text: """
  Module contains tools for processing files into DataFrames or other objects
  """
  from __future__ import print_function
  from collections import defaultdict
  import re
  import csv
  import sys
  import warnings
  import datetime
  
  import numpy as np
  
  from pandas import compat
  from pandas.compat import (range, lrange, StringIO, lzip,
                             zip, string_types, map, u)
  from pandas.types.common import (is_integer, _ensure_object,
                                   is_list_like, is_integer_dtype,
                                   is_float,
                                   is_scalar)
  from pandas.core.index import Index, MultiIndex, RangeIndex
  from pandas.core.frame import DataFrame
  from pandas.core.common import AbstractMethodError
  from pandas.core.config import get_option
  from pandas.io.date_converters import generic_parser
  from pandas.io.common import (get_filepath_or_buffer, _validate_header_arg,
                                _get_handle, UnicodeReader, UTF8Recoder,
                                BaseIterator, CParserError, EmptyDataError,
                                ParserWarning, _NA_VALUES)
  from pandas.tseries import tools
  
  from pandas.util.decorators import Appender
  
  import pandas.lib as lib
  import pandas.parser as _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 = u('\ufeff')
  
  _parser_params = """Also supports optionally iterating or breaking of the file
  into chunks.
  
  Additional help can be found in the `online docs for IO Tools
  <http://pandas.pydata.org/pandas-docs/stable/io.html>`_.
  
  Parameters
  ----------
  filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any \
  object with a read() method (such as a file handle or StringIO)
      The string could be a URL. Valid URL schemes include http, ftp, s3, and
      file. For file URLs, a host is expected. For instance, a local file could
      be file ://localhost/path/to/table.csv
  %s
  delimiter : str, default ``None``
      Alternative argument name for sep.
  delim_whitespace : boolean, 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.
  
      .. versionadded:: 0.18.1 support for the Python parser.
  
  header : int or list of ints, default 'infer'
      Row number(s) to use as the column names, and the start of the data.
      Default behavior is as if set to 0 if no ``names`` passed, otherwise
      ``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, default None
      List of column names to use. If file contains no header row, then you
      should explicitly pass header=None. Duplicates in this list are not
      allowed unless mangle_dupe_cols=True, which is the default.
  index_col : int or sequence or False, default None
      Column to use as the row labels of the DataFrame. If a sequence is given, a
      MultiIndex is used. If you have a malformed file with delimiters at the end
      of each line, you might consider index_col=False to force pandas to _not_
      use the first column as the index (row names)
  usecols : array-like, default None
      Return a subset of the columns. All elements in this array 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 `usecols`
      parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Using this parameter
      results in much faster parsing time and lower memory usage.
  as_recarray : boolean, default False
      DEPRECATED: this argument will be removed in a future version. Please call
      `pd.read_csv(...).to_records()` instead.
  
      Return a NumPy recarray instead of a DataFrame after parsing the data.
      If set to True, this option takes precedence over the `squeeze` parameter.
      In addition, as row indices are not available in such a format, the
      `index_col` parameter will be ignored.
  squeeze : boolean, default False
      If the parsed data only contains one column then return a Series
  prefix : str, default None
      Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
  mangle_dupe_cols : boolean, default True
      Duplicate columns will be specified as 'X.0'...'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, default None
      Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
      (Unsupported with engine='python'). Use `str` or `object` to preserve and
      not interpret dtype.
  %s
  converters : dict, default None
      Dict of functions for converting values in certain columns. Keys can either
      be integers or column labels
  true_values : list, default None
      Values to consider as True
  false_values : list, default None
      Values to consider as False
  skipinitialspace : boolean, default False
      Skip spaces after delimiter.
  skiprows : list-like or integer, default None
      Line numbers to skip (0-indexed) or number of lines to skip (int)
      at the start of the file
  skipfooter : int, default 0
      Number of lines at bottom of file to skip (Unsupported with engine='c')
  skip_footer : int, default 0
      DEPRECATED: use the `skipfooter` parameter instead, as they are identical
  nrows : int, default None
      Number of rows of file to read. Useful for reading pieces of large files
  na_values : scalar, str, list-like, or dict, default None
      Additional strings to recognize as NA/NaN. If dict passed, specific
      per-column NA values.  By default the following values are interpreted as
      NaN: `'""" + "'`, `'".join(sorted(_NA_VALUES)) + """'`.
  keep_default_na : bool, default True
      If na_values are specified and keep_default_na is False the default NaN
      values are overridden, otherwise they're appended to.
  na_filter : boolean, 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 : boolean, default False
      Indicate number of NA values placed in non-numeric columns
  skip_blank_lines : boolean, default True
      If True, skip over blank lines rather than interpreting as NaN values
  parse_dates : boolean or list of ints or names or list of lists or dict, \
  default False
  
      * boolean. If True -> try parsing the index.
      * list of ints 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'
  
      Note: A fast-path exists for iso8601-formatted dates.
  infer_datetime_format : boolean, 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 : boolean, default False
      If True and parse_dates specifies combining multiple columns then
      keep the original columns.
  date_parser : function, default None
      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 : boolean, default False
      DD/MM format dates, international and European format
  iterator : boolean, default False
      Return TextFileReader object for iteration or getting chunks with
      ``get_chunk()``.
  chunksize : int, default None
      Return TextFileReader object for iteration. `See IO Tools docs for more
      information
      <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ on
      ``iterator`` and ``chunksize``.
  compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
      For on-the-fly decompression of on-disk data. If 'infer', then use gzip,
      bz2, zip or xz if filepath_or_buffer is a string ending in '.gz', '.bz2',
      '.zip', or 'xz', respectively, and no decompression otherwise. If using
      'zip', the ZIP file must contain only one data file to be read in.
      Set to None for no decompression.
  
      .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.
  
  thousands : str, default None
      Thousands separator
  decimal : str, default '.'
      Character to recognize as decimal point (e.g. use ',' for European data).
  float_precision : string, default None
      Specifies which converter the C engine should use for floating-point
      values. The options are `None` for the ordinary converter,
      `high` for the high-precision converter, and `round_trip` for the
      round-trip converter.
  lineterminator : str (length 1), default None
      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 : boolean, 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), default None
      One-character string used to escape delimiter when quoting is QUOTE_NONE.
  comment : str, default None
      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, default None
      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 instance, default None
      If None defaults to Excel dialect. Ignored if sep longer than 1 char
      See csv.Dialect documentation for more details
  tupleize_cols : boolean, default False
      Leave a list of tuples on columns as is (default is to convert to
      a Multi Index on the columns)
  error_bad_lines : boolean, 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. (Only valid with C parser)
  warn_bad_lines : boolean, default True
      If error_bad_lines is False, and warn_bad_lines is True, a warning for each
      "bad line" will be output. (Only valid with C parser).
  low_memory : boolean, 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)
  buffer_lines : int, default None
      DEPRECATED: this argument will be removed in a future version because its
      value is not respected by the parser
  compact_ints : boolean, default False
      DEPRECATED: this argument will be removed in a future version
  
      If compact_ints is True, then for any column that is of integer dtype,
      the parser will attempt to cast it as the smallest integer dtype possible,
      either signed or unsigned depending on the specification from the
      `use_unsigned` parameter.
  use_unsigned : boolean, default False
      DEPRECATED: this argument will be removed in a future version
  
      If integer columns are being compacted (i.e. `compact_ints=True`), specify
      whether the column should be compacted to the smallest signed or unsigned
      integer dtype.
  memory_map : boolean, 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.
  
