pandas.Panel.rename

Panel.rename(items=None, major_axis=None, minor_axis=None, **kwargs) [source]

Alter axes input function or functions. 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. Alternatively, change Series.name with a scalar value (Series only).

Parameters:

items, major_axis, minor_axis : scalar, list-like, dict-like or function, optional Scalar or list-like will alter the Series.name attribute, and raise on DataFrame or Panel. dict-like or functions are transformations to apply to that axis’ values copy : boolean, default True Also copy underlying data inplace : boolean, default False Whether to return a new Panel. 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.

Returns:

renamed : Panel (new object)

See also

pandas.NDFrame.rename_axis

Examples

>>> s = pd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: int64
>>> s.rename("my_name") # scalar, changes Series.name
0    1
1    2
2    3
Name: my_name, dtype: int64
>>> s.rename(lambda x: x ** 2)  # function, changes labels
0    1
1    2
4    3
dtype: int64
>>> s.rename({1: 3, 2: 5})  # mapping, changes labels
0    1
3    2
5    3
dtype: int64

Since DataFrame doesn’t have a .name attribute, only mapping-type arguments are allowed.

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(2)
Traceback (most recent call last):
...
TypeError: 'int' object is not callable

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.

>>> df.rename(index=str, columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

>>> df.rename(index=str, columns={"A": "a", "C": "c"})
   a  B
0  1  4
1  2  5
2  3  6

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

See the user guide for more.