Classe « DataFrame »

Méthode pandas.DataFrame.dot

Signature de la méthode dot

def dot(self, other) 

Description

dot.__doc__

        Compute the matrix multiplication between the DataFrame and other.

        This method computes the matrix product between the DataFrame and the
        values of an other Series, DataFrame or a numpy array.

        It can also be called using ``self @ other`` in Python >= 3.5.

        Parameters
        ----------
        other : Series, DataFrame or array-like
            The other object to compute the matrix product with.

        Returns
        -------
        Series or DataFrame
            If other is a Series, return the matrix product between self and
            other as a Series. If other is a DataFrame or a numpy.array, return
            the matrix product of self and other in a DataFrame of a np.array.

        See Also
        --------
        Series.dot: Similar method for Series.

        Notes
        -----
        The dimensions of DataFrame and other must be compatible in order to
        compute the matrix multiplication. In addition, the column names of
        DataFrame and the index of other must contain the same values, as they
        will be aligned prior to the multiplication.

        The dot method for Series computes the inner product, instead of the
        matrix product here.

        Examples
        --------
        Here we multiply a DataFrame with a Series.

        >>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
        >>> s = pd.Series([1, 1, 2, 1])
        >>> df.dot(s)
        0    -4
        1     5
        dtype: int64

        Here we multiply a DataFrame with another DataFrame.

        >>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
        >>> df.dot(other)
            0   1
        0   1   4
        1   2   2

        Note that the dot method give the same result as @

        >>> df @ other
            0   1
        0   1   4
        1   2   2

        The dot method works also if other is an np.array.

        >>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
        >>> df.dot(arr)
            0   1
        0   1   4
        1   2   2

        Note how shuffling of the objects does not change the result.

        >>> s2 = s.reindex([1, 0, 2, 3])
        >>> df.dot(s2)
        0    -4
        1     5
        dtype: int64