parsers.py

"""
Module contains tools for processing files into DataFrames or other objects
"""
from __future__ import print_function
from pandas.compat import range, lrange, StringIO, lzip, zip, string_types, map
from pandas import compat
import re
import csv
import warnings

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.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)
from pandas.tseries import tools

from pandas.util.decorators import Appender

import pandas.lib as lib
import pandas.tslib as tslib
import pandas.parser as _parser


class ParserWarning(Warning):
    pass

_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 : 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 (length 1)
    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 None
    Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
    QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
    Default (None) results in QUOTE_MINIMAL behavior.
skipinitialspace : boolean, default False
    Skip spaces after delimiter
escapechar : string (length 1), default None
    One-character string used to escape delimiter when quoting is QUOTE_NONE.
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')
compression : {'gzip', 'bz2', 'zip', 'infer', None}, default 'infer'
    For on-the-fly decompression of on-disk data. If 'infer', then use gzip or
    bz2 if filepath_or_buffer is a string ending in '.gz', '.bz2' or '.zip',
    respectively, and no decompression otherwise. Set to None for no
    decompression.
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, list of ints, default 'infer'
    Row number(s) to use as the column names, and the start of the
    data.  Defaults 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 are 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.
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
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, default None
    List of column names to use. If file contains no header row, then you
    should explicitly pass header=None
prefix : string, default None
    Prefix to add to column numbers when no header, e.g 'X' for X0, X1, ...
na_values : str, list-like or dict, default None
    Additional strings to recognize as NA/NaN. If dict passed, specific
    per-column NA values
true_values : list, default None
    Values to consider as True
false_values : list, default None
    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, default False
    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'
    A fast-path exists for iso8601-formatted dates.
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
thousands : str, default None
    Thousands separator
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.
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 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``.
skipfooter : int, default 0
    Number of lines at bottom of file to skip (Unsupported with engine='c')
converters : dict, default None
    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'). `List of Python
    standard encodings
    <https://docs.python.org/3/library/codecs.html#standard-encodings>`_
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, default None
    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)
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).
infer_datetime_format : boolean, default False
    If True and parse_dates is enabled for a column, attempt to infer
    the datetime format to speed up the processing
skip_blank_lines : boolean, default True
    If True, skip over blank lines rather than interpreting as NaN values

Returns
-------
result : DataFrame or TextParser
"""

_csv_params = """sep : string, default ','
    Delimiter to use. If sep is None, will try to automatically determine
    this. Regular expressions are accepted.
engine : {'c', 'python'}
    Parser engine to use. The C engine is faster while the python engine is
    currently more feature-complete."""

_table_params = """sep : string, default \\t (tab-stop)
    Delimiter to use. Regular expressions are accepted.
engine : {'c', 'python'}
    Parser engine to use. The C engine is faster while the python engine is
    currently more feature-complete."""

_read_csv_doc = """
Read CSV (comma-separated) file into DataFrame

%s
""" % (_parser_params % _csv_params)

_read_table_doc = """
Read general delimited file into DataFrame

%s
""" % (_parser_params % _table_params)

_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 _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

    # 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'
            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)
    nrows = kwds.pop('nrows', None)
    chunksize = kwds.get('chunksize', 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' can not be used"
                                  " together yet.")
    elif 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': 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,
    'decimal': b'.',
    'float_precision': None
}

_fwf_defaults = {
    'colspecs': 'infer',
    'widths': None,
}

_c_unsupported = set(['skip_footer'])
_python_unsupported = set(_c_parser_defaults.keys())


def _make_parser_function(name, sep=','):

    default_sep = sep

    def parser_f(filepath_or_buffer,
                 sep=sep,
                 dialect=None,
                 compression='infer',

                 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,
                 true_values=None,
                 false_values=None,
                 delimiter=None,
                 converters=None,
                 dtype=None,
                 usecols=None,

                 engine=None,
                 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,
                 float_precision=None,
                 nrows=None,
                 iterator=False,
                 chunksize=None,

                 verbose=False,
                 encoding=None,
                 squeeze=False,
                 mangle_dupe_cols=True,
                 tupleize_cols=False,
                 infer_datetime_format=False,
                 skip_blank_lines=True):

        # 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

        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)


# 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', '#N/A', 'N/A', 'NA', '#NA',
    'NULL', 'NaN', '-NaN', 'nan', '-nan', ''
])


class TextFileReader(object):
    """