  Returns
  -------
  result : DataFrame or TextParser
  """
  
  # engine is not used in read_fwf() so is factored out of the shared docstring
  _engine_doc = """engine : {'c', 'python'}, optional
      Parser engine to use. The C engine is faster while the python engine is
      currently more feature-complete."""
  
  _sep_doc = r"""sep : str, default {default}
      Delimiter to use. If sep is None, will try to automatically determine
      this. Separators longer than 1 character and different from ``'\s+'`` will
      be interpreted as regular expressions, will force use of the python parsing
      engine and will ignore quotes in the data. Regex example: ``'\r\t'``"""
  
  _read_csv_doc = """
  Read CSV (comma-separated) file into DataFrame
  
  %s
  """ % (_parser_params % (_sep_doc.format(default="','"), _engine_doc))
  
  _read_table_doc = """
  Read general delimited file into DataFrame
  
  %s
  """ % (_parser_params % (_sep_doc.format(default="\\t (tab-stop)"),
                           _engine_doc))
  
  _fwf_widths = """\
  colspecs : list of pairs (int, int) or 'infer'. optional
      A list of pairs (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 (default='infer').
  widths : list of ints. optional
      A list of field widths which can be used instead of 'colspecs' if
      the intervals are contiguous.
  """
  
  _read_fwf_doc = """
  Read a table of fixed-width formatted lines into DataFrame
  
  %s
  
  Also, 'delimiter' is used to specify the filler character of the
  fields if it is not spaces (e.g., '~').
  """ % (_parser_params % (_fwf_widths, ''))
  
  
  def _validate_nrows(nrows):
      """
      Checks whether the 'nrows' 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.
      """
      msg = "'nrows' must be an integer"
  
      if nrows is not None:
          if is_float(nrows):
              if int(nrows) != nrows:
                  raise ValueError(msg)
              nrows = int(nrows)
          elif not is_integer(nrows):
              raise ValueError(msg)
  
      return nrows
  
  
  def _read(filepath_or_buffer, kwds):
      "Generic reader of line files."
      encoding = kwds.get('encoding', None)
      if encoding is not None:
          encoding = re.sub('_', '-', encoding).lower()
          kwds['encoding'] = encoding
  
      # If the input could be a filename, check for a recognizable compression
      # extension.  If we're reading from a URL, the `get_filepath_or_buffer`
      # will use header info to determine compression, so use what it finds in
      # that case.
      inferred_compression = kwds.get('compression')
      if inferred_compression == 'infer':
          if isinstance(filepath_or_buffer, compat.string_types):
              if filepath_or_buffer.endswith('.gz'):
                  inferred_compression = 'gzip'
              elif filepath_or_buffer.endswith('.bz2'):
                  inferred_compression = 'bz2'
              elif filepath_or_buffer.endswith('.zip'):
                  inferred_compression = 'zip'
              elif filepath_or_buffer.endswith('.xz'):
                  inferred_compression = 'xz'
              else:
                  inferred_compression = None
          else:
              inferred_compression = None
  
      filepath_or_buffer, _, compression = get_filepath_or_buffer(
          filepath_or_buffer, encoding,
          compression=kwds.get('compression', None))
      kwds['compression'] = (inferred_compression if compression == 'infer'
                             else compression)
  
      if kwds.get('date_parser', None) is not None:
          if isinstance(kwds['parse_dates'], bool):
              kwds['parse_dates'] = True
  
      # Extract some of the arguments (pass chunksize on).
      iterator = kwds.get('iterator', False)
      chunksize = kwds.get('chunksize', None)
      nrows = _validate_nrows(kwds.pop('nrows', None))
  
      # Create the parser.
      parser = TextFileReader(filepath_or_buffer, **kwds)
  
      if (nrows is not None) and (chunksize is not None):
          raise NotImplementedError("'nrows' and 'chunksize' cannot be used"
                                    " together yet.")
      elif nrows is not None:
          data = parser.read(nrows)
          parser.close()
          return data
      elif chunksize or iterator:
          return parser
  
      data = parser.read()
      parser.close()
      return data
  
  _parser_defaults = {
      'delimiter': None,
  
      'doublequote': True,
      'escapechar': None,
      'quotechar': '"',
      'quoting': csv.QUOTE_MINIMAL,
      'skipinitialspace': False,
      'lineterminator': None,
  
      'header': 'infer',
      'index_col': None,
      'names': None,
      'prefix': None,
      'skiprows': None,
      'na_values': None,
      'true_values': None,
      'false_values': None,
      'converters': None,
      'skipfooter': 0,
  
      'keep_default_na': True,
      'thousands': None,
      'comment': None,
      'decimal': b'.',
  
      # 'engine': 'c',
      'parse_dates': False,
      'keep_date_col': False,
      'dayfirst': False,
      'date_parser': None,
  
      'usecols': None,
  
      # 'nrows': None,
      # 'iterator': False,
      'chunksize': None,
      'verbose': False,
      'encoding': None,
      'squeeze': False,
      'compression': None,
      'mangle_dupe_cols': True,
      'tupleize_cols': False,
      'infer_datetime_format': False,
      'skip_blank_lines': True
  }
  
  
  _c_parser_defaults = {
      'delim_whitespace': False,
      'as_recarray': False,
      'na_filter': True,
      'compact_ints': False,
      'use_unsigned': False,
      'low_memory': True,
      'memory_map': False,
      'buffer_lines': None,
      'error_bad_lines': True,
      'warn_bad_lines': True,
      'dtype': None,
      'float_precision': None
  }
  
  _fwf_defaults = {
      'colspecs': 'infer',
      'widths': None,
  }
  
  _c_unsupported = set(['skipfooter'])
  _python_unsupported = set([
      'low_memory',
      'buffer_lines',
      'error_bad_lines',
      'warn_bad_lines',
      'dtype',
      'float_precision',
  ])
  _deprecated_args = set([
      'as_recarray',
      'buffer_lines',
      'compact_ints',
      'use_unsigned',
  ])
  
  
  def _make_parser_function(name, sep=','):
  
      default_sep = sep
  
      def parser_f(filepath_or_buffer,
                   sep=sep,
                   delimiter=None,
  
                   # Column and Index Locations and Names
                   header='infer',
                   names=None,
                   index_col=None,
                   usecols=None,
                   squeeze=False,
                   prefix=None,
                   mangle_dupe_cols=True,
  
                   # General Parsing Configuration
                   dtype=None,
                   engine=None,
                   converters=None,
                   true_values=None,
                   false_values=None,
                   skipinitialspace=False,
                   skiprows=None,
                   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,
  
                   # Iteration
                   iterator=False,
                   chunksize=None,
  
                   # Quoting, Compression, and File Format
                   compression='infer',
                   thousands=None,
                   decimal=b'.',
                   lineterminator=None,
                   quotechar='"',
                   quoting=csv.QUOTE_MINIMAL,
                   escapechar=None,
                   comment=None,
                   encoding=None,
                   dialect=None,
                   tupleize_cols=False,
  
