text: """
  Module contains tools for processing files into DataFrames or other objects
  """
  from StringIO import StringIO
  import re
  from itertools import izip
  import csv
  from warnings import warn
  
  import numpy as np
  
  from pandas.core.index import Index, MultiIndex
  from pandas.core.frame import DataFrame
  import datetime
  import pandas.core.common as com
  from pandas.util import py3compat
  from pandas.io.date_converters import generic_parser
  from pandas.io.common import get_filepath_or_buffer
  
  from pandas.util.decorators import Appender
  
  import pandas.lib as lib
  import pandas.tslib as tslib
  import pandas.parser as _parser
  from pandas.tseries.period import Period
  
  _parser_params = """Also supports optionally iterating or breaking of the file
  into chunks.
  
  Parameters
  ----------
  filepath_or_buffer : string or file handle / 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
  lineterminator : string (length 1), default None
      Character to break file into lines. Only valid with C parser
  quotechar : string
      The character to used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored.
  quoting : int
      Controls whether quotes should be recognized. Values are taken from
      `csv.QUOTE_*` values. Acceptable values are 0, 1, 2, and 3 for
      QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONE, and QUOTE_NONNUMERIC, respectively.
  skipinitialspace : boolean, default False
      Skip spaces after delimiter
  escapechar : string
  dtype : Type name or dict of column -> type
      Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
  compression : {'gzip', 'bz2', None}, default None
      For on-the-fly decompression of on-disk data
  dialect : string 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
  header : int, default 0 if names parameter not specified,
      Row to use for the column labels of the parsed DataFrame. Specify None if
      there is no header row. Can be a list of integers that specify row
      locations for a multi-index on the columns E.g. [0,1,3]. Interveaning
      rows that are not specified (E.g. 2 in this example are skipped)
  skiprows : list-like or integer
      Row numbers to skip (0-indexed) or number of rows to skip (int)
      at the start of the file
  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)
  names : array-like
      List of column names to use. If file contains no header row, then you
      should explicitly pass header=None
  prefix : string or None (default)
      Prefix to add to column numbers when no header, e.g 'X' for X0, X1, ...
  na_values : list-like or dict, default None
      Additional strings to recognize as NA/NaN. If dict passed, specific
      per-column NA values
  true_values : list
      Values to consider as True
  false_values : list
      Values to consider as False
  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
  parse_dates : boolean, list of ints or names, list of lists, or dict
      If True -> try parsing the index.
      If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
      If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
      {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo'
  keep_date_col : boolean, default False
      If True and parse_dates specifies combining multiple columns then
      keep the original columns.
  date_parser : function
      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.
  dayfirst : boolean, default False
      DD/MM format dates, international and European format
  thousands : str, default None
      Thousands separator
  comment : str, default None
      Indicates remainder of line should not be parsed
      Does not support line commenting (will return empty line)
  decimal : str, default '.'
      Character to recognize as decimal point. E.g. use ',' for European data
  nrows : int, default None
      Number of rows of file to read. Useful for reading pieces of large files
  iterator : boolean, default False
      Return TextFileReader object
  chunksize : int, default None
      Return TextFileReader object for iteration
  skipfooter : int, default 0
      Number of line at bottom of file to skip
  converters : dict. optional
      Dict of functions for converting values in certain columns. Keys can either
      be integers or column labels
  verbose : boolean, default False
      Indicate number of NA values placed in non-numeric columns
  delimiter : string, default None
      Alternative argument name for sep. Regular expressions are accepted.
  encoding : string, default None
      Encoding to use for UTF when reading/writing (ex. 'utf-8')
  squeeze : boolean, default False
      If the parsed data only contains one column then return a Series
  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
  usecols : array-like
      Return a subset of the columns.
      Results in much faster parsing time and lower memory usage.
  mangle_dupe_cols: boolean, default True
      Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'
  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)
  
  Returns
  -------
  result : DataFrame or TextParser
  """
  
  _csv_sep = """sep : string, default ','
      Delimiter to use. If sep is None, will try to automatically determine
      this. Regular expressions are accepted.
  """
  
  _table_sep = """sep : string, default \\t (tab-stop)
      Delimiter to use. Regular expressions are accepted."""
  
