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: |
index : scalar, list-like, dict-like or function, optional Scalar or list-like will alter the 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) |
See also
pandas.NDFrame.rename_axis
>>> 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
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http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.Series.rename.html