classmethod DataFrame.from_csv(path, header=0, sep=', ', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False)
Read CSV file (DISCOURAGED, please use pandas.read_csv()
instead).
It is preferable to use the more powerful pandas.read_csv()
for most general purposes, but from_csv
makes for an easy roundtrip to and from a file (the exact counterpart of to_csv
), especially with a DataFrame of time series data.
This method only differs from the preferred pandas.read_csv()
in some defaults:
index_col
is 0
instead of None
(take first column as index by default)parse_dates
is True
instead of False
(try parsing the index as datetime by default)So a pd.DataFrame.from_csv(path)
can be replaced by pd.read_csv(path, index_col=0, parse_dates=True)
.
Parameters: |
path : string file path or file handle / StringIO header : int, default 0 Row to use as header (skip prior rows) sep : string, default ‘,’ Field delimiter index_col : int or sequence, default 0 Column to use for index. If a sequence is given, a MultiIndex is used. Different default from read_table parse_dates : boolean, default True Parse dates. Different default from read_table tupleize_cols : boolean, default False write multi_index columns as a list of tuples (if True) or new (expanded format) if False) infer_datetime_format: boolean, default False If True and |
---|---|
Returns: |
y : DataFrame |
See also
© 2011–2012 Lambda Foundry, Inc. and PyData Development Team
© 2008–2011 AQR Capital Management, LLC
© 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.DataFrame.from_csv.html