  _read_csv_doc = """
  Read CSV (comma-separated) file into DataFrame
  
  %s
  """ % (_parser_params % _csv_sep)
  
  _read_table_doc = """
  Read general delimited file into DataFrame
  
  %s
  """ % (_parser_params % _table_sep)
  
  _fwf_widths = """\
  colspecs : a list of pairs (tuples), giving the extents
      of the fixed-width fields of each line as half-open internals
      (i.e.,  [from, to[  ).
  widths : 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 _read(filepath_or_buffer, kwds):
      "Generic reader of line files."
      encoding = kwds.get('encoding', None)
      skipfooter = kwds.pop('skipfooter', None)
      if skipfooter is not None:
          kwds['skip_footer'] = skipfooter
  
      filepath_or_buffer, _ = get_filepath_or_buffer(filepath_or_buffer)
  
      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)
      nrows = kwds.pop('nrows', None)
      chunksize = kwds.get('chunksize', None)
  
      # Create the parser.
      parser = TextFileReader(filepath_or_buffer, **kwds)
  
      if nrows is not None:
          return parser.read(nrows)
      elif chunksize or iterator:
          return parser
  
      return parser.read()
  
  _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,
      'skip_footer': 0,
      'converters': None,
  
      'keep_default_na': True,
      'thousands': None,
      'comment': None,
  
      # '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':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,
      'factorize': True,
      'dtype': None,
      'decimal': b'.'
  }
  
  _fwf_defaults = {
      'colspecs': None,
      'widths': None
  }
  
  _c_unsupported = set(['skip_footer'])
  _python_unsupported = set(_c_parser_defaults.keys())
  
  
  def _make_parser_function(name, sep=','):
  
      def parser_f(filepath_or_buffer,
                   sep=sep,
                   dialect=None,
                   compression=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,
                   skipfooter=None,
                   skip_footer=0,
                   na_values=None,
                   na_fvalues=None,
                   true_values=None,
                   false_values=None,
                   delimiter=None,
                   converters=None,
                   dtype=None,
                   usecols=None,
  
                   engine='c',
                   delim_whitespace=False,
                   as_recarray=False,
                   na_filter=True,
                   compact_ints=False,
                   use_unsigned=False,
                   low_memory=_c_parser_defaults['low_memory'],
                   buffer_lines=None,
                   warn_bad_lines=True,
                   error_bad_lines=True,
  
                   keep_default_na=True,
                   thousands=None,
                   comment=None,
                   decimal=b'.',
  
                   parse_dates=False,
                   keep_date_col=False,
                   dayfirst=False,
                   date_parser=None,
  
                   memory_map=False,
                   nrows=None,
                   iterator=False,
                   chunksize=None,
  
                   verbose=False,
                   encoding=None,
                   squeeze=False,
                   mangle_dupe_cols=True,
                   tupleize_cols=True,
                   ):
  
          # Alias sep -> delimiter.
          if delimiter is None:
              delimiter = sep
  
          kwds = dict(delimiter=delimiter,
                      engine=engine,
                      dialect=dialect,
                      compression=compression,
  
                      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,
                      na_fvalues=na_fvalues,
                      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,
  
                      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,
              )
  
          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=None, widths=None, **kwds):
      # Check input arguments.
      if bool(colspecs is None) == bool(widths is None):
          raise ValueError("You must specify only one of 'widths' and "
                           "'colspecs'")
  
      # Compute 'colspec' 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)
  
  
  # common NA values
  # no longer excluding inf representations
  # '1.#INF','-1.#INF', '1.#INF000000',
  _NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN',
                   '#N/A N/A', 'NA', '#NA', 'NULL', 'NaN',
                   'nan', ''])
  
  
  class TextFileReader(object):
      """
  
      Passed dialect overrides any of the related parser options
  
      """
  
      def __init__(self, f, engine='python', **kwds):
  
          self.f = f
  
          if kwds.get('dialect') is not None:
              dialect = kwds['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
  
          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 _get_options_with_defaults(self, engine):
          kwds = self.orig_options
  
          options = {}
          for argname, default in _parser_defaults.iteritems():
              if argname in kwds:
                  value = kwds[argname]
              else:
                  value = default
  
              options[argname] = value
  
          for argname, default in _c_parser_defaults.iteritems():
              if argname in kwds:
                  value = kwds[argname]
                  if engine != 'c' and value != default:
                      raise ValueError('%s is not supported with %s parser' %
                                       (argname, engine))
              options[argname] = value
  
          if engine == 'python-fwf':
              for argname, default in _fwf_defaults.iteritems():
                  if argname in kwds:
                      value = kwds[argname]
                  options[argname] = value
  
          return options
  
      def _clean_options(self, options, engine):
          result = options.copy()
  
          sep = options['delimiter']
          if (sep is None and not options['delim_whitespace']):
              if engine == 'c':
                  print ('Using Python parser to sniff delimiter')
                  engine = 'python'
          elif sep is not None and len(sep) > 1:
              # wait until regex engine integrated
              engine = 'python'
  
