# pandas.DataFrame.from_csv¶

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

Deprecated since version 0.21.0: 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. y : DataFrame
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