ENH: Bring Series.dot up to par with DataFrame.dot

pandas/core/series.py

@@ -1944,6 +1944,47 @@ def clip_lower(self, threshold):
         """
         return pa.where(self < threshold, threshold, self)
 
+    def dot(self, other):
+        """
+        Matrix multiplication with DataFrame or inner-product with Series objects
+
+        Parameters
+        ----------
+        other : Series or DataFrame 
+
+        Returns
+        -------
+        dot_product : scalar or Series
+        """
+        from pandas.core.frame import DataFrame
+        if isinstance(other, (Series, DataFrame)):
+            common = self.index.union(other.index)
+            if (len(common) > len(self.index) or
+                    len(common) > len(other.index)):
+                raise ValueError('matrices are not aligned')
+
+            left = self.reindex(index=common, copy=False)
+            right = other.reindex(index=common, copy=False)
+            lvals = left.values
+            rvals = right.values
+        else:
+            left = self
+            lvals = self.values
+            rvals = np.asarray(other)
+            if lvals.shape[0] != rvals.shape[0]:
+                raise Exception('Dot product shape mismatch, %s vs %s' %
+                                (lvals.shape, rvals.shape))
+
+        if isinstance(other, DataFrame):
+            return self._constructor(np.dot(lvals, rvals),
+                                     index=other.columns)
+        elif isinstance(other, Series):
+            return np.dot(lvals, rvals)
+        elif isinstance(rvals, np.ndarray):
+            return np.dot(lvals, rvals)
+        else:  # pragma: no cover
+            raise TypeError('unsupported type: %s' % type(other))
+
 #------------------------------------------------------------------------------
 # Combination
 

pandas/tests/test_series.py

@@ -2486,6 +2486,33 @@ def test_count(self):
 
         self.assertEqual(self.ts.count(), np.isfinite(self.ts).sum())
 
+    def test_dot(self):
+        a = Series(np.random.randn(4), index=['p', 'q', 'r', 's'])
+        b = DataFrame(np.random.randn(3, 4), index=['1', '2', '3'],
+                      columns=['p', 'q', 'r', 's']).T
+
+        result = a.dot(b)
+        expected = Series(np.dot(a.values, b.values),
+                             index=['1', '2', '3'])
+        assert_series_equal(result, expected)
+
+        #Check index alignment
+        b2 = b.reindex(index=reversed(b.index))
+        result = a.dot(b)
+        assert_series_equal(result, expected)
+
+        # Check ndarray argument
+        result = a.dot(b.values)
+        self.assertTrue(np.all(result == expected.values))
+        self.assertEquals(a.dot(b['2'].values), expected['2'])
+
+        #Check series argument
+        self.assertEquals(a.dot(b['1']), expected['1'])
+        self.assertEquals(a.dot(b2['1']), expected['1'])
+
+        self.assertRaises(Exception, a.dot, a.values[:3])
+        self.assertRaises(ValueError, a.dot, b.T)
+
     def test_value_counts_nunique(self):
         s = Series(['a', 'b', 'b', 'b', 'b', 'a', 'c', 'd', 'd', 'a'])
         hist = s.value_counts()