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, 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=b'.', lineterminator=None, quotechar='"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, doublequote=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)
[source]
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: |
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, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used and automatically detect the separator by Python’s builtin sniffer tool, 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 to infer the column names: if no names are passed the behavior is identical to names : array-like, default None List of column names to use. If file contains no header row, then you should explicitly pass header=None. Duplicates in this list will cause a 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 : list-like or callable, default None Return a subset of the columns. If list-like, all elements 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 If callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True. An example of a valid callable argument would be 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’, ‘X.1’, …’X.N’, rather than ‘X’…’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns. dtype : Type name or dict of column -> type, default None Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} 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 or callable, default None Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file. If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be 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 : scalar, str, list-like, or dict, default None Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’. keep_default_na : bool, default True Whether or not to include the default NaN values when parsing the data. Depending on whether
Note that if 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
If a column or index contains an unparseable date, the entire column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use Note: A fast-path exists for iso8601-formatted dates. infer_datetime_format : boolean, default False If True and keep_date_col : boolean, default False If True and 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 the 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’ and 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). float_precision : string, default None Specifies which converter the C engine should use for floating-point values. The options are lineterminator : str (length 1), default None Character to break file into lines. Only valid with C parser. quotechar : str (length 1), optional The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored. quoting : int or csv.QUOTE_* instance, default 0 Control field quoting behavior per doublequote : boolean, default When quotechar is specified and quoting is not 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 provided, this parameter will override values (default or not) for the following parameters: tupleize_cols : boolean, default False Deprecated since version 0.21.0: This argument will be removed and will always convert to MultiIndex Leave a list of tuples on columns as is (default is to convert to a MultiIndex 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. 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. low_memory : boolean, default True Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. To ensure no mixed types either set False, or specify the type with the memory_map : boolean, default False If a filepath is provided for |
---|---|
Returns: |
|
© 2008–2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.read_table.html