _use_cols. We don't store columns because they are
                      # overwritten.
                      self._handle_usecols(columns, names)
                  else:
                      self._col_indices = None
                      num_original_columns = len(names)
                  columns = [names]
              else:
                  columns = self._handle_usecols(columns, columns[0])
          else:
              try:
                  line = self._buffered_line()
  
              except StopIteration as err:
                  if not names:
                      raise EmptyDataError("No columns to parse from file") from err
  
                  line = names[:]
  
              ncols = len(line)
              num_original_columns = ncols
  
              if not names:
                  if self.prefix:
                      # pandas\io\parsers.py:2711: error: List comprehension has
                      # incompatible type List[str]; expected List[None]  [misc]
                      columns = [
                          [
                              f"{self.prefix}{i}"  # type: ignore[misc]
                              for i in range(ncols)
                          ]
                      ]
                  else:
                      # pandas\io\parsers.py:2713: error: Argument 1 to "list"
                      # has incompatible type "range"; expected "Iterable[None]"
                      # [arg-type]
                      columns = [list(range(ncols))]  # type: ignore[arg-type]
                  columns = self._handle_usecols(columns, columns[0])
              else:
                  if self.usecols is None or len(names) >= num_original_columns:
                      columns = self._handle_usecols([names], names)
                      num_original_columns = len(names)
                  else:
                      if not callable(self.usecols) and len(names) != len(self.usecols):
                          raise ValueError(
                              "Number of passed names did not match number of "
                              "header fields in the file"
                          )
                      # Ignore output but set used columns.
                      self._handle_usecols([names], names)
                      columns = [names]
                      num_original_columns = ncols
  
          return columns, num_original_columns, unnamed_cols
  
      def _handle_usecols(self, columns, usecols_key):
          """
          Sets self._col_indices
  
          usecols_key is used if there are string usecols.
          """
          if self.usecols is not None:
              if callable(self.usecols):
                  col_indices = _evaluate_usecols(self.usecols, usecols_key)
              elif any(isinstance(u, str) for u in self.usecols):
                  if len(columns) > 1:
                      raise ValueError(
                          "If using multiple headers, usecols must be integers."
                      )
                  col_indices = []
  
                  for col in self.usecols:
                      if isinstance(col, str):
                          try:
                              col_indices.append(usecols_key.index(col))
                          except ValueError:
                              _validate_usecols_names(self.usecols, usecols_key)
                      else:
                          col_indices.append(col)
              else:
                  col_indices = self.usecols
  
              columns = [
                  [n for i, n in enumerate(column) if i in col_indices]
                  for column in columns
              ]
              self._col_indices = col_indices
          return columns
  
      def _buffered_line(self):
          """
          Return a line from buffer, filling buffer if required.
          """
          if len(self.buf) > 0:
              return self.buf[0]
          else:
              return self._next_line()
  
      def _check_for_bom(self, first_row):
          """
          Checks whether the file begins with the BOM character.
          If it does, remove it. In addition, if there is quoting
          in the field subsequent to the BOM, remove it as well
          because it technically takes place at the beginning of
          the name, not the middle of it.
          """
          # first_row will be a list, so we need to check
          # that that list is not empty before proceeding.
          if not first_row:
              return first_row
  
          # The first element of this row is the one that could have the
          # BOM that we want to remove. Check that the first element is a
          # string before proceeding.
          if not isinstance(first_row[0], str):
              return first_row
  
          # Check that the string is not empty, as that would
          # obviously not have a BOM at the start of it.
          if not first_row[0]:
              return first_row
  
          # Since the string is non-empty, check that it does
          # in fact begin with a BOM.
          first_elt = first_row[0][0]
          if first_elt != _BOM:
              return first_row
  
          first_row_bom = first_row[0]
  
          if len(first_row_bom) > 1 and first_row_bom[1] == self.quotechar:
              start = 2
              quote = first_row_bom[1]
              end = first_row_bom[2:].index(quote) + 2
  
              # Extract the data between the quotation marks
              new_row = first_row_bom[start:end]
  
              # Extract any remaining data after the second
              # quotation mark.
              if len(first_row_bom) > end + 1:
                  new_row += first_row_bom[end + 1 :]
  
          else:
  
              # No quotation so just remove BOM from first element
              new_row = first_row_bom[1:]
          return [new_row] + first_row[1:]
  
      def _is_line_empty(self, line):
          """
          Check if a line is empty or not.
  
          Parameters
          ----------
          line : str, array-like
              The line of data to check.
  
