TextParser(*args, **kwds)

Parameters

data : file-like object or list

delimiter : separator character to use

dialect : str or csv.Dialect instance, optional

Ignored if delimiter is longer than 1 character

names : sequence, default

header : int, default 0

Row to use to parse column labels. Defaults to the first row. Prior rows will be discarded

index_col : int or list, optional

Column or columns to use as the (possibly hierarchical) index

has_index_names: bool, default False :

True if the cols defined in index_col have an index name and are not in the header.

na_values : scalar, str, list-like, or dict, optional

Additional strings to recognize as NA/NaN.

keep_default_na : bool, default True

thousands : str, optional

Thousands separator

comment : str, optional

Comment out remainder of line

parse_dates : bool, default False

keep_date_col : bool, default False

date_parser : function, optional

skiprows : list of integers

Row numbers to skip

skipfooter : int

Number of line at bottom of file to skip

converters : dict, optional

Dict of functions for converting values in certain columns. Keys can either be integers or column labels, values are functions that take one input argument, the cell (not column) content, and return the transformed content.

encoding : str, optional

Encoding to use for UTF when reading/writing (ex. 'utf-8')

squeeze : bool, default False

returns Series if only one column.

infer_datetime_format: bool, default False :

If True and :None:None:`parse_dates` is True for a column, try to infer the datetime format based on the first datetime string. If the format can be inferred, there often will be a large parsing speed-up.

float_precision : str, optional

Specifies which converter the C engine should use for floating-point values. The options are :None:None:`None` or :None:None:`high` for the ordinary converter, legacy for the original lower precision pandas converter, and :None:None:`round_trip` for the round-trip converter.

versionchanged

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

Examples

See :

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File: /pandas/io/parsers/readers.py#1293
type: <class 'function'>
Commit: