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# pandas.DataFrame.from_csv

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

  Read delimited file into DataFrame

  Parameters : | path : string file path or file handle / StringIO header : int, default 0 Row to use at 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

  Notes

  Preferable to use read_table for most general purposes but from_csv makes for an easy roundtrip to and from file, especially with a DataFrame of time series data

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