BUG: fix reindexing MultiIndex with categorical datetime-like level

doc/source/whatsnew/v0.23.2.txt

@@ -54,7 +54,8 @@ Fixed Regressions
 
 - Fixed regression in :meth:`to_csv` when handling file-like object incorrectly (:issue:`21471`)
 - Bug in both :meth:`DataFrame.first_valid_index` and :meth:`Series.first_valid_index` raised for a row index having duplicate values (:issue:`21441`)
--
+- Fixed regression in :meth:`~DataFrame.reindex` and :meth:`~DataFrame.groupby`
+ with a MultiIndex or multiple keys that contains categorical datetime-like values (:issue:`21390`).
 
 .. _whatsnew_0232.performance:
 

pandas/core/indexes/multi.py

@@ -819,11 +819,17 @@ def values(self):
  return self._tuples
 
  values = []
- for lev, lab in zip(self.levels, self.labels):
+ for i, (lev, lab) in enumerate(zip(self.levels, self.labels)):
  # Need to box timestamps, etc.
  box = hasattr(lev, '_box_values')
+ if is_categorical_dtype(lev):
+ # TODO GH-21390
+ taken = self.get_level_values(i)._values.get_values()
+ if not isinstance(taken, np.ndarray):
+ # Datetime/Period index
+ taken = np.array(taken.astype(object))
  # Try to minimize boxing.
- if box and len(lev) > len(lab):
+ elif box and len(lev) > len(lab):
  taken = lev._box_values(algos.take_1d(lev._ndarray_values,
  lab))
  elif box:

pandas/tests/frame/test_axis_select_reindex.py

@@ -10,7 +10,7 @@
 import numpy as np
 
 from pandas.compat import lrange, lzip, u
-from pandas import (compat, DataFrame, Series, Index, MultiIndex,
+from pandas import (compat, DataFrame, Series, Index, MultiIndex, Categorical,
  date_range, isna)
 import pandas as pd
 
@@ -1129,6 +1129,19 @@ def test_reindex_multi(self):
 
  assert_frame_equal(result, expected)
 
+ def test_reindex_multi_categorical_time(self):
+ # https://github.com/pandas-dev/pandas/issues/21390
+ midx = pd.MultiIndex.from_product(
+ [Categorical(['a', 'b', 'c']),
+ Categorical(date_range("2012-01-01", periods=3, freq='H'))])
+ df = pd.DataFrame({'a': range(len(midx))}, index=midx)
+ df2 = df.iloc[[0, 1, 2, 3, 4, 5, 6, 8]]
+
+ result = df2.reindex(midx)
+ expected = pd.DataFrame(
+ {'a': [0, 1, 2, 3, 4, 5, 6, np.nan, 8]}, index=midx)
+ assert_frame_equal(result, expected)
+
  data = [[1, 2, 3], [1, 2, 3]]
 
  @pytest.mark.parametrize('actual', [

pandas/tests/groupby/test_categorical.py

@@ -854,3 +854,23 @@ def test_empty_prod():
  result = df.groupby("A", observed=False).B.prod(min_count=1)
  expected = pd.Series([2, 1, np.nan], expected_idx, name='B')
  tm.assert_series_equal(result, expected)
+
+
+def test_groupby_multiindex_categorical_datetime():
+ # https://github.com/pandas-dev/pandas/issues/21390
+
+ df = pd.DataFrame({
+ 'key1': pd.Categorical(list('abcbabcba')),
+ 'key2': pd.Categorical(
+ list(pd.date_range('2018-06-01 00', freq='1T', periods=3)) * 3),
+ 'values': np.arange(9),
+ })
+ result = df.groupby(['key1', 'key2']).mean()
+
+ idx = pd.MultiIndex.from_product(
+ [pd.Categorical(['a', 'b', 'c']),
+ pd.Categorical(pd.date_range('2018-06-01 00', freq='1T', periods=3))],
+ names=['key1', 'key2'])
+ expected = pd.DataFrame(
+ {'values': [0, 4, 8, 3, 4, 5, 6, np.nan, 2]}, index=idx)
+ assert_frame_equal(result, expected)

pandas/tests/indexes/test_multi.py

@@ -12,8 +12,8 @@
 
 import pandas as pd
 
-from pandas import (CategoricalIndex, DataFrame, Index, MultiIndex,
- compat, date_range, period_range)
+from pandas import (CategoricalIndex, Categorical, DataFrame, Index,
+ MultiIndex, compat, date_range, period_range)
 from pandas.compat import PY3, long, lrange, lzip, range, u, PYPY
 from pandas.errors import PerformanceWarning, UnsortedIndexError
 from pandas.core.dtypes.dtypes import CategoricalDtype
@@ -1591,6 +1591,14 @@ def test_get_indexer_nearest(self):
  with pytest.raises(NotImplementedError):
  midx.get_indexer(['a'], method='pad', tolerance=2)
 
+ def test_get_indexer_categorical_time(self):
+ # https://github.com/pandas-dev/pandas/issues/21390
+ midx = MultiIndex.from_product(
+ [Categorical(['a', 'b', 'c']),
+ Categorical(date_range("2012-01-01", periods=3, freq='H'))])
+ result = midx.get_indexer(midx)
+ tm.assert_numpy_array_equal(result, np.arange(9, dtype=np.intp))
+
  def test_hash_collisions(self):
  # non-smoke test that we don't get hash collisions