          Returns
          -------
          boolean : Whether or not the line is empty.
          """
          return not line or all(not x for x in line)
  
      def _next_line(self):
          if isinstance(self.data, list):
              while self.skipfunc(self.pos):
                  self.pos += 1
  
              while True:
                  try:
                      line = self._check_comments([self.data[self.pos]])[0]
                      self.pos += 1
                      # either uncommented or blank to begin with
                      if not self.skip_blank_lines and (
                          self._is_line_empty(self.data[self.pos - 1]) or line
                      ):
                          break
                      elif self.skip_blank_lines:
                          ret = self._remove_empty_lines([line])
                          if ret:
                              line = ret[0]
                              break
                  except IndexError:
                      raise StopIteration
          else:
              while self.skipfunc(self.pos):
                  self.pos += 1
                  # assert for mypy, data is Iterator[str] or None, would error in next
                  assert self.data is not None
                  next(self.data)
  
              while True:
                  orig_line = self._next_iter_line(row_num=self.pos + 1)
                  self.pos += 1
  
                  if orig_line is not None:
                      line = self._check_comments([orig_line])[0]
  
                      if self.skip_blank_lines:
                          ret = self._remove_empty_lines([line])
  
                          if ret:
                              line = ret[0]
                              break
                      elif self._is_line_empty(orig_line) or line:
                          break
  
          # This was the first line of the file,
          # which could contain the BOM at the
          # beginning of it.
          if self.pos == 1:
              line = self._check_for_bom(line)
  
          self.line_pos += 1
          self.buf.append(line)
          return line
  
      def _alert_malformed(self, msg, row_num):
          """
          Alert a user about a malformed row.
  
          If `self.error_bad_lines` is True, the alert will be `ParserError`.
          If `self.warn_bad_lines` is True, the alert will be printed out.
  
          Parameters
          ----------
          msg : The error message to display.
          row_num : The row number where the parsing error occurred.
                    Because this row number is displayed, we 1-index,
                    even though we 0-index internally.
          """
          if self.error_bad_lines:
              raise ParserError(msg)
          elif self.warn_bad_lines:
              base = f"Skipping line {row_num}: "
              sys.stderr.write(base + msg + "\n")
  
      def _next_iter_line(self, row_num):
          """
          Wrapper around iterating through `self.data` (CSV source).
  
          When a CSV error is raised, we check for specific
          error messages that allow us to customize the
          error message displayed to the user.
  
          Parameters
          ----------
          row_num : The row number of the line being parsed.
          """
          try:
              # assert for mypy, data is Iterator[str] or None, would error in next
              assert self.data is not None
              return next(self.data)
          except csv.Error as e:
              if self.warn_bad_lines or self.error_bad_lines:
                  msg = str(e)
  
                  if "NULL byte" in msg or "line contains NUL" in msg:
                      msg = (
                          "NULL byte detected. This byte "
                          "cannot be processed in Python's "
                          "native csv library at the moment, "
                          "so please pass in engine='c' instead"
                      )
  
                  if self.skipfooter > 0:
                      reason = (
                          "Error could possibly be due to "
                          "parsing errors in the skipped footer rows "
                          "(the skipfooter keyword is only applied "
                          "after Python's csv library has parsed "
                          "all rows)."
                      )
                      msg += ". " + reason
  
                  self._alert_malformed(msg, row_num)
              return None
  
      def _check_comments(self, lines):
          if self.comment is None:
              return lines
          ret = []
          for line in lines:
              rl = []
              for x in line:
                  if (
                      not isinstance(x, str)
                      or self.comment not in x
                      or x in self.na_values
                  ):
                      rl.append(x)
                  else:
                      x = x[: x.find(self.comment)]
                      if len(x) > 0:
                          rl.append(x)
                      break
              ret.append(rl)
          return ret
  
      def _remove_empty_lines(self, lines):
          """
          Iterate through the lines and remove any that are
          either empty or contain only one whitespace value
  
          Parameters
          ----------
          lines : array-like
              The array of lines that we are to filter.
  
