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DataFrame.rename(index=None, columns=None, **kwargs)
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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, columns : 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? valuescopy : boolean, default True
Also copy underlying data
inplace : boolean, default False
Whether to return a new DataFrame. If True then value of copy is ignored.
Returns: renamed : DataFrame (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 >>> 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
DataFrame.rename()
2017-01-12 04:46:24
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