test for 8669

doc/source/whatsnew/v0.23.0.txt

@@ -407,7 +407,7 @@ Other Enhancements
 - Updated ``to_gbq`` and ``read_gbq`` signature and documentation to reflect changes from
   the Pandas-GBQ library version 0.4.0. Adds intersphinx mapping to Pandas-GBQ
   library. (:issue:`20564`)
-  
+
 .. _whatsnew_0230.api_breaking:
 
 Backwards incompatible API changes
@@ -487,7 +487,7 @@ If you wish to retain the old behavior while using Python >= 3.6, you can use
 Categorical Grouping no longer expands to all possible groupers
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-In previous versions, grouping by 1 or more categorical columns would result in an index that was the cartesian product of all of the categories for each grouper, not just the observed values. This is inconsistent with output for other dtypes, can potentially cast to different dtypes (as missing values are introduced), and could cause a huge frame to be generated. Pandas will now return only the observed values, regardless if grouping on a categorical column; note that the categorical dtype is *still* preserved. You will still have a categorical columns (:issue:`14942`). This reverts issues (:issue:`10132`, :issue:`8138`, :issue:`14942`, :issue:`15217`, :issue:`17594`)
+In previous versions, grouping by 1 or more categorical columns would result in an index that was the cartesian product of all of the categories for each grouper, not just the observed values. This is inconsistent with output for other dtypes, can potentially cast to different dtypes (as missing values are introduced), and could cause a huge frame to be generated. Pandas will now return only the observed values, regardless if grouping on a categorical column; note that the categorical dtype is *still* preserved. You will still have a categorical columns (:issue:`14942`, :issue:`8138`, :issue:`15217`, :issue:`17594`, :issue:`8669`)
 
 
 

pandas/tests/groupby/test_categorical.py

@@ -226,6 +226,7 @@ def test_with_multiple_groupers_no_expand():
     # gh-14942 (implement)
     # gh-10132 (revert)
     # gh-8138 (revert)
+    # gh-8869
 
     cat1 = Categorical(["a", "a", "b", "b"],
                        categories=["a", "b", "z"], ordered=True)
@@ -316,6 +317,20 @@ def test_with_multiple_groupers_no_expand():
                      "C3": [10, 100, 200, 34]}, index=idx)
     tm.assert_frame_equal(res, exp)
 
+    # gh-8869
+    # with as_index
+    d = {'foo': [10, 8, 4, 8, 4, 1, 1], 'bar': [10, 20, 30, 40, 50, 60, 70],
+         'baz': ['d', 'c', 'e', 'a', 'a', 'd', 'c']}
+    df = pd.DataFrame(d)
+    cat = pd.cut(df['foo'], np.linspace(0, 10, 3))
+    df['range'] = cat
+    groups = df.groupby(['range', 'baz'], as_index=False)
+    result = groups.agg('mean')
+
+    groups2 = df.groupby(['range', 'baz'], as_index=True)
+    expected = groups2.agg('mean').reset_index()
+    tm.assert_frame_equal(result, expected)
+
 
 def test_datetime():
     # GH9049: ensure backward compatibility