                   # Error Handling
                   error_bad_lines=True,
                   warn_bad_lines=True,
  
                   skipfooter=0,
                   skip_footer=0,  # deprecated
  
                   # Internal
                   doublequote=True,
                   delim_whitespace=False,
                   as_recarray=False,
                   compact_ints=False,
                   use_unsigned=False,
                   low_memory=_c_parser_defaults['low_memory'],
                   buffer_lines=None,
                   memory_map=False,
                   float_precision=None):
  
          # Alias sep -> delimiter.
          if delimiter is None:
              delimiter = sep
  
          if delim_whitespace and delimiter is not default_sep:
              raise ValueError("Specified a delimiter with both sep and"
                               " delim_whitespace=True; you can only"
                               " specify one.")
  
          if engine is not None:
              engine_specified = True
          else:
              engine = 'c'
              engine_specified = False
  
          if skip_footer != 0:
              warnings.warn("The 'skip_footer' argument has "
                            "been deprecated and will be removed "
                            "in a future version. Please use the "
                            "'skipfooter' argument instead.",
                            FutureWarning, stacklevel=2)
  
          kwds = dict(delimiter=delimiter,
                      engine=engine,
                      dialect=dialect,
                      compression=compression,
                      engine_specified=engine_specified,
  
                      doublequote=doublequote,
                      escapechar=escapechar,
                      quotechar=quotechar,
                      quoting=quoting,
                      skipinitialspace=skipinitialspace,
                      lineterminator=lineterminator,
  
                      header=header,
                      index_col=index_col,
                      names=names,
                      prefix=prefix,
                      skiprows=skiprows,
                      na_values=na_values,
                      true_values=true_values,
                      false_values=false_values,
                      keep_default_na=keep_default_na,
                      thousands=thousands,
                      comment=comment,
                      decimal=decimal,
  
                      parse_dates=parse_dates,
                      keep_date_col=keep_date_col,
                      dayfirst=dayfirst,
                      date_parser=date_parser,
  
                      nrows=nrows,
                      iterator=iterator,
                      chunksize=chunksize,
                      skipfooter=skipfooter or skip_footer,
                      converters=converters,
                      dtype=dtype,
                      usecols=usecols,
                      verbose=verbose,
                      encoding=encoding,
                      squeeze=squeeze,
                      memory_map=memory_map,
                      float_precision=float_precision,
  
                      na_filter=na_filter,
                      compact_ints=compact_ints,
                      use_unsigned=use_unsigned,
                      delim_whitespace=delim_whitespace,
                      as_recarray=as_recarray,
                      warn_bad_lines=warn_bad_lines,
                      error_bad_lines=error_bad_lines,
                      low_memory=low_memory,
                      buffer_lines=buffer_lines,
                      mangle_dupe_cols=mangle_dupe_cols,
                      tupleize_cols=tupleize_cols,
                      infer_datetime_format=infer_datetime_format,
                      skip_blank_lines=skip_blank_lines)
  
          return _read(filepath_or_buffer, kwds)
  
      parser_f.__name__ = name
  
      return parser_f
  
  read_csv = _make_parser_function('read_csv', sep=',')
  read_csv = Appender(_read_csv_doc)(read_csv)
  
  read_table = _make_parser_function('read_table', sep='\t')
  read_table = Appender(_read_table_doc)(read_table)
  
  
  @Appender(_read_fwf_doc)
  def read_fwf(filepath_or_buffer, colspecs='infer', widths=None, **kwds):
      # 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['engine'] = 'python-fwf'
      return _read(filepath_or_buffer, kwds)
  
  
  class TextFileReader(BaseIterator):
      """
  
      Passed dialect overrides any of the related parser options
  
      """
  
      def __init__(self, f, engine=None, **kwds):
  
          self.f = f
  
          if engine is not None:
              engine_specified = True
          else:
              engine = 'python'
              engine_specified = False
  
          self._engine_specified = kwds.get('engine_specified', engine_specified)
  
          if kwds.get('dialect') is not None:
              dialect = kwds['dialect']
              if dialect in csv.list_dialects():
                  dialect = csv.get_dialect(dialect)
              kwds['delimiter'] = dialect.delimiter
              kwds['doublequote'] = dialect.doublequote
              kwds['escapechar'] = dialect.escapechar
              kwds['skipinitialspace'] = dialect.skipinitialspace
              kwds['quotechar'] = dialect.quotechar
              kwds['quoting'] = dialect.quoting
  
          if kwds.get('header', 'infer') == 'infer':
              kwds['header'] = 0 if kwds.get('names') is None else None
  
          self.orig_options = kwds
  
          # miscellanea
          self.engine = engine
          self._engine = None
          self._currow = 0
  
          options = self._get_options_with_defaults(engine)
  
          self.chunksize = options.pop('chunksize', None)
          self.squeeze = options.pop('squeeze', False)
  
          # might mutate self.engine
          self.options, self.engine = self._clean_options(options, engine)
          if 'has_index_names' in kwds:
              self.options['has_index_names'] = kwds['has_index_names']
  
          self._make_engine(self.engine)
  
      def close(self):
          self._engine.close()
  
      def _get_options_with_defaults(self, engine):
          kwds = self.orig_options
  
          options = {}
  
          for argname, default in compat.iteritems(_parser_defaults):
              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 compat.iteritems(_c_parser_defaults):
              if argname in kwds:
                  value = kwds[argname]
  
                  if engine != 'c' and value != default:
                      if ('python' in engine and
                              argname not in _python_unsupported):
                          pass
                      else:
                          raise ValueError(
                              'The %r option is not supported with the'
                              ' %r engine' % (argname, engine))
              else:
                  value = default
              options[argname] = value
  
          if engine == 'python-fwf':
              for argname, default in compat.iteritems(_fwf_defaults):
                  options[argname] = kwds.get(argname, default)
  
          return options
  
      def _clean_options(self, options, engine):
          result = options.copy()
  
          engine_specified = self._engine_specified
          fallback_reason = None
  
          sep = options['delimiter']
          delim_whitespace = options['delim_whitespace']
  
