modin.pandas.DataFrame.rename

DataFrame.rename(mapper: Renamer | None = None, *, index: Renamer | None = None, columns: Renamer | None = None, axis: Axis | None = None, copy: bool | None = None, inplace: bool = False, level: Level | None = None, errors: IgnoreRaise = 'ignore') → DataFrame | None[source]

Rename columns or index 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.

Parameters:

  • mapper (dict-like or function) – Dict-like or function transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.

  • index (dict-like or function) – Alternative to specifying axis (mapper, axis=0 is equivalent to index=mapper).

  • columns (dict-like or function) – Alternative to specifying axis (mapper, axis=1 is equivalent to columns=mapper).

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – 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. copy has been ignored with Snowflake execution engine.

  • inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one. 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 with the renamed axis labels or None if inplace=True.

Return type:

DataFrame or None

Raises:

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

See also

DataFrame.rename_axis

Set the name of the axis.

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
Copy

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
Copy

Cast index labels to a different type:

>>> df.index
Index([0, 1, 2], dtype='int64')
Copy
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
  ...
KeyError: "['C'] not found in axis"
Copy

Using axis-style parameters:

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6
Copy
>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6
Copy
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