fix a indices bug for categorical-datetime columns

doc/source/whatsnew/v0.25.2.rst

@@ -99,7 +99,7 @@ Other
 ^^^^^
 
 - Compatibility with Python 3.8 in :meth:`DataFrame.query` (:issue:`27261`)
--
+- Bug in :func:`get_indexer_dict` when passed keys are not numpy array. (:issue:`26860`)
 
 .. _whatsnew_0.252.contributors:
 

pandas/core/sorting.py

@@ -305,6 +305,8 @@ def get_flattened_iterator(comp_ids, ngroups, levels, labels):
 
 def get_indexer_dict(label_list, keys):
     """ return a diction of {labels} -> {indexers} """
+    # address GH 26860
+    keys = [np.asarray(key) for key in keys]
     shape = list(map(len, keys))
 
     group_index = get_group_index(label_list, shape, sort=True, xnull=True)

pandas/tests/groupby/test_groupby.py

@@ -351,6 +351,100 @@ def f3(x):
         df2.groupby("a").apply(f3)
 
 
+def _all_combinations(elems):
+    from itertools import chain, combinations
+
+    out = chain.from_iterable(
+        combinations(elems, n + 1) for n in range(len(elems))
+    )
+    return list(out)
+
+
+@pytest.mark.parametrize(
+    'gb_cols', _all_combinations([
+        'int_series', 'int_series_cat', 'float_series', 'float_series_cat',
+        'dt_series', 'dt_series_cat', 'period_series', 'period_series_cat'
+    ]),
+    ids=lambda cols: ",".join(cols)
+)
+def test_groupby_indices(gb_cols):
+    # GH 26860
+    # Test if DataFrame Groupby builds gb.indices correctly.
+
+    gb_cols = list(gb_cols)
+
+    int_series = pd.Series([1, 2, 3])
+    dt_series = pd.to_datetime(['2018Q1', '2018Q2', '2018Q3'])
+    df = pd.DataFrame(
+        data={
+            'int_series': int_series,
+            'int_series_cat': int_series.astype('category'),
+            'float_series': int_series.astype('float'),
+            'float_series_cat': int_series.astype('float').astype('category'),
+            'dt_series': dt_series,
+            'dt_series_cat': dt_series.astype('category'),
+            'period_series': dt_series.to_period('Q'),
+            'period_series_cat': dt_series.to_period('Q').astype('category')
+        },
+        columns=[
+            'int_series',
+            'int_series_cat',
+            'float_series',
+            'float_series_cat',
+            'dt_series',
+            'dt_series_cat',
+            'period_series',
+            'period_series_cat'
+        ]
+    )
+
+    num_gb_cols = len(gb_cols)
+
+    if num_gb_cols == 1:
+        s = df[gb_cols[0]]
+        col_vals = list(s.unique())
+
+        if pd.api.types.is_datetime64_any_dtype(s):
+            col_vals = list(map(pd.Timestamp, col_vals))
+
+        target = {
+            key: np.array([i])
+            for i, key in enumerate(col_vals)
+        }
+    else:
+        col_vals = {
+            col: list(df[col].unique())
+            for col in gb_cols
+        }
+
+        def to_dt(elems):
+            elems = map(pd.Timestamp, elems)
+            elems = map(lambda dt: dt.to_datetime64(), elems)
+            elems = list(elems)
+            return elems
+
+        for col in gb_cols:
+            if pd.api.types.is_datetime64_any_dtype(df[col]):
+                col_vals[col] = to_dt(col_vals[col])
+
+            elif pd.api.types.is_categorical_dtype(df[col]):
+                if pd.api.types.is_datetime64_any_dtype(df[col].cat.categories):
+                    col_vals[col] = to_dt(col_vals[col])
+
+        it = zip(*(col_vals[col] for col in col_vals.keys()))
+        target = {
+            key: np.array([i])
+            for i, key in enumerate(it)
+        }
+
+    indices = df.groupby(gb_cols).indices
+
+    assert set(target.keys()) == set(indices.keys())
+    for key in target.keys():
+        assert pd.core.dtypes.missing.array_equivalent(
+            target[key], indices[key])
+
+
 def test_attr_wrapper(ts):
     grouped = ts.groupby(lambda x: x.weekday())