          # C engine not supported yet
          if engine == 'c':
              if options['skip_footer'] > 0:
                  engine = 'python'
  
          if engine == 'c':
              for arg in _c_unsupported:
                  del result[arg]
  
          if 'python' in engine:
              for arg in _python_unsupported:
                  del result[arg]
  
          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')
  
          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 AssertionError()
          else:
              converters = {}
  
          # Converting values to NA
          na_values, na_fvalues = _clean_na_values(na_values, keep_default_na)
  
          if com.is_integer(skiprows):
              skiprows = range(skiprows)
          skiprows = set() if skiprows is None else set(skiprows)
  
          # put stuff back
          result['index_col'] = index_col
          result['names'] = names
          result['converters'] = converters
          result['na_values'] = na_values
          result['na_fvalues'] = na_fvalues
          result['skiprows'] = skiprows
  
          return result, engine
  
      def __iter__(self):
          try:
              if self.chunksize:
                  while True:
                      yield self.read(self.chunksize)
              else:
                  yield self.read()
          except StopIteration:
              pass
  
      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 NotImplementedError
  
      def read(self, nrows=None):
          suppressed_warnings = False
          if nrows is not None:
              if self.options.get('skip_footer'):
                  raise ValueError('skip_footer not supported for iteration')
  
              # # XXX hack
              # if isinstance(self._engine, CParserWrapper):
              #     suppressed_warnings = True
              #     self._engine.set_error_bad_lines(False)
  
          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)
  
          df = DataFrame(col_dict, columns=columns, index=index)
  
          if self.squeeze and len(df.columns) == 1:
              return df[df.columns[0]]
          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
  
  
  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.pop('index_col', None)
          self.index_names = None
          self.col_names = None
  
          self.parse_dates = 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.tupleize_cols = kwds.get('tupleize_cols',True)
  
          self._date_conv = _make_date_converter(date_parser=self.date_parser,
                                                 dayfirst=self.dayfirst)
  
          # validate header options for mi
          self.header = kwds.get('header')
          if isinstance(self.header,(list,tuple,np.ndarray)):
              if kwds.get('as_recarray'):
                  raise Exception("cannot specify as_recarray when "
                                  "specifying a multi-index header")
              if kwds.get('usecols'):
                  raise Exception("cannot specify usecols when "
                                  "specifying a multi-index header")
              if kwds.get('names'):
                  raise Exception("cannot specify names when "
                                  "specifying a multi-index header")
  
              # validate index_col that only contains integers
              if self.index_col is not None:
                  if not (isinstance(self.index_col,(list,tuple,np.ndarray)) and all(
                          [ com.is_integer(i) for i in self.index_col ]) or com.is_integer(self.index_col)):
                      raise Exception("index_col must only contain row numbers "
                                      "when specifying a multi-index header")
  
          self._name_processed = False
  
      @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 np.isscalar(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)
  
          orig_header = list(header)
  
          # 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 = zip(*[ extract(r) for r in header ])
          names = ic + columns
  
          # 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 c[n] for c in columns ]):
                  raise _parser.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_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 len(self.index_col) == 0:
              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.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, basestring):
                  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, basestring):
                  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 dct.iteritems():
              conv_f = None if converters is None else converters.get(c, None)
              col_na_values, col_na_fvalues = _get_na_values(c, na_values, na_fvalues)
              coerce_type = True
              if conv_f is not None:
                  values = lib.map_infer(values, conv_f)
                  coerce_type = False
              cvals, na_count = self._convert_types(values,
                                                    set(col_na_values) | col_na_fvalues,
                                                    coerce_type)
              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 com.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
  
      def _exclude_implicit_index(self, alldata):
  
          if self._implicit_index:
              excl_indices = self.index_col
  
              data = {}
              offset = 0
              for i, col in enumerate(self.orig_names):
                  while i + offset in excl_indices:
                      offset += 1
                  data[col] = alldata[i + offset]
          else:
              data = dict((k, v) for k, v in izip(self.orig_names, alldata))
  
          return data
  
  
  class CParserWrapper(ParserBase):
      """
  
