xorbits.pandas.DataFrame.applymap#

DataFrame.applymap(func: PythonFuncType, na_action: str | None = None, **kwargs) → DataFrame[source]#

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters

  • func (callable) – Python function, returns a single value from a single value.

  • na_action ({None, 'ignore'}, default None) –

    If ‘ignore’, propagate NaN values, without passing them to func.

    New in version 1.2.

  • **kwargs –

    Additional keyword arguments to pass as keywords arguments to func.

    New in version 1.3.0.

Returns

Transformed DataFrame.

Return type

DataFrame

See also

DataFrame.apply

Apply a function along input axis of DataFrame.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])  
>>> df  
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.applymap(lambda x: len(str(x)))  
   0  1
0  3  4
1  5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()  
>>> df_copy.iloc[0, 0] = pd.NA  
>>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')  
     0  1
0  NaN  4
1  5.0  5

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.applymap(lambda x: x**2)  
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it’s better to avoid applymap in that case.

>>> df ** 2  
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

This docstring was copied from pandas.core.frame.DataFrame.