          # C engine not supported yet
          if engine == 'c':
              if options['skipfooter'] > 0:
                  fallback_reason = "the 'c' engine does not support"\
                                    " skipfooter"
                  engine = 'python'
  
          encoding = sys.getfilesystemencoding() or 'utf-8'
          if sep is None and not delim_whitespace:
              if engine == 'c':
                  fallback_reason = "the 'c' engine does not support"\
                                    " sep=None with delim_whitespace=False"
                  engine = 'python'
          elif sep is not None and len(sep) > 1:
              if engine == 'c' and sep == '\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"\
                                    " different from '\s+' are"\
                                    " interpreted as regex)"
                  engine = 'python'
  
          elif len(sep.encode(encoding)) > 1:
              if engine not in ('python', 'python-fwf'):
                  fallback_reason = "the separator encoded in {encoding}"\
                                    " is > 1 char long, and the 'c' engine"\
                                    " does not support such separators".format(
                                        encoding=encoding)
                  engine = 'python'
          elif delim_whitespace:
              if 'python' in engine:
                  result['delimiter'] = '\s+'
  
          if fallback_reason and engine_specified:
              raise ValueError(fallback_reason)
  
          if engine == 'c':
              for arg in _c_unsupported:
                  del result[arg]
  
          if 'python' in engine:
              for arg in _python_unsupported:
                  if fallback_reason and result[arg] != _c_parser_defaults[arg]:
                      msg = ("Falling back to the 'python' engine because"
                             " {reason}, but this causes {option!r} to be"
                             " ignored as it is not supported by the 'python'"
                             " engine.").format(reason=fallback_reason,
                                                option=arg)
                      if arg == 'dtype':
                          msg += " (Note the 'converters' option provides"\
                                 " similar functionality.)"
                      raise ValueError(msg)
                  del result[arg]
  
          if fallback_reason:
              warnings.warn(("Falling back to the 'python' engine because"
                             " {0}; you can avoid this warning by specifying"
                             " engine='python'.").format(fallback_reason),
                            ParserWarning, stacklevel=5)
  
          index_col = options['index_col']
          names = options['names']
          converters = options['converters']
          na_values = options['na_values']
          skiprows = options['skiprows']
  
          # really delete this one
          keep_default_na = result.pop('keep_default_na')
  
          _validate_header_arg(options['header'])
  
          depr_warning = ''
  
          for arg in _deprecated_args:
              parser_default = _c_parser_defaults[arg]
              msg = ("The '{arg}' argument has been deprecated "
                     "and will be removed in a future version."
                     .format(arg=arg))
  
              if arg == 'as_recarray':
                  msg += ' Please call pd.to_csv(...).to_records() instead.'
  
              if result.get(arg, parser_default) != parser_default:
                  depr_warning += msg + '\n\n'
  
          if depr_warning != '':
              warnings.warn(depr_warning, FutureWarning, stacklevel=2)
  
          if index_col is True:
              raise ValueError("The value of index_col couldn't be 'True'")
          if _is_index_col(index_col):
              if not isinstance(index_col, (list, tuple, np.ndarray)):
                  index_col = [index_col]
          result['index_col'] = index_col
  
          names = list(names) if names is not None else names
  
          # type conversion-related
          if converters is not None:
              if not isinstance(converters, dict):
                  raise TypeError('Type converters must be a dict or'
                                  ' subclass, input was '
                                  'a {0!r}'.format(type(converters).__name__))
          else:
              converters = {}
  
          # Converting values to 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 = lrange(skiprows)
              skiprows = set() if skiprows is None else set(skiprows)
  
          # put stuff back
          result['names'] = names
          result['converters'] = converters
          result['na_values'] = na_values
          result['na_fvalues'] = na_fvalues
          result['skiprows'] = skiprows
  
          return result, engine
  
      def __next__(self):
          try:
              return self.get_chunk()
          except StopIteration:
              self.close()
              raise
  
      def _make_engine(self, engine='c'):
          if engine == 'c':
              self._engine = CParserWrapper(self.f, **self.options)
          else:
              if engine == 'python':
                  klass = PythonParser
              elif engine == 'python-fwf':
                  klass = FixedWidthFieldParser
              self._engine = klass(self.f, **self.options)
  
      def _failover_to_python(self):
          raise AbstractMethodError(self)
  
      def read(self, nrows=None):
          if nrows is not None:
              if self.options.get('skipfooter'):
                  raise ValueError('skipfooter not supported for iteration')
  
          ret = self._engine.read(nrows)
  
          if self.options.get('as_recarray'):
              return ret
  
          # May alter columns / col_dict
          index, columns, col_dict = self._create_index(ret)
  
          if index is None:
              if col_dict:
                  # Any column is actually fine:
                  new_rows = len(compat.next(compat.itervalues(col_dict)))
                  index = RangeIndex(self._currow, self._currow + new_rows)
              else:
                  new_rows = 0
          else:
              new_rows = len(index)
  
          df = DataFrame(col_dict, columns=columns, index=index)
  
          self._currow += new_rows
  
          if self.squeeze and len(df.columns) == 1:
              return df[df.columns[0]].copy()
          return df
  
      def _create_index(self, ret):
          index, columns, col_dict = ret
          return index, columns, col_dict
  
      def get_chunk(self, size=None):
          if size is None:
              size = self.chunksize
          return self.read(nrows=size)
  
  
  def _is_index_col(col):
      return col is not None and col is not False
  
  
  def _validate_usecols_arg(usecols):
      """
      Check whether or not the 'usecols' parameter
      contains all integers (column selection by index)
      or strings (column by name). Raises a ValueError
      if that is not the case.
      """
      msg = ("The elements of 'usecols' must "
             "either be all strings, all unicode, or all integers")
  
      if usecols is not None:
          usecols_dtype = lib.infer_dtype(usecols)
          if usecols_dtype not in ('empty', 'integer',
                                   'string', 'unicode'):
              raise ValueError(msg)
  
          return set(usecols)
      return usecols
  
  
  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(object):
  
      def __init__(self, kwds):
          self.names = kwds.get('names')
          self.orig_names = None
          self.prefix = kwds.pop('prefix', None)
  
          self.index_col = kwds.get('index_col', None)
          self.index_names = None
          self.col_names = None
  
          self.parse_dates = _validate_parse_dates_arg(
              kwds.pop('parse_dates', False))
          self.date_parser = kwds.pop('date_parser', None)
          self.dayfirst = kwds.pop('dayfirst', False)
          self.keep_date_col = kwds.pop('keep_date_col', False)
  
          self.na_values = kwds.get('na_values')
          self.na_fvalues = kwds.get('na_fvalues')
          self.true_values = kwds.get('true_values')
          self.false_values = kwds.get('false_values')
          self.as_recarray = kwds.get('as_recarray', False)
          self.tupleize_cols = kwds.get('tupleize_cols', False)
          self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True)
          self.infer_datetime_format = kwds.pop('infer_datetime_format', False)
  
          self._date_conv = _make_date_converter(
              date_parser=self.date_parser,
              dayfirst=self.dayfirst,
              infer_datetime_format=self.infer_datetime_format
          )
  
          # validate header options for mi
          self.header = kwds.get('header')
          if isinstance(self.header, (list, tuple, np.ndarray)):
              if kwds.get('as_recarray'):
                  raise ValueError("cannot specify as_recarray when "
                                   "specifying a multi-index header")
              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")
  
          self._name_processed = False
  
          self._first_chunk = True
  
          # GH 13932
          # keep references to file handles opened by the parser itself
          self.handles = []
  
      def close(self):
          for f in self.handles:
              f.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:
              name = self.index_names[i]
              j = self.index_col[i]
  
              if is_scalar(self.parse_dates):
                  return (j == self.parse_dates) or (name == self.parse_dates)
              else:
                  return (j in self.parse_dates) or (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, names, index_col = _clean_index_names(index_names,
                                                             self.index_col)
  
          # 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 = lzip(*[extract(r) for r in header])
          names = ic + columns
  
          def tostr(x):
              return str(x) if not isinstance(x, compat.string_types) else x
  