      """
  
      def __init__(self, src, **kwds):
          self.kwds = kwds
          kwds = kwds.copy()
  
          self.as_recarray = kwds.get('as_recarray', False)
          ParserBase.__init__(self, kwds)
  
          if 'utf-16' in (kwds.get('encoding') or ''):
              if isinstance(src, basestring):
                  src = open(src, 'rb')
              src = com.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 = 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 = ['X%d' % i
                                for i in range(self._reader.table_width)]
              else:
                  self.names = range(self._reader.table_width)
  
          # XXX
          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 _set_noconvert_columns(self):
          names = self.names
  
          def _set(x):
              if com.is_integer(x):
                  self._reader.set_noconvert(x)
              else:
                  self._reader.set_noconvert(names.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)
  
      def set_error_bad_lines(self, status):
          self._reader.set_error_bad_lines(int(status))
  
      def read(self, nrows=None):
          if self.as_recarray:
              # what to do if there are leading columns?
              return self._reader.read(nrows)
  
          try:
              data = self._reader.read(nrows)
          except StopIteration:
              if nrows is None:
                  return None, self.names, {}
              else:
                  raise
  
          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)
  
              # 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)
  
              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 : iterable, default None
          Custom NA values
      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
      skip_footer : int
          Number of line at bottom of file to skip
      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
      """
      kwds['engine'] = 'python'
      return TextFileReader(*args, **kwds)
  
  # delimiter=None, dialect=None, names=None, header=0,
  # index_col=None,
  # na_values=None,
  # na_filter=True,
  # thousands=None,
  # quotechar='"',
  # escapechar=None,
  # doublequote=True,
  # skipinitialspace=False,
  # quoting=csv.QUOTE_MINIMAL,
  # comment=None, parse_dates=False, keep_date_col=False,
  # date_parser=None, dayfirst=False,
  # chunksize=None, skiprows=None, skip_footer=0, converters=None,
  # verbose=False, encoding=None, squeeze=False):
  
  
  def count_empty_vals(vals):
      return sum([1 for v in vals if v == '' or v is None])
  
  
  def _wrap_compressed(f, compression):
      compression = compression.lower()
      if compression == 'gzip':
          import gzip
          return gzip.GzipFile(fileobj=f)
      elif compression == 'bz2':
          raise ValueError('Python cannot read bz2 data from file handle')
      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
  
          if kwds['usecols'] is not None:
              raise Exception("usecols not supported with engine='python'"
                              " or multicharacter separators (yet).")
  
          self.encoding = kwds['encoding']
          self.compression = kwds['compression']
          self.skiprows = kwds['skiprows']
  
          self.skip_footer = kwds['skip_footer']
          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.mangle_dupe_cols = kwds.get('mangle_dupe_cols',True)
  
          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.thousands = kwds['thousands']
          self.comment = kwds['comment']
          self._comment_lines = []
  
          if isinstance(f, basestring):
              f = com._get_handle(f, 'r', encoding=self.encoding,
                                  compression=self.compression)
          elif self.compression:
              f = _wrap_compressed(f, self.compression)
  
          if hasattr(f, 'readline'):
              self._make_reader(f)
          else:
              self.data = f
          self.columns = self._infer_columns()
  
          # we are processing a multi index column
          if len(self.columns) > 1:
              self.columns, self.index_names, self.col_names, _ = self._extract_multi_indexer_columns(
                  self.columns, self.index_names, self.col_names)
          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._get_index_name(self.columns)
              self._name_processed = True
              if self.index_names is None:
                  self.index_names = index_names
          self._first_chunk = True
  
      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
                  sniffed = csv.Sniffer().sniff(line)
                  dia.delimiter = sniffed.delimiter
                  if self.encoding is not None:
                      self.buf.extend(list(
                          com.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 = com.UnicodeReader(f, dialect=dia,
                                             encoding=self.encoding,
                                             strict=True)
              else:
                  reader = csv.reader(f, dialect=dia,
                                      strict=True)
  
          else:
              def _read():
                  line = next(f)
                  pat = re.compile(sep)
                  if (py3compat.PY3 and isinstance(line, bytes)):
                      yield pat.split(line.decode('utf-8').strip())
                      for line in f:
                          yield pat.split(line.decode('utf-8').strip())
                  else:
                      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 len(content) == 0:  # pragma: no cover
              # DataFrame with the right metadata, even though it's length 0
              return _get_empty_meta(self.orig_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, data = self._do_date_conversions(self.columns, data)
  
