pandas.DataFrame.rename — pandas 0.25.3 documentation (original) (raw)

DataFrame. rename(self, mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore')[source]¶

Alter axes labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.

See the user guide for more.

Parameters:	mapper : dict-like or function Dict-like or functions transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index andcolumns. index : dict-like or function Alternative to specifying axis (mapper, axis=0is equivalent to index=mapper). columns : dict-like or function Alternative to specifying axis (mapper, axis=1is equivalent to columns=mapper). axis : int or str Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’. copy : bool, default True Also copy underlying data. inplace : bool, default False Whether to return a new DataFrame. If True then value of copy is ignored. level : int or level name, default None In case of a MultiIndex, only rename labels in the specified level. errors : {‘ignore’, ‘raise’}, default ‘ignore’ If ‘raise’, raise a KeyError when a dict-like mapper, index, or columns contains labels that are not present in the Index being transformed. If ‘ignore’, existing keys will be renamed and extra keys will be ignored.
Returns:	DataFrame DataFrame with the renamed axis labels.
Raises:	KeyError If any of the labels is not found in the selected axis and “errors=’raise’”.

Parameters:

mapper : dict-like or function Dict-like or functions transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index andcolumns. index : dict-like or function Alternative to specifying axis (mapper, axis=0is equivalent to index=mapper). columns : dict-like or function Alternative to specifying axis (mapper, axis=1is equivalent to columns=mapper). axis : int or str Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’. copy : bool, default True Also copy underlying data. inplace : bool, default False Whether to return a new DataFrame. If True then value of copy is ignored. level : int or level name, default None In case of a MultiIndex, only rename labels in the specified level. errors : {‘ignore’, ‘raise’}, default ‘ignore’ If ‘raise’, raise a KeyError when a dict-like mapper, index, or columns contains labels that are not present in the Index being transformed. If ‘ignore’, existing keys will be renamed and extra keys will be ignored.

Returns:

DataFrame DataFrame with the renamed axis labels.

Raises:

KeyError If any of the labels is not found in the selected axis and “errors=’raise’”.

Examples

DataFrame.rename supports two calling conventions

(index=index_mapper, columns=columns_mapper, ...)
(mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Rename columns using a mapping:

df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) df.rename(columns={"A": "a", "B": "c"}) a c 0 1 4 1 2 5 2 3 6

Rename index using a mapping:

df.rename(index={0: "x", 1: "y", 2: "z"}) A B x 1 4 y 2 5 z 3 6

Cast index labels to a different type:

df.index RangeIndex(start=0, stop=3, step=1) df.rename(index=str).index Index(['0', '1', '2'], dtype='object')

df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise") Traceback (most recent call last): KeyError: ['C'] not found in axis

Using axis-style parameters

df.rename(str.lower, axis='columns') a b 0 1 4 1 2 5 2 3 6

df.rename({1: 2, 2: 4}, axis='index') A B 0 1 4 2 2 5 4 3 6