          # if we find 'Unnamed' all of a single level, then our header was too
          # long
          for n in range(len(columns[0])):
              if all(['Unnamed' in tostr(c[n]) for c in columns]):
                  raise CParserError(
                      "Passed header=[%s] are too many rows for this "
                      "multi_index of columns"
                      % ','.join([str(x) for x in self.header])
                  )
  
          # clean the column names (if we have an index_col)
          if len(ic):
              col_names = [r[0] if len(r[0]) and 'Unnamed' not in r[0] 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
              counts = {}
  
              for i, col in enumerate(names):
                  cur_count = counts.get(col, 0)
  
                  if cur_count > 0:
                      names[i] = '%s.%d' % (col, cur_count)
  
                  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 (not self.tupleize_cols and len(columns) and
                  not isinstance(columns, MultiIndex) and
                  all([isinstance(c, tuple) for c in 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._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)
              index = index.set_names(indexnamerow[:coffset])
  
          # 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, compat.string_types):
                  return col
              raise ValueError('Index %s invalid' % col)
          index = None
  
          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 reversed(sorted(to_remove)):
              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, compat.string_types):
                  return icol
  
              if col_names is None:
                  raise ValueError(('Must supply column order to use %s as '
                                    'index') % str(icol))
  
              for i, c in enumerate(col_names):
                  if i == icol:
                      return c
  
          index = None
  
          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 reversed(sorted(to_remove)):
              data.pop(c)
              col_names.remove(c)
  
          return index
  
      def _agg_index(self, index, try_parse_dates=True):
          arrays = []
          for i, arr in enumerate(index):
  
              if (try_parse_dates and self._should_parse_dates(i)):
                  arr = self._date_conv(arr)
  
              col_na_values = self.na_values
              col_na_fvalues = self.na_fvalues
  
              if isinstance(self.na_values, dict):
                  col_name = self.index_names[i]
                  if col_name is not None:
                      col_na_values, col_na_fvalues = _get_na_values(
                          col_name, self.na_values, self.na_fvalues)
  
              arr, _ = self._convert_types(arr, col_na_values | col_na_fvalues)
              arrays.append(arr)
  
          index = MultiIndex.from_arrays(arrays, names=self.index_names)
  
          return index
  
      def _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose=False,
                               converters=None):
          result = {}
          for c, values in compat.iteritems(dct):
              conv_f = None if converters is None else converters.get(c, None)
  
              if self.na_filter:
                  col_na_values, col_na_fvalues = _get_na_values(
                      c, na_values, na_fvalues)
              else:
                  col_na_values, col_na_fvalues = set(), set()
  
              coerce_type = True
              if conv_f is not None:
                  try:
                      values = lib.map_infer(values, conv_f)
                  except ValueError:
                      mask = lib.ismember(values, na_values).view(np.uint8)
                      values = lib.map_infer_mask(values, conv_f, mask)
                  coerce_type = False
  
              cvals, na_count = self._convert_types(
                  values, set(col_na_values) | col_na_fvalues, coerce_type)
  
              if issubclass(cvals.dtype.type, np.integer) and self.compact_ints:
                  cvals = lib.downcast_int64(
                      cvals, _parser.na_values,
                      self.use_unsigned)
  
              result[c] = cvals
              if verbose and na_count:
                  print('Filled %d NA values in column %s' % (na_count, str(c)))
          return result
  
      def _convert_types(self, values, na_values, try_num_bool=True):
          na_count = 0
          if issubclass(values.dtype.type, (np.number, np.bool_)):
              mask = lib.ismember(values, 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:
              try:
                  result = lib.maybe_convert_numeric(values, na_values, False)
              except Exception:
                  result = values
                  if values.dtype == np.object_:
                      na_count = lib.sanitize_objects(result, na_values, False)
          else:
              result = values
              if values.dtype == np.object_:
                  na_count = lib.sanitize_objects(values, na_values, False)
  
          if result.dtype == np.object_ and try_num_bool:
              result = lib.maybe_convert_bool(values,
                                              true_values=self.true_values,
                                              false_values=self.false_values)
  
          return result, na_count
  
      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, **kwds):
          self.kwds = kwds
          kwds = kwds.copy()
  
          ParserBase.__init__(self, kwds)
  
          if 'utf-16' in (kwds.get('encoding') or ''):
              if isinstance(src, compat.string_types):
                  src = open(src, 'rb')
                  self.handles.append(src)
              src = UTF8Recoder(src, kwds['encoding'])
              kwds['encoding'] = 'utf-8'
  
          # #2442
          kwds['allow_leading_cols'] = self.index_col is not False
  
          self._reader = _parser.TextReader(src, **kwds)
  
          # XXX
          self.usecols = _validate_usecols_arg(self._reader.usecols)
  
          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 = ['%s%d' % (self.prefix, i)
                                for i in range(self._reader.table_width)]
              else:
                  self.names = lrange(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:
              if len(self.names) > len(self.usecols):
                  self.names = [n for i, n in enumerate(self.names)
                                if (i in self.usecols or n in self.usecols)]
  
              if len(self.names) < len(self.usecols):
                  raise ValueError("Usecols do not match 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)
  
                  if self.index_names is None:
                      self.index_names = index_names
  
              if self._reader.header is None and not passed_names:
                  self.index_names = [None] * len(self.index_names)
  
          self._implicit_index = self._reader.leading_cols > 0
  
      def close(self):
          for f in self.handles:
              f.close()
          try:
              self._reader.close()
          except:
              pass
  
      def _set_noconvert_columns(self):
          names = self.orig_names
          usecols = self.usecols
  
          def _set(x):
              if usecols and is_integer(x):
                  x = list(usecols)[x]
  
              if not is_integer(x):
                  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'))
  
                  if self.usecols is not None:
                      columns = self._filter_usecols(columns)
  
                  col_dict = dict(filter(lambda item: item[0] in columns,
                                         col_dict.items()))
  
                  return index, columns, col_dict
  
              else:
                  raise
  
          # Done with first read, next time raise StopIteration
          self._first_chunk = False
  
          if self.as_recarray:
              # what to do if there are leading columns?
              return data
  
          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 = MultiIndex.from_arrays(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 = dict((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
              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 = dict((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
          if self.usecols is not None and len(names) != len(self.usecols):
              names = [name for i, name in enumerate(names)
                       if i in self.usecols or name in self.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)
  