          Returns
          -------
          filtered_lines : array-like
              The same array of lines with the "empty" ones removed.
          """
          ret = []
          for line in lines:
              # Remove empty lines and lines with only one whitespace value
              if (
                  len(line) > 1
                  or len(line) == 1
                  and (not isinstance(line[0], str) or line[0].strip())
              ):
                  ret.append(line)
          return ret
  
      def _check_thousands(self, lines):
          if self.thousands is None:
              return lines
  
          return self._search_replace_num_columns(
              lines=lines, search=self.thousands, replace=""
          )
  
      def _search_replace_num_columns(self, lines, search, replace):
          ret = []
          for line in lines:
              rl = []
              for i, x in enumerate(line):
                  if (
                      not isinstance(x, str)
                      or search not in x
                      or (self._no_thousands_columns and i in self._no_thousands_columns)
                      or not self.num.search(x.strip())
                  ):
                      rl.append(x)
                  else:
                      rl.append(x.replace(search, replace))
              ret.append(rl)
          return ret
  
      def _check_decimal(self, lines):
          if self.decimal == _parser_defaults["decimal"]:
              return lines
  
          return self._search_replace_num_columns(
              lines=lines, search=self.decimal, replace="."
          )
  
      def _clear_buffer(self):
          self.buf = []
  
      _implicit_index = False
  
      def _get_index_name(self, columns):
          """
          Try several cases to get lines:
  
          0) There are headers on row 0 and row 1 and their
          total summed lengths equals the length of the next line.
          Treat row 0 as columns and row 1 as indices
          1) Look for implicit index: there are more columns
          on row 1 than row 0. If this is true, assume that row
          1 lists index columns and row 0 lists normal columns.
          2) Get index from the columns if it was listed.
          """
          orig_names = list(columns)
          columns = list(columns)
  
          try:
              line = self._next_line()
          except StopIteration:
              line = None
  
          try:
              next_line = self._next_line()
          except StopIteration:
              next_line = None
  
          # implicitly index_col=0 b/c 1 fewer column names
          implicit_first_cols = 0
          if line is not None:
              # leave it 0, #2442
              # Case 1
              if self.index_col is not False:
                  implicit_first_cols = len(line) - self.num_original_columns
  
              # Case 0
              if next_line is not None:
                  if len(next_line) == len(line) + self.num_original_columns:
                      # column and index names on diff rows
                      self.index_col = list(range(len(line)))
                      self.buf = self.buf[1:]
  
                      for c in reversed(line):
                          columns.insert(0, c)
  
                      # Update list of original names to include all indices.
                      orig_names = list(columns)
                      self.num_original_columns = len(columns)
                      return line, orig_names, columns
  
          if implicit_first_cols > 0:
              # Case 1
              self._implicit_index = True
              if self.index_col is None:
                  self.index_col = list(range(implicit_first_cols))
  
              index_name = None
  
          else:
              # Case 2
              (index_name, columns_, self.index_col) = _clean_index_names(
                  columns, self.index_col, self.unnamed_cols
              )
  
          return index_name, orig_names, columns
  
      def _rows_to_cols(self, content):
          col_len = self.num_original_columns
  
          if self._implicit_index:
              col_len += len(self.index_col)
  
          max_len = max(len(row) for row in content)
  
          # Check that there are no rows with too many
          # elements in their row (rows with too few
          # elements are padded with NaN).
          if max_len > col_len and self.index_col is not False and self.usecols is None:
  
              footers = self.skipfooter if self.skipfooter else 0
              bad_lines = []
  
              iter_content = enumerate(content)
              content_len = len(content)
              content = []
  
              for (i, l) in iter_content:
                  actual_len = len(l)
  
                  if actual_len > col_len:
                      if self.error_bad_lines or self.warn_bad_lines:
                          row_num = self.pos - (content_len - i + footers)
                          bad_lines.append((row_num, actual_len))
  
                          if self.error_bad_lines:
                              break
                  else:
                      content.append(l)
  
              for row_num, actual_len in bad_lines:
                  msg = (
                      f"Expected {col_len} fields in line {row_num + 1}, saw "
                      f"{actual_len}"
                  )
                  if (
                      self.delimiter
                      and len(self.delimiter) > 1
                      and self.quoting != csv.QUOTE_NONE
                  ):
                      # see gh-13374
                      reason = (
                          "Error could possibly be due to quotes being "
                          "ignored when a multi-char delimiter is used."
                      )
                      msg += ". " + reason
  
                  self._alert_malformed(msg, row_num + 1)
  
          # see gh-13320
          zipped_content = list(lib.to_object_array(content, min_width=col_len).T)
  
          if self.usecols:
              if self._implicit_index:
                  zipped_content = [
                      a
                      for i, a in enumerate(zipped_content)
                      if (
                          i < len(self.index_col)
                          # pandas\io\parsers.py:3159: error: Unsupported right
                          # operand type for in ("Optional[Any]")  [operator]
                          or i - len(self.index_col)  # type: ignore[operator]
                          in self._col_indices
                      )
                  ]
              else:
                  zipped_content = [
                      # pandas\io\parsers.py:3164: error: Unsupported right
                      # operand type for in ("Optional[Any]")  [operator]
                      a
                      for i, a in enumerate(zipped_content)
                      if i in self._col_indices  # type: ignore[operator]
                  ]
          return zipped_content
  
      def _get_lines(self, rows=None):
          lines = self.buf
          new_rows = None
  
