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classmethod DataFrame.from_csv(path, header=0, sep=', ', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False)
[source] -
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, butfrom_csv
makes for an easy roundtrip to and from a file (the exact counterpart ofto_csv
), especially with a DataFrame of time series data.This method only differs from the preferred
pandas.read_csv()
in some defaults:-
index_col
is0
instead ofNone
(take first column as index by default) -
parse_dates
isTrue
instead ofFalse
(try parsing the index as datetime by default)
So a
pd.DataFrame.from_csv(path)
can be replaced bypd.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
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.Returns: y : DataFrame
See also
-
DataFrame.from_csv()
2017-01-12 04:45:51
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