ENH: Add matmul to DataFrame, Series

doc/source/whatsnew/v0.23.0.txt

@@ -403,6 +403,7 @@ Other Enhancements
   ``SQLAlchemy`` dialects supporting multivalue inserts include: ``mysql``, ``postgresql``, ``sqlite`` and any dialect with ``supports_multivalues_insert``. (:issue:`14315`, :issue:`8953`)
 - :func:`read_html` now accepts a ``displayed_only`` keyword argument to controls whether or not hidden elements are parsed (``True`` by default) (:issue:`20027`)
 - zip compression is supported via ``compression=zip`` in :func:`DataFrame.to_pickle`, :func:`Series.to_pickle`, :func:`DataFrame.to_csv`, :func:`Series.to_csv`, :func:`DataFrame.to_json`, :func:`Series.to_json`. (:issue:`17778`)
+- :class:`DataFrame` and :class:`Series` now support matrix multiplication (```@```) operator (:issue:`10259`) for Python>=3.5
 
 .. _whatsnew_0230.api_breaking:
 

pandas/core/frame.py

@@ -863,7 +863,8 @@ def __len__(self):
 
     def dot(self, other):
         """
-        Matrix multiplication with DataFrame or Series objects
+        Matrix multiplication with DataFrame or Series objects.  Can also be
+        called using `self @ other` in Python >= 3.5.
 
         Parameters
         ----------
@@ -905,6 +906,14 @@ def dot(self, other):
         else:  # pragma: no cover
             raise TypeError('unsupported type: %s' % type(other))
 
+    def __matmul__(self, other):
+        """ Matrix multiplication using binary `@` operator in Python>=3.5 """
+        return self.dot(other)
+
+    def __rmatmul__(self, other):
+        """ Matrix multiplication using binary `@` operator in Python>=3.5 """
+        return self.T.dot(np.transpose(other)).T
+
     # ----------------------------------------------------------------------
     # IO methods (to / from other formats)
 

pandas/core/series.py

@@ -1994,7 +1994,7 @@ def autocorr(self, lag=1):
     def dot(self, other):
         """
         Matrix multiplication with DataFrame or inner-product with Series
-        objects
+        objects. Can also be called using `self @ other` in Python >= 3.5.
 
         Parameters
         ----------
@@ -2033,6 +2033,14 @@ def dot(self, other):
         else:  # pragma: no cover
             raise TypeError('unsupported type: %s' % type(other))
 
+    def __matmul__(self, other):
+        """ Matrix multiplication using binary `@` operator in Python>=3.5 """
+        return self.dot(other)
+
+    def __rmatmul__(self, other):
+        """ Matrix multiplication using binary `@` operator in Python>=3.5 """
+        return self.dot(other)
+
     @Substitution(klass='Series')
     @Appender(base._shared_docs['searchsorted'])
     @deprecate_kwarg(old_arg_name='v', new_arg_name='value')

pandas/tests/frame/test_analytics.py

@@ -5,6 +5,7 @@
 import warnings
 from datetime import timedelta
 from distutils.version import LooseVersion
+import operator
 import sys
 import pytest
 
@@ -13,7 +14,7 @@
 from numpy.random import randn
 import numpy as np
 
-from pandas.compat import lrange, product
+from pandas.compat import lrange, product, PY35
 from pandas import (compat, isna, notna, DataFrame, Series,
                     MultiIndex, date_range, Timestamp, Categorical,
                     _np_version_under1p15)
@@ -2091,7 +2092,6 @@ def test_clip_with_na_args(self):
                               self.frame)
 
     # Matrix-like
-
     def test_dot(self):
         a = DataFrame(np.random.randn(3, 4), index=['a', 'b', 'c'],
                       columns=['p', 'q', 'r', 's'])
@@ -2144,6 +2144,63 @@ def test_dot(self):
         with tm.assert_raises_regex(ValueError, 'aligned'):
             df.dot(df2)
 
