classmethod DataFrame.from_csv(path, header=0, sep=', ', index_col=0, parse_dates=True, encoding=None, tupleize_cols=None, infer_datetime_format=False)
[source]
Read CSV file.
Deprecated since version 0.21.0: 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: |
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: |
|
See also
© 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.DataFrame.from_csv.html