          # already fetched some number
          if rows is not None:
              # we already have the lines in the buffer
              if len(self.buf) >= rows:
                  new_rows, self.buf = self.buf[:rows], self.buf[rows:]
  
              # need some lines
              else:
                  rows -= len(self.buf)
  
          if new_rows is None:
              if isinstance(self.data, list):
                  if self.pos > len(self.data):
                      raise StopIteration
                  if rows is None:
                      new_rows = self.data[self.pos :]
                      new_pos = len(self.data)
                  else:
                      new_rows = self.data[self.pos : self.pos + rows]
                      new_pos = self.pos + rows
  
                  # Check for stop rows. n.b.: self.skiprows is a set.
                  if self.skiprows:
                      new_rows = [
                          row
                          for i, row in enumerate(new_rows)
                          if not self.skipfunc(i + self.pos)
                      ]
  
                  lines.extend(new_rows)
                  self.pos = new_pos
  
              else:
                  new_rows = []
                  try:
                      if rows is not None:
                          for _ in range(rows):
                              # assert for mypy, data is Iterator[str] or None, would
                              # error in next
                              assert self.data is not None
                              new_rows.append(next(self.data))
                          lines.extend(new_rows)
                      else:
                          rows = 0
  
                          while True:
                              new_row = self._next_iter_line(row_num=self.pos + rows + 1)
                              rows += 1
  
                              if new_row is not None:
                                  new_rows.append(new_row)
  
                  except StopIteration:
                      if self.skiprows:
                          new_rows = [
                              row
                              for i, row in enumerate(new_rows)
                              if not self.skipfunc(i + self.pos)
                          ]
                      lines.extend(new_rows)
                      if len(lines) == 0:
                          raise
                  self.pos += len(new_rows)
  
              self.buf = []
          else:
              lines = new_rows
  
          if self.skipfooter:
              lines = lines[: -self.skipfooter]
  
          lines = self._check_comments(lines)
          if self.skip_blank_lines:
              lines = self._remove_empty_lines(lines)
          lines = self._check_thousands(lines)
          return self._check_decimal(lines)
  
  
  def _make_date_converter(
      date_parser=None, dayfirst=False, infer_datetime_format=False, cache_dates=True
  ):
      def converter(*date_cols):
          if date_parser is None:
              strs = parsing.concat_date_cols(date_cols)
  
              try:
                  return tools.to_datetime(
                      ensure_object(strs),
                      utc=None,
                      dayfirst=dayfirst,
                      errors="ignore",
                      infer_datetime_format=infer_datetime_format,
                      cache=cache_dates,
                  ).to_numpy()
  
              except ValueError:
                  return tools.to_datetime(
                      parsing.try_parse_dates(strs, dayfirst=dayfirst), cache=cache_dates
                  )
          else:
              try:
                  result = tools.to_datetime(
                      date_parser(*date_cols), errors="ignore", cache=cache_dates
                  )
                  if isinstance(result, datetime.datetime):
                      raise Exception("scalar parser")
                  return result
              except Exception:
                  try:
                      return tools.to_datetime(
                          parsing.try_parse_dates(
                              parsing.concat_date_cols(date_cols),
                              parser=date_parser,
                              dayfirst=dayfirst,
                          ),
                          errors="ignore",
                      )
                  except Exception:
                      return generic_parser(date_parser, *date_cols)
  
      return converter
  
  
  def _process_date_conversion(
      data_dict,
      converter,
      parse_spec,
      index_col,
      index_names,
      columns,
      keep_date_col=False,
  ):
      def _isindex(colspec):
          return (isinstance(index_col, list) and colspec in index_col) or (
              isinstance(index_names, list) and colspec in index_names
          )
  
      new_cols = []
      new_data = {}
  
      orig_names = columns
      columns = list(columns)
  
      date_cols = set()
  
      if parse_spec is None or isinstance(parse_spec, bool):
          return data_dict, columns
  
      if isinstance(parse_spec, list):
          # list of column lists
          for colspec in parse_spec:
              if is_scalar(colspec):
                  if isinstance(colspec, int) and colspec not in data_dict:
                      colspec = orig_names[colspec]
                  if _isindex(colspec):
                      continue
                  data_dict[colspec] = converter(data_dict[colspec])
              else:
                  new_name, col, old_names = _try_convert_dates(
                      converter, colspec, data_dict, orig_names
                  )
                  if new_name in data_dict:
                      raise ValueError(f"New date column already in dict {new_name}")
                  new_data[new_name] = col
                  new_cols.append(new_name)
                  date_cols.update(old_names)
  
      elif isinstance(parse_spec, dict):
          # dict of new name to column list
          for new_name, colspec in parse_spec.items():
              if new_name in data_dict:
                  raise ValueError(f"Date column {new_name} already in dict")
  
