Merge pull request #11400 from rekcahpassyla/multiindex_setitem

BUG: using .ix with a multi-index indexer

Author: jreback

doc/source/whatsnew/v0.17.1.txt

@@ -71,7 +71,7 @@ Bug Fixes
 - Bug in ``HDFStore.append`` with strings whose encoded length exceded the max unencoded length (:issue:`11234`)
 - Bug in merging ``datetime64[ns, tz]`` dtypes (:issue:`11405`)
 - Bug in ``HDFStore.select`` when comparing with a numpy scalar in a where clause (:issue:`11283`)
-
+- Bug in using ``DataFrame.ix`` with a multi-index indexer(:issue:`11372`) 
 
 
 - Bug in tz-conversions with an ambiguous time and ``.dt`` accessors (:issue:`11295`)

pandas/core/indexing.py

@@ -443,11 +443,14 @@ def can_do_equal_len():
                 # we have an equal len Frame
                 if isinstance(value, ABCDataFrame) and value.ndim > 1:
                     sub_indexer = list(indexer)
+                    multiindex_indexer = isinstance(labels, MultiIndex)
 
                     for item in labels:
                         if item in value:
                             sub_indexer[info_axis] = item
-                            v = self._align_series(tuple(sub_indexer), value[item])
+                            v = self._align_series(
+                                tuple(sub_indexer), value[item], multiindex_indexer
+                            )
                         else:
                             v = np.nan
 
@@ -516,8 +519,28 @@ def can_do_equal_len():
             self.obj._data = self.obj._data.setitem(indexer=indexer, value=value)
             self.obj._maybe_update_cacher(clear=True)
 
-    def _align_series(self, indexer, ser):
-        # indexer to assign Series can be tuple, slice, scalar
+    def _align_series(self, indexer, ser, multiindex_indexer=False):
+        """
+        Parameters
+        ----------
+        indexer : tuple, slice, scalar
+            The indexer used to get the locations that will be set to 
+            `ser`
+
+        ser : pd.Series
+            The values to assign to the locations specified by `indexer`
+
+        multiindex_indexer : boolean, optional
+            Defaults to False. Should be set to True if `indexer` was from
+            a `pd.MultiIndex`, to avoid unnecessary broadcasting. 
+
+
+        Returns:
+        --------
+        `np.array` of `ser` broadcast to the appropriate shape for assignment
+        to the locations selected by `indexer`
+
+        """
         if isinstance(indexer, (slice, np.ndarray, list, Index)):
             indexer = tuple([indexer])
 
@@ -555,7 +578,7 @@ def _align_series(self, indexer, ser):
                 ser = ser.reindex(obj.axes[0][indexer[0]], copy=True)._values
 
                 # single indexer
-                if len(indexer) > 1:
+                if len(indexer) > 1 and not multiindex_indexer:
                     l = len(indexer[1])
                     ser = np.tile(ser, l).reshape(l, -1).T
 

pandas/tests/test_indexing.py

@@ -762,32 +762,95 @@ def compare(result, expected):
         result2 = s.loc[0:3]
         assert_series_equal(result1,result2)
 
-    def test_loc_setitem_multiindex(self):
+    def test_setitem_multiindex(self):
+        for index_fn in ('ix', 'loc'):
+            def check(target, indexers, value, compare_fn, expected=None):
+                fn = getattr(target, index_fn)
+                fn.__setitem__(indexers, value)
+                result = fn.__getitem__(indexers)
+                if expected is None:
+                    expected = value
+                compare_fn(result, expected)
+            # GH7190
+            index = pd.MultiIndex.from_product([np.arange(0,100), np.arange(0, 80)], names=['time', 'firm'])
+            t, n = 0, 2
+            df = DataFrame(np.nan,columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
+            check(
+                target=df, indexers=((t,n), 'X'),
+                value=0, compare_fn=self.assertEqual
+            )
 