          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, default None
          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, default None
          Column or columns to use as the (possibly hierarchical) index
      has_index_names: boolean, 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, default None
          Additional strings to recognize as NA/NaN.
      keep_default_na : bool, default True
      thousands : str, default None
          Thousands separator
      comment : str, default None
          Comment out remainder of line
      parse_dates : boolean, default False
      keep_date_col : boolean, default False
      date_parser : function, default None
      skiprows : list of integers
          Row numbers to skip
      skipfooter : int
          Number of line at bottom of file to skip
      converters : dict, default None
          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 : string, default None
          Encoding to use for UTF when reading/writing (ex. 'utf-8')
      squeeze : boolean, default False
          returns Series if only one column
      infer_datetime_format: boolean, 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 : string, default None
          Specifies which converter the C engine should use for floating-point
          values. The options are None for the ordinary converter,
          'high' for the high-precision converter, and 'round_trip' for the
          round-trip converter.
      """
      kwds['engine'] = 'python'
      return TextFileReader(*args, **kwds)
  
  
  def count_empty_vals(vals):
      return sum([1 for v in vals if v == '' or v is None])
  
  
  def _wrap_compressed(f, compression, encoding=None):
      """wraps compressed fileobject in a decompressing fileobject
      NOTE: For all files in Python 3.2 and for bzip'd files under all Python
      versions, this means reading in the entire file and then re-wrapping it in
      StringIO.
      """
      compression = compression.lower()
      encoding = encoding or get_option('display.encoding')
  
      if compression == 'gzip':
          import gzip
  
          f = gzip.GzipFile(fileobj=f)
          if compat.PY3:
              from io import TextIOWrapper
  
              f = TextIOWrapper(f)
          return f
      elif compression == 'bz2':
          import bz2
  
          if compat.PY3:
              f = bz2.open(f, 'rt', encoding=encoding)
          else:
              # Python 2's bz2 module can't take file objects, so have to
              # run through decompress manually
              data = bz2.decompress(f.read())
              f = StringIO(data)
          return f
      elif compression == 'zip':
          import zipfile
          zip_file = zipfile.ZipFile(f)
          zip_names = zip_file.namelist()
  
          if len(zip_names) == 1:
              file_name = zip_names.pop()
              f = zip_file.open(file_name)
              return f
  
          elif len(zip_names) == 0:
              raise ValueError('Corrupted or zero files found in compressed '
                               'zip file %s', zip_file.filename)
  
          else:
              raise ValueError('Multiple files found in compressed '
                               'zip file %s', str(zip_names))
  
      elif compression == 'xz':
  
          lzma = compat.import_lzma()
          f = lzma.LZMAFile(f)
  
          if compat.PY3:
              from io import TextIOWrapper
  
              f = TextIOWrapper(f)
  
          return f
  
      else:
          raise ValueError('do not recognize compression method %s'
                           % compression)
  
  
  class PythonParser(ParserBase):
  
      def __init__(self, f, **kwds):
          """
          Workhorse function for processing nested list into DataFrame
  
          Should be replaced by np.genfromtxt eventually?
          """
          ParserBase.__init__(self, kwds)
  
          self.data = None
          self.buf = []
          self.pos = 0
          self.line_pos = 0
  
          self.encoding = kwds['encoding']
          self.compression = kwds['compression']
          self.memory_map = kwds['memory_map']
          self.skiprows = kwds['skiprows']
  
          self.skipfooter = kwds['skipfooter']
          self.delimiter = kwds['delimiter']
  
          self.quotechar = kwds['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.names_passed = kwds['names'] or None
  
          self.na_filter = kwds['na_filter']
  
          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.compact_ints = kwds['compact_ints']
          self.use_unsigned = kwds['use_unsigned']
          self.thousands = kwds['thousands']
          self.decimal = kwds['decimal']
  
          self.comment = kwds['comment']
          self._comment_lines = []
  
          if isinstance(f, compat.string_types):
              f = _get_handle(f, 'r', encoding=self.encoding,
                              compression=self.compression,
                              memory_map=self.memory_map)
              self.handles.append(f)
          elif self.compression:
              f = _wrap_compressed(f, self.compression, self.encoding)
              self.handles.append(f)
          # in Python 3, convert BytesIO or fileobjects passed with an encoding
          elif compat.PY3 and isinstance(f, compat.BytesIO):
              from io import TextIOWrapper
  
              f = TextIOWrapper(f, encoding=self.encoding)
              self.handles.append(f)
  
          # Set self.data to something that can read lines.
          if hasattr(f, 'readline'):
              self._make_reader(f)
          else:
              self.data = f
  
          # Get columns in two steps: infer from data, then
          # infer column indices from self.usecols if is is specified.
          self._col_indices = None
          self.columns, self.num_original_columns = self._infer_columns()
  
          # Now self.columns has the set of columns that we will process.
          # The original set is stored in self.original_columns.
          if len(self.columns) > 1:
              # we are processing a multi index column
              self.columns, self.index_names, self.col_names, _ = (
                  self._extract_multi_indexer_columns(
                      self.columns, self.index_names, self.col_names
                  )
              )
              # Update list of original names to include all indices.
              self.num_original_columns = len(self.columns)
          else:
              self.columns = self.columns[0]
  
          # get popped off for index
          self.orig_names = list(self.columns)
  
          # needs to be cleaned/refactored
          # multiple date column thing turning into a real spaghetti factory
  
          if not self._has_complex_date_col:
              (index_names, self.orig_names, self.columns) = (
                  self._get_index_name(self.columns))
              self._name_processed = True
              if self.index_names is None:
                  self.index_names = index_names
  
          if self.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')
  
          if self.thousands is None:
              self.nonnum = re.compile('[^-^0-9^%s]+' % self.decimal)
          else:
              self.nonnum = re.compile('[^-^0-9^%s^%s]+' % (self.thousands,
                                                            self.decimal))
  
      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
  
              sniff_sep = True
  
              if sep is not None:
                  sniff_sep = False
                  dia.delimiter = sep
              # attempt to sniff the delimiter
              if sniff_sep:
                  line = f.readline()
                  while self.pos in self.skiprows:
                      self.pos += 1
                      line = f.readline()
  
                  line = self._check_comments([line])[0]
  
                  self.pos += 1
                  self.line_pos += 1
                  sniffed = csv.Sniffer().sniff(line)
                  dia.delimiter = sniffed.delimiter
                  if self.encoding is not None:
                      self.buf.extend(list(
                          UnicodeReader(StringIO(line),
                                        dialect=dia,
                                        encoding=self.encoding)))
                  else:
                      self.buf.extend(list(csv.reader(StringIO(line),
                                                      dialect=dia)))
  
              if self.encoding is not None:
                  reader = UnicodeReader(f, dialect=dia,
                                         encoding=self.encoding,
                                         strict=True)
              else:
                  reader = csv.reader(f, dialect=dia,
                                      strict=True)
  
          else:
              def _read():
                  line = f.readline()
  
                  if compat.PY2 and self.encoding:
                      line = line.decode(self.encoding)
  
                  pat = re.compile(sep)
                  yield pat.split(line.strip())
                  for line in f:
                      yield pat.split(line.strip())
              reader = _read()
  
          self.data = reader
  
      def read(self, rows=None):
          try:
              content = self._get_lines(rows)
          except StopIteration:
              if self._first_chunk:
                  content = []
              else:
                  raise
  
          # done with first read, next time raise StopIteration
          self._first_chunk = False
  
          columns = list(self.orig_names)
          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)
              return _get_empty_meta(names, self.index_col,
                                     self.index_names)
  
          # 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 = self._exclude_implicit_index(alldata)
  
          columns = self._maybe_dedup_names(self.columns)
          columns, data = self._do_date_conversions(columns, data)
  
          data = self._convert_data(data)
          if self.as_recarray:
              return self._to_recarray(data, columns)
  