              _, col, old_names = _try_convert_dates(
                  converter, colspec, data_dict, orig_names
              )
  
              new_data[new_name] = col
              new_cols.append(new_name)
              date_cols.update(old_names)
  
      data_dict.update(new_data)
      new_cols.extend(columns)
  
      if not keep_date_col:
          for c in list(date_cols):
              data_dict.pop(c)
              new_cols.remove(c)
  
      return data_dict, new_cols
  
  
  def _try_convert_dates(parser, colspec, data_dict, columns):
      colset = set(columns)
      colnames = []
  
      for c in colspec:
          if c in colset:
              colnames.append(c)
          elif isinstance(c, int) and c not in columns:
              colnames.append(columns[c])
          else:
              colnames.append(c)
  
      new_name = "_".join(str(x) for x in colnames)
      to_parse = [data_dict[c] for c in colnames if c in data_dict]
  
      new_col = parser(*to_parse)
      return new_name, new_col, colnames
  
  
  def _clean_na_values(na_values, keep_default_na=True):
  
      if na_values is None:
          if keep_default_na:
              na_values = STR_NA_VALUES
          else:
              na_values = set()
          # pandas\io\parsers.py:3387: error: Need type annotation for
          # 'na_fvalues' (hint: "na_fvalues: Set[<type>] = ...")  [var-annotated]
          na_fvalues = set()  # type: ignore[var-annotated]
      elif isinstance(na_values, dict):
          old_na_values = na_values.copy()
          na_values = {}  # Prevent aliasing.
  
          # Convert the values in the na_values dictionary
          # into array-likes for further use. This is also
          # where we append the default NaN values, provided
          # that `keep_default_na=True`.
          for k, v in old_na_values.items():
              if not is_list_like(v):
                  v = [v]
  
              if keep_default_na:
                  v = set(v) | STR_NA_VALUES
  
              na_values[k] = v
          # pandas\io\parsers.py:3404: error: Incompatible types in assignment
          # (expression has type "Dict[Any, Any]", variable has type "Set[Any]")
          # [assignment]
          na_fvalues = {  # type: ignore[assignment]
              k: _floatify_na_values(v) for k, v in na_values.items()
          }
      else:
          if not is_list_like(na_values):
              na_values = [na_values]
          na_values = _stringify_na_values(na_values)
          if keep_default_na:
              na_values = na_values | STR_NA_VALUES
  
          na_fvalues = _floatify_na_values(na_values)
  
      return na_values, na_fvalues
  
  
  def _clean_index_names(columns, index_col, unnamed_cols):
      if not _is_index_col(index_col):
          return None, columns, index_col
  
      columns = list(columns)
  
      # In case of no rows and multiindex columns we have to set index_names to
      # list of Nones GH#38292
      if not columns:
          return [None] * len(index_col), columns, index_col
  
      cp_cols = list(columns)
      index_names = []
  
      # don't mutate
      index_col = list(index_col)
  
      for i, c in enumerate(index_col):
          if isinstance(c, str):
              index_names.append(c)
              for j, name in enumerate(cp_cols):
                  if name == c:
                      index_col[i] = j
                      columns.remove(name)
                      break
          else:
              name = cp_cols[c]
              columns.remove(name)
              index_names.append(name)
  
      # Only clean index names that were placeholders.
      for i, name in enumerate(index_names):
          if isinstance(name, str) and name in unnamed_cols:
              # pandas\io\parsers.py:3445: error: No overload variant of
              # "__setitem__" of "list" matches argument types "int", "None"
              # [call-overload]
              index_names[i] = None  # type: ignore[call-overload]
  
      return index_names, columns, index_col
  
  
  def _get_empty_meta(columns, index_col, index_names, dtype: Optional[DtypeArg] = None):
      columns = list(columns)
  
      # Convert `dtype` to a defaultdict of some kind.
      # This will enable us to write `dtype[col_name]`
      # without worrying about KeyError issues later on.
      if not is_dict_like(dtype):
          # if dtype == None, default will be object.
          default_dtype = dtype or object
          dtype = defaultdict(lambda: default_dtype)
      else:
          dtype = cast(dict, dtype)
          dtype = defaultdict(
              lambda: object,
              {columns[k] if is_integer(k) else k: v for k, v in dtype.items()},
          )
  