          data = self._convert_data(data)
          index, columns = self._make_index(data, alldata, columns, indexnamerow)
  
          return index, columns, 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 self.converters.iteritems():
              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 _infer_columns(self):
          names = self.names
  
          if self.header is not None:
              header = self.header
  
              # we have a mi columns, so read and 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):
  
                  if len(self.buf) > 0:
                      line = self.buf[0]
                  else:
                      line = self._next_line()
  
                  while self.pos <= hr:
                      line = self._next_line()
  
                  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)
                      else:
                          this_columns.append(c)
  
                  if not have_mi_columns:
                      if 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
  
                  columns.append(this_columns)
  
              self._clear_buffer()
  
              if names is not None:
                  if len(names) != len(columns[0]):
                      raise Exception('Number of passed names did not match '
                                      'number of header fields in the file')
                  if len(columns) > 1:
                      raise Exception('Cannot pass names with multi-index columns')
                  columns = [ names ]
  
          else:
              if len(self.buf) > 0:
                  line = self.buf[0]
              else:
                  line = self._next_line()
  
              ncols = len(line)
              if not names:
                  if self.prefix:
                      columns = [ ['X%d' % i for i in range(ncols)] ]
                  else:
                      columns = [ range(ncols) ]
              else:
                  columns = [ names ]
  
          return columns
  
      def _next_line(self):
          if isinstance(self.data, list):
              while self.pos in self.skiprows:
                  self.pos += 1
  
              try:
                  line = self.data[self.pos]
              except IndexError:
                  raise StopIteration
          else:
              while self.pos in self.skiprows:
                  next(self.data)
                  self.pos += 1
  
              line = next(self.data)
  
          line = self._check_comments([line])[0]
          line = self._check_thousands([line])[0]
  
          self.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, basestring) 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_thousands(self, lines):
          if self.thousands is None:
              return lines
          nonnum = re.compile('[^-^0-9^%s^.]+' % self.thousands)
          ret = []
          for l in lines:
              rl = []
              for x in l:
                  if (not isinstance(x, basestring) or
                      self.thousands not in x or
                          nonnum.search(x.strip())):
                      rl.append(x)
                  else:
                      rl.append(x.replace(',', ''))
              ret.append(rl)
          return ret
  
      def _clear_buffer(self):
          self.buf = []
  
      _implicit_index = False
  
      def _get_index_name(self, columns):
          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
  
          index_name = None
  
          # implicitly index_col=0 b/c 1 fewer column names
          implicit_first_cols = 0
          if line is not None:
              # leave it 0, #2442
              if self.index_col is not False:
                  implicit_first_cols = len(line) - len(columns)
  
              if next_line is not None:
                  if len(next_line) == len(line) + len(columns):
                      # column and index names on diff rows
                      implicit_first_cols = 0
  
                      self.index_col = range(len(line))
                      self.buf = self.buf[1:]
  
                      for c in reversed(line):
                          columns.insert(0, c)
  
                      return line, columns, orig_names
  
          if implicit_first_cols > 0:
              self._implicit_index = True
              if self.index_col is None:
                  self.index_col = range(implicit_first_cols)
              index_name = None
  
          else:
              (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):
          zipped_content = list(lib.to_object_array(content).T)
  
          col_len = len(self.orig_names)
          zip_len = len(zipped_content)
  
          if self._implicit_index:
              col_len += len(self.index_col)
  
          if not ((self.skip_footer >= 0)):
              raise AssertionError()
  
          if col_len != zip_len and self.index_col is not False:
              row_num = -1
              i = 0
              for (i, l) in enumerate(content):
                  if len(l) != col_len:
                      break
  
              footers = 0
              if self.skip_footer:
                  footers = self.skip_footer
  
              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)
  