          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)
  
          if self._implicit_index:
              excl_indices = self.index_col
  
              data = {}
              offset = 0
              for i, col in enumerate(names):
                  while i + offset in excl_indices:
                      offset += 1
                  data[col] = alldata[i + offset]
          else:
              data = dict((k, v) for k, v in zip(names, alldata))
  
          return data
  
      # legacy
      def get_chunk(self, size=None):
          if size is None:
              size = self.chunksize
          return self.read(nrows=size)
  
      def _convert_data(self, data):
          # apply converters
          clean_conv = {}
  
          for col, f in compat.iteritems(self.converters):
              if isinstance(col, int) and col not in self.orig_names:
                  col = self.orig_names[col]
              clean_conv[col] = f
  
          return self._convert_to_ndarrays(data, self.na_values, self.na_fvalues,
                                           self.verbose, clean_conv)
  
      def _to_recarray(self, data, columns):
          dtypes = []
          o = compat.OrderedDict()
  
          # use the columns to "order" the keys
          # in the unordered 'data' dictionary
          for col in columns:
              dtypes.append((str(col), data[col].dtype))
              o[col] = data[col]
  
          tuples = lzip(*o.values())
          return np.array(tuples, dtypes)
  
      def _infer_columns(self):
          names = self.names
          num_original_columns = 0
          clear_buffer = True
          if self.header is not None:
              header = self.header
  
              # we have a mi columns, so read an extra line
              if isinstance(header, (list, tuple, np.ndarray)):
                  have_mi_columns = True
                  header = list(header) + [header[-1] + 1]
              else:
                  have_mi_columns = False
                  header = [header]
  
              columns = []
              for level, hr in enumerate(header):
                  try:
                      line = self._buffered_line()
  
                      while self.line_pos <= hr:
                          line = self._next_line()
  
                  except StopIteration:
                      if self.line_pos < hr:
                          raise ValueError(
                              'Passed header=%s but only %d lines in file'
                              % (hr, self.line_pos + 1))
  
                      # We have an empty file, so check
                      # if columns are provided. That will
                      # serve as the 'line' for parsing
                      if not self.names:
                          raise EmptyDataError(
                              "No columns to parse from file")
  
                      line = self.names[:]
  
                  unnamed_count = 0
                  this_columns = []
                  for i, c in enumerate(line):
                      if c == '':
                          if have_mi_columns:
                              this_columns.append('Unnamed: %d_level_%d'
                                                  % (i, level))
                          else:
                              this_columns.append('Unnamed: %d' % i)
                          unnamed_count += 1
                      else:
                          this_columns.append(c)
  
                  if not have_mi_columns and self.mangle_dupe_cols:
                      counts = {}
                      for i, col in enumerate(this_columns):
                          cur_count = counts.get(col, 0)
                          if cur_count > 0:
                              this_columns[i] = '%s.%d' % (col, cur_count)
                          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)
                          if lc != unnamed_count and lc - ic > unnamed_count:
                              clear_buffer = False
                              this_columns = [None] * lc
                              self.buf = [self.buf[-1]]
  
                  columns.append(this_columns)
                  if len(columns) == 1:
                      num_original_columns = len(this_columns)
  
              if clear_buffer:
                  self._clear_buffer()
  
              if names is not None:
                  if ((self.usecols is not None and
                       len(names) != len(self.usecols)) or
                      (self.usecols is None and
                       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:
                  if not names:
                      raise EmptyDataError(
                          "No columns to parse from file")
  
                  line = names[:]
  
              ncols = len(line)
              num_original_columns = ncols
  
              if not names:
                  if self.prefix:
                      columns = [['%s%d' % (self.prefix, i)
                                  for i in range(ncols)]]
                  else:
                      columns = [lrange(ncols)]
                  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 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
  
      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 any([isinstance(u, string_types) for u in self.usecols]):
                  if len(columns) > 1:
                      raise ValueError("If using multiple headers, usecols must "
                                       "be integers.")
                  col_indices = []
                  for u in self.usecols:
                      if isinstance(u, string_types):
                          col_indices.append(usecols_key.index(u))
                      else:
                          col_indices.append(u)
              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], compat.string_types):
              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]
  
          # This is to avoid warnings we get in Python 2.x if
          # we find ourselves comparing with non-Unicode
          if compat.PY2 and not isinstance(first_elt, unicode):  # noqa
              try:
                  first_elt = u(first_elt)
              except UnicodeDecodeError:
                  return first_row
  
          if first_elt != _BOM:
              return first_row
  
          first_row = first_row[0]
  
          if len(first_row) > 1 and first_row[1] == self.quotechar:
              start = 2
              quote = first_row[1]
              end = first_row[2:].index(quote) + 2
  
              # Extract the data between the quotation marks
              new_row = first_row[start:end]
  
              # Extract any remaining data after the second
              # quotation mark.
              if len(first_row) > end + 1:
                  new_row += first_row[end + 1:]
              return [new_row]
          elif len(first_row) > 1:
              return [first_row[1:]]
          else:
              # First row is just the BOM, so we
              # return an empty string.
              return [""]
  
      def _empty(self, line):
          return not line or all(not x for x in line)
  
      def _next_line(self):
          if isinstance(self.data, list):
              while self.pos in self.skiprows:
                  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._empty(self.data[
                              self.pos - 1]) or line):
                          break
                      elif self.skip_blank_lines:
                          ret = self._check_empty([line])
                          if ret:
                              line = ret[0]
                              break
                  except IndexError:
                      raise StopIteration
          else:
              while self.pos in self.skiprows:
                  self.pos += 1
                  next(self.data)
  
              while True:
                  try:
                      orig_line = next(self.data)
                  except csv.Error as e:
                      if 'NULL byte' in str(e):
                          raise csv.Error(
                              'NULL byte detected. This byte '
                              'cannot be processed in Python\'s '
                              'native csv library at the moment, '
                              'so please pass in engine=\'c\' instead.')
                      else:
                          raise
                  line = self._check_comments([orig_line])[0]
                  self.pos += 1
                  if (not self.skip_blank_lines and
                          (self._empty(orig_line) or line)):
                      break
                  elif self.skip_blank_lines:
                      ret = self._check_empty([line])
                      if ret:
                          line = ret[0]
                          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 _check_comments(self, lines):
          if self.comment is None:
              return lines
          ret = []
          for l in lines:
              rl = []
              for x in l:
                  if (not isinstance(x, compat.string_types) or
                          self.comment not in x):
                      rl.append(x)
                  else:
                      x = x[:x.find(self.comment)]
                      if len(x) > 0:
                          rl.append(x)
                      break
              ret.append(rl)
          return ret
  
      def _check_empty(self, lines):
          ret = []
          for l in lines:
              # Remove empty lines and lines with only one whitespace value
              if (len(l) > 1 or len(l) == 1 and
                      (not isinstance(l[0], compat.string_types) or
                       l[0].strip())):
                  ret.append(l)
          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 l in lines:
              rl = []
              for i, x in enumerate(l):
                  if (not isinstance(x, compat.string_types) or
                      search not in x or
                      (self._no_thousands_columns and
                       i in self._no_thousands_columns) or
                          self.nonnum.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 = lrange(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 = lrange(implicit_first_cols)
  
              index_name = None
  
          else:
              # Case 2
              (index_name, columns_,
               self.index_col) = _clean_index_names(columns, self.index_col)
  
          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)
  
          # see gh-13320
          zipped_content = list(lib.to_object_array(
              content, min_width=col_len).T)
          zip_len = len(zipped_content)
  
          if self.skipfooter < 0:
              raise ValueError('skip footer cannot be negative')
  
