#NEWLINE# @ classmethod #NEWLINE# def from_csv( cls , path , sep = ',' , parse_dates = True , header = None , index_col = 0 , encoding = None , infer_datetime_format = False ) : #NEWLINE# #INDENT# "\n        Read CSV file (DISCOURAGED, please use :func:`pandas.read_csv`\n        instead).\n\n        It is preferable to use the more powerful :func:`pandas.read_csv`\n        for most general purposes, but ``from_csv`` makes for an easy\n        roundtrip to and from a file (the exact counterpart of\n        ``to_csv``), especially with a time Series.\n\n        This method only differs from :func:`pandas.read_csv` in some defaults:\n\n        - `index_col` is ``0`` instead of ``None`` (take first column as index\n          by default)\n        - `header` is ``None`` instead of ``0`` (the first row is not used as\n          the column names)\n        - `parse_dates` is ``True`` instead of ``False`` (try parsing the index\n          as datetime by default)\n\n        With :func:`pandas.read_csv`, the option ``squeeze=True`` can be used\n        to return a Series like ``from_csv``.\n\n        Parameters\n        ----------\n        path : string file path or file handle / StringIO\n        sep : string, default ','\n            Field delimiter\n        parse_dates : boolean, default True\n            Parse dates. Different default from read_table\n        header : int, default None\n            Row to use as header (skip prior rows)\n        index_col : int or sequence, default 0\n            Column to use for index. If a sequence is given, a MultiIndex\n            is used. Different default from read_table\n        encoding : string, optional\n            a string representing the encoding to use if the contents are\n            non-ascii, for python versions prior to 3\n        infer_datetime_format: boolean, default False\n            If True and `parse_dates` is True for a column, try to infer the\n            datetime format based on the first datetime string. If the format\n            can be inferred, there often will be a large parsing speed-up.\n\n        See also\n        --------\n        pandas.read_csv\n\n        Returns\n        -------\n        y : Series\n        " #NEWLINE# from pandas.core.frame import DataFrame #NEWLINE# df = DataFrame . from_csv ( path , header = header , index_col = index_col , sep = sep , parse_dates = parse_dates , encoding = encoding , infer_datetime_format = infer_datetime_format ) #NEWLINE# result = df . iloc [ : , 0 ] #NEWLINE# if ( header is None ) : #NEWLINE# #INDENT# result . index . name = result . name = None #NEWLINE# #UNINDENT# return result