    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

        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 compat.iteritems(_parser_defaults):
            options[argname] = kwds.get(argname, default)

        for argname, default in compat.iteritems(_c_parser_defaults):
            if argname in kwds:
                value = kwds[argname]

                if engine != 'c' and value != default:
                    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['skip_footer'] > 0:
                fallback_reason = "the 'c' engine does not support"\
                                  " skip_footer"
                engine = 'python'

        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"
                engine = 'python'

        if fallback_reason and engine_specified:
            raise ValueError(fallback_reason)

        if engine == 'c':
            for arg in _c_unsupported:
                del result[arg]

        if 'python' in engine:
            for arg in _python_unsupported:
                if fallback_reason and result[arg] != _c_parser_defaults[arg]:
                    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'])

        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)

        if com.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 __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 AbstractMethodError(self)

    def read(self, nrows=None):
        if nrows is not None:
            if self.options.get('skip_footer'):
                raise ValueError('skip_footer 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)

        df = DataFrame(col_dict, columns=columns, index=index)

        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


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 = 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', False)
        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(com.is_integer, self.index_col)) or
                        com.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

    @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)

        # 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 _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 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)
            col_na_values, col_na_fvalues = _get_na_values(c, na_values,
                                                           na_fvalues)
            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)
            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


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, compat.string_types):
                src = open(src, 'rb')
            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 = 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)

        # If the names were inferred (not passed by user) and usedcols is
        # defined, then ensure names refers to the used columns, not the
        # document's columns.
        if self.usecols and passed_names:
            col_indices = []
            for u in self.usecols:
                if isinstance(u, string_types):
                    col_indices.append(self.names.index(u))
                else:
                    col_indices.append(u)
            self.names = [n for i, n in enumerate(self.names)
                          if i in col_indices]
            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 _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)

        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)

    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
                return _get_empty_meta(self.orig_names,
                                       self.index_col,
                                       self.index_names,
                                       dtype=self.kwds.get('dtype'))
            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)

            # 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
    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)>1:
            raise ValueError('Multiple files found in compressed '
                             'zip file %s', str(zip_names))
        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.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.usecols = kwds['usecols']
        self.skip_blank_lines = kwds['skip_blank_lines']

        self.names_passed = kwds['names'] or None

        self.has_index_names = False
        if 'has_index_names' in kwds:
            self.has_index_names = kwds['has_index_names']

        self.verbose = kwds['verbose']
        self.converters = kwds['converters']

        self.thousands = kwds['thousands']
        self.comment = kwds['comment']
        self._comment_lines = []

        if isinstance(f, compat.string_types):
            f = _get_handle(f, 'r', encoding=self.encoding,
                                compression=self.compression)
        elif self.compression:
            f = _wrap_compressed(f, self.compression, self.encoding)
        # 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)

        # 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

    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 com.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)
        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 = next(f)
                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
            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

    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 zip(self.orig_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 _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):
                line = self._buffered_line()

                while self.line_pos <= hr:
                    line = self._next_line()

                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:
            # header is None
            line = self._buffered_line()
            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 _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:
                orig_line = next(self.data)
                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

        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
        nonnum = re.compile('[^-^0-9^%s^.]+' % self.thousands)
        ret = []
        for l in lines:
            rl = []
            for i, x in enumerate(l):
                if (not isinstance(x, compat.string_types) or
                    self.thousands not in x or
                    (self._no_thousands_columns
                        and i in self._no_thousands_columns)
                        or nonnum.search(x.strip())):
                    rl.append(x)
                else:
                    rl.append(x.replace(self.thousands, ''))
            ret.append(rl)
        return ret

    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):
        zipped_content = list(lib.to_object_array(content).T)

        col_len = self.num_original_columns
        zip_len = len(zipped_content)

        if self._implicit_index:
            col_len += len(self.index_col)

        if self.skip_footer < 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:
            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)

        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.skip_footer:
            lines = lines[:-self.skip_footer]

        lines = self._check_comments(lines)
        if self.skip_blank_lines:
            lines = self._check_empty(lines)
        return self._check_thousands(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(
                    com._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 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 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):
                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, 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:
        # Convert column indexes to column names.
        dtype = dict((columns[k] if com.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:
            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 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(object):
    """
    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]

    # 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 = kwds.pop('colspecs')

        PythonParser.__init__(self, f, **kwds)

    def _make_reader(self, f):
        self.data = FixedWidthReader(f, self.colspecs, self.delimiter,
                                     self.comment)