Classe « DataFrame »

Méthode pandas.DataFrame.assign

Signature de la méthode assign

def assign(self, **kwargs) -> 'DataFrame' 

Description

assign.__doc__

        Assign new columns to a DataFrame.

        Returns a new object with all original columns in addition to new ones.
        Existing columns that are re-assigned will be overwritten.

        Parameters
        ----------
        **kwargs : dict of {str: callable or Series}
            The column names are keywords. If the values are
            callable, they are computed on the DataFrame and
            assigned to the new columns. The callable must not
            change input DataFrame (though pandas doesn't check it).
            If the values are not callable, (e.g. a Series, scalar, or array),
            they are simply assigned.

        Returns
        -------
        DataFrame
            A new DataFrame with the new columns in addition to
            all the existing columns.

        Notes
        -----
        Assigning multiple columns within the same ``assign`` is possible.
        Later items in '\*\*kwargs' may refer to newly created or modified
        columns in 'df'; items are computed and assigned into 'df' in order.

        Examples
        --------
        >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
        ...                   index=['Portland', 'Berkeley'])
        >>> df
                  temp_c
        Portland    17.0
        Berkeley    25.0

        Where the value is a callable, evaluated on `df`:

        >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
                  temp_c  temp_f
        Portland    17.0    62.6
        Berkeley    25.0    77.0

        Alternatively, the same behavior can be achieved by directly
        referencing an existing Series or sequence:

        >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
                  temp_c  temp_f
        Portland    17.0    62.6
        Berkeley    25.0    77.0

        You can create multiple columns within the same assign where one
        of the columns depends on another one defined within the same assign:

        >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
        ...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
                  temp_c  temp_f  temp_k
        Portland    17.0    62.6  290.15
        Berkeley    25.0    77.0  298.15