pandas.read_table(filepath_or_buffer, sep='\t', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=False, error_bad_lines=True, warn_bad_lines=True, skip_footer=0, doublequote=True, delim_whitespace=False, as_recarray=False, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, memory_map=False, float_precision=None)
Read general delimited file into DataFrame
Also supports optionally iterating or breaking of the file into chunks.
Additional help can be found in the online docs for IO Tools.
Parameters: |
filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO) The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file ://localhost/path/to/table.csv sep : str, default t (tab-stop) Delimiter to use. If sep is None, will try to automatically determine this. Separators longer than 1 character and different from ‘s+’ will be interpreted as regular expressions, will force use of the python parsing engine and will ignore quotes in the data. Regex example: ‘rt’ delimiter : str, default Alternative argument name for sep. delim_whitespace : boolean, default False Specifies whether or not whitespace (e.g. New in version 0.18.1: support for the Python parser. header : int or list of ints, default ‘infer’ Row number(s) to use as the column names, and the start of the data. Default behavior is as if set to 0 if no names : array-like, default None List of column names to use. If file contains no header row, then you should explicitly pass header=None index_col : int or sequence or False, default None Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of each line, you might consider index_col=False to force pandas to _not_ use the first column as the index (row names) usecols : array-like, default None Return a subset of the columns. All elements in this array must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in squeeze : boolean, default False If the parsed data only contains one column then return a Series prefix : str, default None Prefix to add to column numbers when no header, e.g. ‘X’ for X0, X1, ... mangle_dupe_cols : boolean, default True Duplicate columns will be specified as ‘X.0’...’X.N’, rather than ‘X’...’X’ dtype : Type name or dict of column -> type, default None Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} (Unsupported with engine=’python’). Use engine : {‘c’, ‘python’}, optional Parser engine to use. The C engine is faster while the python engine is currently more feature-complete. converters : dict, default None Dict of functions for converting values in certain columns. Keys can either be integers or column labels true_values : list, default None Values to consider as True false_values : list, default None Values to consider as False skipinitialspace : boolean, default False Skip spaces after delimiter. skiprows : list-like or integer, default None Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file skipfooter : int, default 0 Number of lines at bottom of file to skip (Unsupported with engine=’c’) nrows : int, default None Number of rows of file to read. Useful for reading pieces of large files na_values : str or list-like or dict, default None Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: keep_default_na : bool, default True If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they’re appended to. na_filter : boolean, default True Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file verbose : boolean, default False Indicate number of NA values placed in non-numeric columns skip_blank_lines : boolean, default True If True, skip over blank lines rather than interpreting as NaN values parse_dates : boolean or list of ints or names or list of lists or dict, default False
Note: A fast-path exists for iso8601-formatted dates. infer_datetime_format : boolean, default False If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x. keep_date_col : boolean, default False If True and parse_dates specifies combining multiple columns then keep the original columns. date_parser : function, default None Function to use for converting a sequence of string columns to an array of datetime instances. The default uses dayfirst : boolean, default False DD/MM format dates, international and European format iterator : boolean, default False Return TextFileReader object for iteration or getting chunks with chunksize : int, default None Return TextFileReader object for iteration. See IO Tools docs for more information on compression : {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}, default ‘infer’ For on-the-fly decompression of on-disk data. If ‘infer’, then use gzip, bz2, zip or xz if filepath_or_buffer is a string ending in ‘.gz’, ‘.bz2’, ‘.zip’, or ‘xz’, respectively, and no decompression otherwise. If using ‘zip’, the ZIP file must contain only one data file to be read in. Set to None for no decompression. New in version 0.18.1: support for ‘zip’ and ‘xz’ compression. thousands : str, default None Thousands separator decimal : str, default ‘.’ Character to recognize as decimal point (e.g. use ‘,’ for European data). lineterminator : str (length 1), default None Character to break file into lines. Only valid with C parser. quotechar : str (length 1), optional The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored. quoting : int or csv.QUOTE_* instance, default None Control field quoting behavior per escapechar : str (length 1), default None One-character string used to escape delimiter when quoting is QUOTE_NONE. comment : str, default None Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character. Like empty lines (as long as encoding : str, default None Encoding to use for UTF when reading/writing (ex. ‘utf-8’). List of Python standard encodings dialect : str or csv.Dialect instance, default None If None defaults to Excel dialect. Ignored if sep longer than 1 char See csv.Dialect documentation for more details tupleize_cols : boolean, default False Leave a list of tuples on columns as is (default is to convert to a Multi Index on the columns) error_bad_lines : boolean, default True Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these “bad lines” will dropped from the DataFrame that is returned. (Only valid with C parser) warn_bad_lines : boolean, default True If error_bad_lines is False, and warn_bad_lines is True, a warning for each “bad line” will be output. (Only valid with C parser). |
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Returns: |
result : DataFrame or TextParser |
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© 2008–2014 the pandas development team
Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.18.1/generated/pandas.read_table.html