+    @pytest.mark.skipif(not PY35,
+                        reason='matmul supported for Python>=3.5')
+    def test_matmul(self):
+        # matmul test is for GH #10259
+        a = DataFrame(np.random.randn(3, 4), index=['a', 'b', 'c'],
+                      columns=['p', 'q', 'r', 's'])
+        b = DataFrame(np.random.randn(4, 2), index=['p', 'q', 'r', 's'],
+                      columns=['one', 'two'])
+
+        # DataFrame @ DataFrame
+        result = operator.matmul(a, b)
+        expected = DataFrame(np.dot(a.values, b.values),
+                             index=['a', 'b', 'c'],
+                             columns=['one', 'two'])
+        tm.assert_frame_equal(result, expected)
+
+        # DataFrame @ Series
+        result = operator.matmul(a, b.one)
+        expected = Series(np.dot(a.values, b.one.values),
+                          index=['a', 'b', 'c'])
+        tm.assert_series_equal(result, expected)
+
+        # np.array @ DataFrame
+        result = operator.matmul(a.values, b)
+        expected = np.dot(a.values, b.values)
+        tm.assert_almost_equal(result, expected)
+
+        # nested list @ DataFrame (__rmatmul__)
+        result = operator.matmul(a.values.tolist(), b)
+        expected = DataFrame(np.dot(a.values, b.values),
+                             index=['a', 'b', 'c'],
+                             columns=['one', 'two'])
+        tm.assert_almost_equal(result.values, expected.values)
+
+        # mixed dtype DataFrame @ DataFrame
+        a['q'] = a.q.round().astype(int)
+        result = operator.matmul(a, b)
+        expected = DataFrame(np.dot(a.values, b.values),
+                             index=['a', 'b', 'c'],
+                             columns=['one', 'two'])
+        tm.assert_frame_equal(result, expected)
+
+        # different dtypes DataFrame @ DataFrame
+        a = a.astype(int)
+        result = operator.matmul(a, b)
+        expected = DataFrame(np.dot(a.values, b.values),
+                             index=['a', 'b', 'c'],
+                             columns=['one', 'two'])
+        tm.assert_frame_equal(result, expected)
+
+        # unaligned
+        df = DataFrame(randn(3, 4), index=[1, 2, 3], columns=lrange(4))
+        df2 = DataFrame(randn(5, 3), index=lrange(5), columns=[1, 2, 3])
+
+        with tm.assert_raises_regex(ValueError, 'aligned'):
+            operator.matmul(df, df2)
+
 
 @pytest.fixture
 def df_duplicates():

pandas/tests/series/test_analytics.py

@@ -3,7 +3,7 @@
 
 from itertools import product
 from distutils.version import LooseVersion
-
+import operator
 import pytest
 
 from numpy import nan
@@ -18,7 +18,7 @@
 from pandas.core.indexes.timedeltas import Timedelta
 import pandas.core.nanops as nanops
 
-from pandas.compat import lrange, range
+from pandas.compat import lrange, range, PY35
 from pandas import compat
 from pandas.util.testing import (assert_series_equal, assert_almost_equal,
                                  assert_frame_equal, assert_index_equal)
@@ -921,6 +921,52 @@ def test_dot(self):
         pytest.raises(Exception, a.dot, a.values[:3])
         pytest.raises(ValueError, a.dot, b.T)
 
+    @pytest.mark.skipif(not PY35,
+                        reason='matmul supported for Python>=3.5')
+    def test_matmul(self):
+        # matmul test is for GH #10259
+        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
+
+        # Series @ DataFrame
+        result = operator.matmul(a, b)
+        expected = Series(np.dot(a.values, b.values), index=['1', '2', '3'])
+        assert_series_equal(result, expected)
+
+        # DataFrame @ Series
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values),
+                          index=['1', '2', '3'])
+        assert_series_equal(result, expected)
+
+        # Series @ Series
+        result = operator.matmul(a, a)
+        expected = np.dot(a.values, a.values)
+        assert_almost_equal(result, expected)
+
+        # np.array @ Series (__rmatmul__)
+        result = operator.matmul(a.values, a)
+        expected = np.dot(a.values, a.values)
+        assert_almost_equal(result, expected)
+
+        # mixed dtype DataFrame @ Series
+        a['p'] = int(a.p)
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values),
+                          index=['1', '2', '3'])
+        assert_series_equal(result, expected)
+
+        # different dtypes DataFrame @ Series
+        a = a.astype(int)
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values),
+                          index=['1', '2', '3'])
+        assert_series_equal(result, expected)
+
+        pytest.raises(Exception, a.dot, a.values[:3])
+        pytest.raises(ValueError, a.dot, b.T)
+
     def test_value_counts_nunique(self):
 
         # basics.rst doc example