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 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 DataFrame of time series data.
This method only differs from the preferred pandas.read_csv() in some defaults:
- index_col is 0 instead of None (take first column as index by default)
- parse_dates is True instead of False (try parsing the index as datetime by default)
So a pd.DataFrame.from_csv(path) can be replaced by pd.read_csv(path, index_col=0, parse_dates=True).
Parameters: path : string file path or file handle / StringIO
header : int, default 0
Row to use as 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
See also