dask_expr._collection.DataFrame.rename
dask_expr._collection.DataFrame.rename¶
- DataFrame.rename(index=None, columns=None)[source]¶
Rename columns or index labels.
This docstring was copied from pandas.core.frame.DataFrame.rename.
Some inconsistencies with the Dask version may exist.
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
- mapperdict-like or function (Not supported in Dask)
Dict-like or function transformations to apply to that axis’ values. Use either
mapper
andaxis
to specify the axis to target withmapper
, orindex
andcolumns
.- indexdict-like or function (Not supported in Dask)
Alternative to specifying axis (
mapper, axis=0
is equivalent toindex=mapper
).- columnsdict-like or function
Alternative to specifying axis (
mapper, axis=1
is equivalent tocolumns=mapper
).- axis{0 or ‘index’, 1 or ‘columns’}, default 0 (Not supported in Dask)
Axis to target with
mapper
. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’.- copybool, default True (Not supported in Dask)
Also copy underlying data.
Note
The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write
pd.options.mode.copy_on_write = True
- inplacebool, default False (Not supported in Dask)
Whether to modify the DataFrame rather than creating a new one. If True then value of copy is ignored.
- levelint or level name, default None (Not supported in Dask)
In case of a MultiIndex, only rename labels in the specified level.
- errors{‘ignore’, ‘raise’}, default ‘ignore’ (Not supported in Dask)
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 or None
DataFrame with the renamed axis labels or None if
inplace=True
.
- 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
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