# pandas.Series.from_csv¶

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, but from_csv makes for an easy roundtrip to and from a file (the exact counterpart of to_csv), especially with a time Series.

This method only differs from pandas.read_csv() in some defaults:

• index_col is 0 instead of None (take first column as index by default)
• header is None instead of 0 (the first row is not used as the column names)
• parse_dates is True instead of False (try parsing the index as datetime by default)

With pandas.read_csv(), the option squeeze=True can be used to return a Series like from_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. y : Series
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