# pandas.Series.from_csv

classmethod `Series.``from_csv`(path, sep=', ', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False)

  Read CSV file (DEPRECATED, 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.

  Returns: | y : Series

  See also

  `pandas.read_csv`

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