Series.rename()

Series.rename(index=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:

Parameters:	index : 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 Series. If True then value of copy is ignored.
Returns:	renamed : Series (new object)

index : 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 Series. If True then value of copy is ignored.

Returns:

renamed : Series (new object)

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

Links:

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.Series.rename.html

doc_Pandas

2025-01-10 15:47:30

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