      # Even though we have no data, the "index" of the empty DataFrame
      # could for example still be an empty MultiIndex. Thus, we need to
      # check whether we have any index columns specified, via either:
      #
      # 1) index_col (column indices)
      # 2) index_names (column names)
      #
      # Both must be non-null to ensure a successful construction. Otherwise,
      # we have to create a generic empty Index.
      if (index_col is None or index_col is False) or index_names is None:
          index = Index([])
      else:
          data = [Series([], dtype=dtype[name]) for name in index_names]
          index = ensure_index_from_sequences(data, names=index_names)
          index_col.sort()
  
          for i, n in enumerate(index_col):
              columns.pop(n - i)
  
      col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns}
  
      return index, columns, col_dict
  
  
  def _floatify_na_values(na_values):
      # create float versions of the na_values
      result = set()
      for v in na_values:
          try:
              v = float(v)
              if not np.isnan(v):
                  result.add(v)
          except (TypeError, ValueError, OverflowError):
              pass
      return result
  
  
  def _stringify_na_values(na_values):
      """ return a stringified and numeric for these values """
      result = []
      for x in na_values:
          result.append(str(x))
          result.append(x)
          try:
              v = float(x)
  
              # we are like 999 here
              if v == int(v):
                  v = int(v)
                  result.append(f"{v}.0")
                  result.append(str(v))
  
              # pandas\io\parsers.py:3522: error: Argument 1 to "append" of
              # "list" has incompatible type "float"; expected "str"  [arg-type]
              result.append(v)  # type: ignore[arg-type]
          except (TypeError, ValueError, OverflowError):
              pass
          try:
              # pandas\io\parsers.py:3526: error: Argument 1 to "append" of
              # "list" has incompatible type "int"; expected "str"  [arg-type]
              result.append(int(x))  # type: ignore[arg-type]
          except (TypeError, ValueError, OverflowError):
              pass
      return set(result)
  
  
  def _get_na_values(col, na_values, na_fvalues, keep_default_na):
      """
      Get the NaN values for a given column.
  
      Parameters
      ----------
      col : str
          The name of the column.
      na_values : array-like, dict
          The object listing the NaN values as strings.
      na_fvalues : array-like, dict
          The object listing the NaN values as floats.
      keep_default_na : bool
          If `na_values` is a dict, and the column is not mapped in the
          dictionary, whether to return the default NaN values or the empty set.
  
      Returns
      -------
      nan_tuple : A length-two tuple composed of
  
          1) na_values : the string NaN values for that column.
          2) na_fvalues : the float NaN values for that column.
      """
      if isinstance(na_values, dict):
          if col in na_values:
              return na_values[col], na_fvalues[col]
          else:
              if keep_default_na:
                  return STR_NA_VALUES, set()
  
              return set(), set()
      else:
          return na_values, na_fvalues
  
  
  def _get_col_names(colspec, columns):
      colset = set(columns)
      colnames = []
      for c in colspec:
          if c in colset:
              colnames.append(c)
          elif isinstance(c, int):
              colnames.append(columns[c])
      return colnames
  
  
  class FixedWidthReader(abc.Iterator):
      """
      A reader of fixed-width lines.
      """
  
      def __init__(self, f, colspecs, delimiter, comment, skiprows=None, infer_nrows=100):
          self.f = f
          self.buffer = None
          self.delimiter = "\r\n" + delimiter if delimiter else "\n\r\t "
          self.comment = comment
          if colspecs == "infer":
              self.colspecs = self.detect_colspecs(
                  infer_nrows=infer_nrows, skiprows=skiprows
              )
          else:
              self.colspecs = colspecs
  
          if not isinstance(self.colspecs, (tuple, list)):
              raise TypeError(
                  "column specifications must be a list or tuple, "
                  f"input was a {type(colspecs).__name__}"
              )
  
          for colspec in self.colspecs:
              if not (
                  isinstance(colspec, (tuple, list))
                  and len(colspec) == 2
                  and isinstance(colspec[0], (int, np.integer, type(None)))
                  and isinstance(colspec[1], (int, np.integer, type(None)))
              ):
                  raise TypeError(
                      "Each column specification must be "
                      "2 element tuple or list of integers"
                  )
  
      def get_rows(self, infer_nrows, skiprows=None):
          """
          Read rows from self.f, skipping as specified.
  