          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:
                      lines.extend(source[self.pos:])
                      self.pos = len(source)
                  else:
                      lines.extend(source[self.pos:self.pos + rows])
                      self.pos += rows
              else:
                  new_rows = []
                  try:
                      if rows is not None:
                          for _ in xrange(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, 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:
                      lines.extend(new_rows)
                      if len(lines) == 0:
                          raise
                  self.pos += len(new_rows)
  
              self.buf = []
          else:
              lines = new_rows
  
          if self.skip_footer:
              lines = lines[:-self.skip_footer]
  
          lines = self._check_comments(lines)
          return self._check_thousands(lines)
  
  
  def _make_date_converter(date_parser=None, dayfirst=False):
      def converter(*date_cols):
          if date_parser is None:
              strs = _concat_date_cols(date_cols)
              try:
                  return tslib.array_to_datetime(com._ensure_object(strs),
                                                 utc=None, dayfirst=dayfirst)
              except:
                  return lib.try_parse_dates(strs, dayfirst=dayfirst)
          else:
              try:
                  result = date_parser(*date_cols)
                  if isinstance(result, datetime.datetime):
                      raise Exception('scalar parser')
                  return result
              except Exception:
                  try:
                      return lib.try_parse_dates(_concat_date_cols(date_cols),
                                                 parser=date_parser,
                                                 dayfirst=dayfirst)
                  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 np.isscalar(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 parse_spec.iteritems():
              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 and keep_default_na:
          na_values = _NA_VALUES
          na_fvalues = set()
      elif isinstance(na_values, dict):
          if keep_default_na:
              for k, v in na_values.iteritems():
                  v = set(list(v)) | _NA_VALUES
                  na_values[k] = v
          na_fvalues = dict([ (k, _floatify_na_values(v)) for k, v in na_values.items() ])
      else:
          if not com.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, basestring):
              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], basestring) 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):
      columns = list(columns)
  
      if index_col is not None:
          index = MultiIndex.from_arrays([[]] * len(index_col),
                                         names=index_names)
          for n in index_col:
              columns.pop(n)
      else:
          index = Index([])
  
      return index, columns, {}
  
  
  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:
              values = na_values[col]
              fvalues = na_fvalues[col]
              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 py3compat.PY3:
              return np.array([unicode(x) for x in date_cols[0]], dtype=object)
          else:
              return np.array([str(x) if not isinstance(x, basestring) else x
                               for x in date_cols[0]], dtype=object)
  
      # stripped = [map(str.strip, x) for x in date_cols]
      rs = np.array([' '.join([unicode(y) for y in x])
                     for x in zip(*date_cols)], dtype=object)
      return rs
  
  
  class FixedWidthReader(object):
      """
      A reader of fixed-width lines.
      """
      def __init__(self, f, colspecs, filler, thousands=None):
          self.f = f
          self.colspecs = colspecs
          self.filler = filler  # Empty characters between fields.
          self.thousands = thousands
  
          if not ( isinstance(colspecs, (tuple, list))):
              raise AssertionError()
  
          for colspec in colspecs:
              if not ( isinstance(colspec, (tuple, list)) and
                         len(colspec) == 2 and
                         isinstance(colspec[0], int) and
                         isinstance(colspec[1], int) ):
                  raise AssertionError()
  
      def next(self):
          line = next(self.f)
          # Note: 'colspecs' is a sequence of half-open intervals.
          return [line[fromm:to].strip(self.filler or ' ')
                  for (fromm, to) in self.colspecs]
  
      # Iterator protocol in Python 3 uses __next__()
      __next__ = next
  
  
  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 = list(kwds.pop('colspecs'))
  
          PythonParser.__init__(self, f, **kwds)
  
      def _make_reader(self, f):
          self.data = FixedWidthReader(f, self.colspecs, self.delimiter)
  
  
  ##### deprecations in 0.12 #####
  ##### remove in 0.12         #####
  
  from pandas.io import clipboard
  def read_clipboard(**kwargs):
      warn("read_clipboard is now a top-level accessible via pandas.read_clipboard", FutureWarning)
      clipboard.read_clipboard(**kwargs)
  
  def to_clipboard(obj):
      warn("to_clipboard is now an object level method accessible via obj.to_clipboard()", FutureWarning)
      clipboard.to_clipboard(obj)
  
  from pandas.io import excel
  class ExcelWriter(excel.ExcelWriter):
      def __init__(self, path):
          warn("ExcelWriter can now be imported from: pandas.io.excel", FutureWarning)
          super(ExcelWriter, self).__init__(path)
  
  class ExcelFile(excel.ExcelFile):
      def __init__(self, path_or_buf, kind=None, **kwds):
          warn("ExcelFile can now be imported from: pandas.io.excel", FutureWarning)
          super(ExcelFile, self).__init__(path_or_buf, kind=kind, **kwds)
  