          # Loop through rows to verify lengths are correct.
          if (col_len != zip_len and
                  self.index_col is not False and
                  self.usecols is None):
              i = 0
              for (i, l) in enumerate(content):
                  if len(l) != col_len:
                      break
  
              footers = 0
              if self.skipfooter:
                  footers = self.skipfooter
  
              row_num = self.pos - (len(content) - i + footers)
  
              msg = ('Expected %d fields in line %d, saw %d' %
                     (col_len, row_num + 1, zip_len))
              raise ValueError(msg)
  
          if self.usecols:
              if self._implicit_index:
                  zipped_content = [
                      a for i, a in enumerate(zipped_content)
                      if (i < len(self.index_col) or
                          i - len(self.index_col) in self._col_indices)]
              else:
                  zipped_content = [a for i, a in enumerate(zipped_content)
                                    if i in self._col_indices]
          return zipped_content
  
      def _get_lines(self, rows=None):
          source = self.data
          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(source, list):
                  if self.pos > len(source):
                      raise StopIteration
                  if rows is None:
                      new_rows = source[self.pos:]
                      new_pos = len(source)
                  else:
                      new_rows = source[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 i + self.pos not in self.skiprows]
  
                  lines.extend(new_rows)
                  self.pos = new_pos
  
              else:
                  new_rows = []
                  try:
                      if rows is not None:
                          for _ in range(rows):
                              new_rows.append(next(source))
                          lines.extend(new_rows)
                      else:
                          rows = 0
                          while True:
                              try:
                                  new_rows.append(next(source))
                                  rows += 1
                              except csv.Error as inst:
                                  if 'newline inside string' in str(inst):
                                      row_num = str(self.pos + rows)
                                      msg = ('EOF inside string starting with '
                                             'line ' + row_num)
                                      raise Exception(msg)
                                  raise
                  except StopIteration:
                      if self.skiprows:
                          new_rows = [row for i, row in enumerate(new_rows)
                                      if self.pos + i not in self.skiprows]
                      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._check_empty(lines)
          lines = self._check_thousands(lines)
          return self._check_decimal(lines)
  
  
  def _make_date_converter(date_parser=None, dayfirst=False,
                           infer_datetime_format=False):
      def converter(*date_cols):
          if date_parser is None:
              strs = _concat_date_cols(date_cols)
  
              try:
                  return tools.to_datetime(
                      _ensure_object(strs),
                      utc=None,
                      box=False,
                      dayfirst=dayfirst,
                      errors='ignore',
                      infer_datetime_format=infer_datetime_format
                  )
              except:
                  return tools.to_datetime(
                      lib.try_parse_dates(strs, dayfirst=dayfirst))
          else:
              try:
                  result = tools.to_datetime(
                      date_parser(*date_cols), errors='ignore')
                  if isinstance(result, datetime.datetime):
                      raise Exception('scalar parser')
                  return result
              except Exception:
                  try:
                      return tools.to_datetime(
                          lib.try_parse_dates(_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('New date column already in dict %s' %
                                       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 compat.iteritems(parse_spec):
              if new_name in data_dict:
                  raise ValueError('Date column %s already in dict' %
                                   new_name)
  
              _, 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(str(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 = _NA_VALUES
          else:
              na_values = []
          na_fvalues = set()
      elif isinstance(na_values, dict):
          if keep_default_na:
              for k, v in compat.iteritems(na_values):
                  if not is_list_like(v):
                      v = [v]
                  v = set(v) | _NA_VALUES
                  na_values[k] = v
          na_fvalues = dict([
              (k, _floatify_na_values(v)) for k, v in na_values.items()  # noqa
          ])
      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 | _NA_VALUES
  
          na_fvalues = _floatify_na_values(na_values)
  
      return na_values, na_fvalues
  
  
  def _clean_index_names(columns, index_col):
      if not _is_index_col(index_col):
          return None, columns, index_col
  
      columns = list(columns)
  
      cp_cols = list(columns)
      index_names = []
  
      # don't mutate
      index_col = list(index_col)
  
      for i, c in enumerate(index_col):
          if isinstance(c, compat.string_types):
              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)
  
      # hack
      if isinstance(index_names[0], compat.string_types)\
              and 'Unnamed' in index_names[0]:
          index_names[0] = None
  
      return index_names, columns, index_col
  
  
  def _get_empty_meta(columns, index_col, index_names, dtype=None):
      columns = list(columns)
  
      if dtype is None:
          dtype = {}
      else:
          if not isinstance(dtype, dict):
              dtype = defaultdict(lambda: dtype)
          # Convert column indexes to column names.
          dtype = dict((columns[k] if is_integer(k) else k, v)
                       for k, v in compat.iteritems(dtype))
  
      if index_col is None or index_col is False:
          index = Index([])
      else:
          index = [np.empty(0, dtype=dtype.get(index_name, np.object))
                   for index_name in index_names]
          index = MultiIndex.from_arrays(index, names=index_names)
          index_col.sort()
          for i, n in enumerate(index_col):
              columns.pop(n - i)
  
      col_dict = dict((col_name,
                       np.empty(0, dtype=dtype.get(col_name, np.object)))
                      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:
              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("%s.0" % v)
                  result.append(str(v))
  
              result.append(v)
          except:
              pass
          try:
              result.append(int(x))
          except:
              pass
      return set(result)
  
  
  def _get_na_values(col, na_values, na_fvalues):
      if isinstance(na_values, dict):
          if col in na_values:
              return na_values[col], na_fvalues[col]
          else:
              return _NA_VALUES, 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
  
  
  def _concat_date_cols(date_cols):
      if len(date_cols) == 1:
          if compat.PY3:
              return np.array([compat.text_type(x) for x in date_cols[0]],
                              dtype=object)
          else:
              return np.array([
                  str(x) if not isinstance(x, compat.string_types) else x
                  for x in date_cols[0]
              ], dtype=object)
  
      rs = np.array([' '.join([compat.text_type(y) for y in x])
                     for x in zip(*date_cols)], dtype=object)
      return rs
  
  
  class FixedWidthReader(BaseIterator):
      """
      A reader of fixed-width lines.
      """
  
      def __init__(self, f, colspecs, delimiter, comment):
          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()
          else:
              self.colspecs = colspecs
  
          if not isinstance(self.colspecs, (tuple, list)):
              raise TypeError("column specifications must be a list or tuple, "
                              "input was a %r" % 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, n):
          rows = []
          for i, row in enumerate(self.f, 1):
              rows.append(row)
              if i >= n:
                  break
          self.buffer = iter(rows)
          return rows
  
      def detect_colspecs(self, n=100):
          # Regex escape the delimiters
          delimiters = ''.join([r'\%s' % x for x in self.delimiter])
          pattern = re.compile('([^%s]+)' % delimiters)
          rows = self.get_rows(n)
          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]
          return list(zip(edges[::2], edges[1::2]))
  
      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')
  
          PythonParser.__init__(self, f, **kwds)
  
      def _make_reader(self, f):
          self.data = FixedWidthReader(f, self.colspecs, self.delimiter,
                                       self.comment)
  