          We distinguish buffer_rows (the first <= infer_nrows
          lines) from the rows returned to detect_colspecs
          because it's simpler to leave the other locations
          with skiprows logic alone than to modify them to
          deal with the fact we skipped some rows here as
          well.
  
          Parameters
          ----------
          infer_nrows : int
              Number of rows to read from self.f, not counting
              rows that are skipped.
          skiprows: set, optional
              Indices of rows to skip.
  
          Returns
          -------
          detect_rows : list of str
              A list containing the rows to read.
  
          """
          if skiprows is None:
              skiprows = set()
          buffer_rows = []
          detect_rows = []
          for i, row in enumerate(self.f):
              if i not in skiprows:
                  detect_rows.append(row)
              buffer_rows.append(row)
              if len(detect_rows) >= infer_nrows:
                  break
          self.buffer = iter(buffer_rows)
          return detect_rows
  
      def detect_colspecs(self, infer_nrows=100, skiprows=None):
          # Regex escape the delimiters
          delimiters = "".join(fr"\{x}" for x in self.delimiter)
          pattern = re.compile(f"([^{delimiters}]+)")
          rows = self.get_rows(infer_nrows, skiprows)
          if not rows:
              raise EmptyDataError("No rows from which to infer column width")
          max_len = max(map(len, rows))
          mask = np.zeros(max_len + 1, dtype=int)
          if self.comment is not None:
              rows = [row.partition(self.comment)[0] for row in rows]
          for row in rows:
              for m in pattern.finditer(row):
                  mask[m.start() : m.end()] = 1
          shifted = np.roll(mask, 1)
          shifted[0] = 0
          edges = np.where((mask ^ shifted) == 1)[0]
          edge_pairs = list(zip(edges[::2], edges[1::2]))
          return edge_pairs
  
      def __next__(self):
          if self.buffer is not None:
              try:
                  line = next(self.buffer)
              except StopIteration:
                  self.buffer = None
                  line = next(self.f)
          else:
              line = next(self.f)
          # Note: 'colspecs' is a sequence of half-open intervals.
          return [line[fromm:to].strip(self.delimiter) for (fromm, to) in self.colspecs]
  
  
  class FixedWidthFieldParser(PythonParser):
      """
      Specialization that Converts fixed-width fields into DataFrames.
      See PythonParser for details.
      """
  
      def __init__(self, f, **kwds):
          # Support iterators, convert to a list.
          self.colspecs = kwds.pop("colspecs")
          self.infer_nrows = kwds.pop("infer_nrows")
          PythonParser.__init__(self, f, **kwds)
  
      def _make_reader(self, f):
          self.data = FixedWidthReader(
              f,
              self.colspecs,
              self.delimiter,
              self.comment,
              self.skiprows,
              self.infer_nrows,
          )
  
      def _remove_empty_lines(self, lines) -> List:
          """
          Returns the list of lines without the empty ones. With fixed-width
          fields, empty lines become arrays of empty strings.
  
          See PythonParser._remove_empty_lines.
          """
          return [
              line
              for line in lines
              if any(not isinstance(e, str) or e.strip() for e in line)
          ]
  
  
  def _refine_defaults_read(
      dialect: Union[str, csv.Dialect],
      delimiter: Union[str, object],
      delim_whitespace: bool,
      engine: str,
      sep: Union[str, object],
      defaults: Dict[str, Any],
  ):
      """Validate/refine default values of input parameters of read_csv, read_table.
  
      Parameters
      ----------
      dialect : str or csv.Dialect
          If provided, this parameter will override values (default or not) for the
          following parameters: `delimiter`, `doublequote`, `escapechar`,
          `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
          override values, a ParserWarning will be issued. See csv.Dialect
          documentation for more details.
      delimiter : str or object
          Alias for sep.
      delim_whitespace : bool
          Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
          used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
          is set to True, nothing should be passed in for the ``delimiter``
          parameter.
      engine : {{'c', 'python'}}
          Parser engine to use. The C engine is faster while the python engine is
          currently more feature-complete.
      sep : str or object
          A delimiter provided by the user (str) or a sentinel value, i.e.
          pandas._libs.lib.no_default.
      defaults: dict
          Default values of input parameters.
  
      Returns
      -------
      kwds : dict
          Input parameters with correct values.
  
