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classmethod Series.from_csv(path, sep=', ', parse_dates=True, header=None, index_col=0, encoding=None, 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 time Series.This method only differs from
pandas.read_csv()
in some defaults:-
index_col
is0
instead ofNone
(take first column as index by default) -
header
isNone
instead of0
(the first row is not used as the column names) -
parse_dates
isTrue
instead ofFalse
(try parsing the index as datetime by default)
With
pandas.read_csv()
, the optionsqueeze=True
can be used to return a Series likefrom_csv
.Parameters: path : string file path or file handle / StringIO
sep : string, default ?,?
Field delimiter
parse_dates : boolean, default True
Parse dates. Different default from read_table
header : int, default None
Row to use as header (skip prior rows)
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
encoding : string, optional
a string representing the encoding to use if the contents are non-ascii, for python versions prior to 3
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 : Series
See also
-
Series.from_csv()
2017-01-12 04:53:58
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