DOC: Upgraded Docstring pandas.DataFrame.dot

pandas/core/frame.py

@@ -1046,16 +1046,71 @@ def __len__(self):
 
     def dot(self, other):
         """
-        Matrix multiplication with DataFrame or Series objects. Can also be
-        called using `self @ other` in Python >= 3.5.
+        Compute the matrix mutiplication 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 : DataFrame or Series
+        other : Series, DataFrame or array-like
+            The other object to compute the matrix product with.
 
         Returns
         -------
-        dot_product : DataFrame or Series
+        Series or DataFrame
+            If other is a Series, return the matrix product between self and
+            other as a Serie. 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: Compute the inner product of a Series with another Series
+            or the columns of a DataFrame or the columns of a numpy array.
+
+        Notes
+        -----
+        The dimensions of DataFrame and other must be compatible in order to
+        compute the matrix 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
         """
         if isinstance(other, (Series, DataFrame)):
             common = self.columns.union(other.index)