-        # GH7190
-        index = pd.MultiIndex.from_product([np.arange(0,100), np.arange(0, 80)], names=['time', 'firm'])
-        t, n = 0, 2
+            df = DataFrame(-999,columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
+            check(
+                target=df, indexers=((t,n), 'X'),
+                value=1, compare_fn=self.assertEqual
+            )
 
-        df = DataFrame(np.nan,columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
-        df.loc[(t,n),'X'] = 0
-        result = df.loc[(t,n),'X']
-        self.assertEqual(result, 0)
+            df = DataFrame(columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
+            check(
+                target=df, indexers=((t,n), 'X'),
+                value=2, compare_fn=self.assertEqual
+            )
 
-        df = DataFrame(-999,columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
-        df.loc[(t,n),'X'] = 1
-        result = df.loc[(t,n),'X']
-        self.assertEqual(result, 1)
+            # GH 7218, assinging with 0-dim arrays
+            df = DataFrame(-999,columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
+            check(
+                target=df, indexers=((t,n), 'X'),
+                value=np.array(3), compare_fn=self.assertEqual,
+                expected=3,
+            )
 
-        df = DataFrame(columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
-        df.loc[(t,n),'X'] = 2
-        result = df.loc[(t,n),'X']
-        self.assertEqual(result, 2)
+            # GH5206
+            df = pd.DataFrame(
+                np.arange(25).reshape(5, 5), columns='A,B,C,D,E'.split(','),
+                dtype=float
+            )
+            df['F'] = 99
+            row_selection = df['A'] % 2 == 0
+            col_selection = ['B', 'C']
+            df.ix[row_selection, col_selection] = df['F']
+            output = pd.DataFrame(99., index=[0, 2, 4], columns=['B', 'C'])
+            assert_frame_equal(df.ix[row_selection, col_selection], output)
+            check(
+                target=df, indexers=(row_selection, col_selection),
+                value=df['F'], compare_fn=assert_frame_equal,
+                expected=output,
+            )
 
-        # GH 7218, assinging with 0-dim arrays
-        df = DataFrame(-999,columns=['A', 'w', 'l', 'a', 'x', 'X', 'd', 'profit'], index=index)
-        df.loc[(t,n), 'X'] = np.array(3)
-        result = df.loc[(t,n),'X']
-        self.assertEqual(result,3)
+            # GH11372
+            idx = pd.MultiIndex.from_product([
+                ['A', 'B', 'C'],
+                pd.date_range('2015-01-01', '2015-04-01', freq='MS')
+            ])
+            cols = pd.MultiIndex.from_product([
+                ['foo', 'bar'],
+                pd.date_range('2016-01-01', '2016-02-01', freq='MS')
+            ])
+            df = pd.DataFrame(np.random.random((12, 4)), index=idx, columns=cols)
+            subidx = pd.MultiIndex.from_tuples(
+                [('A', pd.Timestamp('2015-01-01')), ('A', pd.Timestamp('2015-02-01'))]
+            )
+            subcols = pd.MultiIndex.from_tuples(
+                [('foo', pd.Timestamp('2016-01-01')), ('foo', pd.Timestamp('2016-02-01'))]
+            )
+            vals = pd.DataFrame(np.random.random((2, 2)), index=subidx, columns=subcols)
+            check(
+                target=df, indexers=(subidx, subcols),
+                value=vals, compare_fn=assert_frame_equal,
+            )
+            # set all columns
+            vals = pd.DataFrame(np.random.random((2, 4)), index=subidx, columns=cols)
+            check(
+                target=df, indexers=(subidx, slice(None, None, None)),
+                value=vals, compare_fn=assert_frame_equal,
+            )
+            # identity
+            copy = df.copy()
+            check(
+                target=df, indexers=(df.index, df.columns),
+                value=df, compare_fn=assert_frame_equal,
+                expected=copy
+            )
 
     def test_indexing_with_datetime_tz(self):