      Raises
      ------
      ValueError : If a delimiter was specified with ``sep`` (or ``delimiter``) and
          ``delim_whitespace=True``.
      """
      # fix types for sep, delimiter to Union(str, Any)
      delim_default = defaults["delimiter"]
      kwds: Dict[str, Any] = {}
      # gh-23761
      #
      # When a dialect is passed, it overrides any of the overlapping
      # parameters passed in directly. We don't want to warn if the
      # default parameters were passed in (since it probably means
      # that the user didn't pass them in explicitly in the first place).
      #
      # "delimiter" is the annoying corner case because we alias it to
      # "sep" before doing comparison to the dialect values later on.
      # Thus, we need a flag to indicate that we need to "override"
      # the comparison to dialect values by checking if default values
      # for BOTH "delimiter" and "sep" were provided.
      if dialect is not None:
          kwds["sep_override"] = delimiter is None and (
              sep is lib.no_default or sep == delim_default
          )
  
      # Alias sep -> delimiter.
      if delimiter is None:
          delimiter = sep
  
      if delim_whitespace and (delimiter is not lib.no_default):
          raise ValueError(
              "Specified a delimiter with both sep and "
              "delim_whitespace=True; you can only specify one."
          )
  
      if delimiter is lib.no_default:
          # assign default separator value
          kwds["delimiter"] = delim_default
      else:
          kwds["delimiter"] = delimiter
  
      if engine is not None:
          kwds["engine_specified"] = True
      else:
          kwds["engine"] = "c"
          kwds["engine_specified"] = False
  
      return kwds
  
  
  def _extract_dialect(kwds: Dict[str, Any]) -> Optional[csv.Dialect]:
      """
      Extract concrete csv dialect instance.
  
      Returns
      -------
      csv.Dialect or None
      """
      if kwds.get("dialect") is None:
          return None
  
      dialect = kwds["dialect"]
      if dialect in csv.list_dialects():
          dialect = csv.get_dialect(dialect)
  
      _validate_dialect(dialect)
  
      return dialect
  
  
  MANDATORY_DIALECT_ATTRS = (
      "delimiter",
      "doublequote",
      "escapechar",
      "skipinitialspace",
      "quotechar",
      "quoting",
  )
  
  
  def _validate_dialect(dialect: csv.Dialect) -> None:
      """
      Validate csv dialect instance.
  
      Raises
      ------
      ValueError
          If incorrect dialect is provided.
      """
      for param in MANDATORY_DIALECT_ATTRS:
          if not hasattr(dialect, param):
              raise ValueError(f"Invalid dialect {dialect} provided")
  
  
  def _merge_with_dialect_properties(
      dialect: csv.Dialect,
      defaults: Dict[str, Any],
  ) -> Dict[str, Any]:
      """
      Merge default kwargs in TextFileReader with dialect parameters.
  
      Parameters
      ----------
      dialect : csv.Dialect
          Concrete csv dialect. See csv.Dialect documentation for more details.
      defaults : dict
          Keyword arguments passed to TextFileReader.
  
      Returns
      -------
      kwds : dict
          Updated keyword arguments, merged with dialect parameters.
      """
      kwds = defaults.copy()
  
      for param in MANDATORY_DIALECT_ATTRS:
          dialect_val = getattr(dialect, param)
  
          parser_default = _parser_defaults[param]
          provided = kwds.get(param, parser_default)
  
          # Messages for conflicting values between the dialect
          # instance and the actual parameters provided.
          conflict_msgs = []
  
          # Don't warn if the default parameter was passed in,
          # even if it conflicts with the dialect (gh-23761).
          if provided != parser_default and provided != dialect_val:
              msg = (
                  f"Conflicting values for '{param}': '{provided}' was "
                  f"provided, but the dialect specifies '{dialect_val}'. "
                  "Using the dialect-specified value."
              )
  
              # Annoying corner case for not warning about
              # conflicts between dialect and delimiter parameter.
              # Refer to the outer "_read_" function for more info.
              if not (param == "delimiter" and kwds.pop("sep_override", False)):
                  conflict_msgs.append(msg)
  
          if conflict_msgs:
              warnings.warn("\n\n".join(conflict_msgs), ParserWarning, stacklevel=2)
          kwds[param] = dialect_val
      return kwds
  
  
  def _validate_skipfooter(kwds: Dict[str, Any]) -> None:
      """
      Check whether skipfooter is compatible with other kwargs in TextFileReader.
  
      Parameters
      ----------
      kwds : dict
          Keyword arguments passed to TextFileReader.
  
      Raises
      ------
      ValueError
          If skipfooter is not compatible with other parameters.
      """
      if kwds.get("skipfooter"):
          if kwds.get("iterator") or kwds.get("chunksize"):
              raise ValueError("'skipfooter' not supported for iteration")
          if kwds.get("nrows"):
              raise ValueError("'skipfooter' not supported with 'nrows'")
  