Classe « DataFrame »

Méthode pandas.DataFrame.applymap

Signature de la méthode applymap

def applymap(self, func, na_action: 'Optional[str]' = None) -> 'DataFrame' 

Description

applymap.__doc__

        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.

            .. versionadded:: 1.2

        Returns
        -------
        DataFrame
            Transformed 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  <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.

        >>> 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