applymap(self, func: 'PythonFuncType', na_action: 'str | None' = None, **kwargs) -> 'DataFrame'

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.

versionadded

**kwargs :

Additional keyword arguments to pass as keywords arguments to func .

versionadded

Returns

DataFrame

Transformed DataFrame.

Apply a function to a Dataframe elementwise.

See Also

DataFrame.apply

Apply a function along input axis of DataFrame.

Examples

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

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

Like Series.map, NA values can be ignored:

This example is valid syntax, but we were not able to check execution

>>> 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  <NA>  4
1     5  5

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

This example is valid syntax, but we were not able to check execution

>>> 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.

This example is valid syntax, but we were not able to check execution

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.frame.DataFrame.apply pandas.core.series.Series.map pandas.core.generic.NDFrame.pipe

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