918206

Using this and not upstream's __array_priority__ fix
pandas-dev/pandas@ad2a14f
to allow np.array += DataFrame to remain in-place (same object ID /
other views also affected) and an array (not a DataFrame).

Author: jbrockmendel, Rebecca N. Palmer <[email protected]>
Origin: upstream 
Bug-Debian: https://bugs.debian.org/918206 https://bugs.debian.org/923707
Forwarded: not-needed


Gbp-Pq: Name 918206.patch

Author: Debian Science Team

pandas/core/generic.py

@@ -1607,6 +1607,8 @@ def __array__(self, dtype=None):
 
     def __array_wrap__(self, result, context=None):
         d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False)
+        if context is not None and context[0]==np.matmul and not hasattr(context[1][0],'index'):
+            d.pop('index',None)
         return self._constructor(result, **d).__finalize__(self)
 
     # ideally we would define this to avoid the getattr checks, but

pandas/tests/frame/test_analytics.py

@@ -2283,8 +2283,11 @@ def test_matmul(self):
 
         # np.array @ DataFrame
         result = operator.matmul(a.values, b)
+        assert isinstance(result, DataFrame)
+        assert result.columns.equals(b.columns)
+        assert result.index.equals(pd.Index(range(3)))
         expected = np.dot(a.values, b.values)
-        tm.assert_almost_equal(result, expected)
+        tm.assert_almost_equal(result.values, expected)
 
         # nested list @ DataFrame (__rmatmul__)
         result = operator.matmul(a.values.tolist(), b)