Merge branch 'master' into 24893-pivot_table

pandas-dev · WillAyd · Nov 23, 2019 · Sep 1, 2019 · Sep 1, 2019 · Sep 1, 2019
commit c957d01987bc8cce9a643f7b69ba4d1a0e8e8bc2
diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
@@ -595,8 +595,16 @@ Reshaping
 
 - Bug in :meth:`DataFrame.apply` that caused incorrect output with empty :class:`DataFrame` (:issue:`28202`, :issue:`21959`)
 - Bug in :meth:`DataFrame.stack` not handling non-unique indexes correctly when creating MultiIndex (:issue: `28301`)
--
 - Bug in :meth:`pivot_table` not returning correct type ``float`` when ``margins=True`` and ``aggfunc='mean'`` (:issue:`24893`)
+- Bug :func:`merge_asof` could not use :class:`datetime.timedelta` for ``tolerance`` kwarg (:issue:`28098`)
+- Bug in :func:`merge`, did not append suffixes correctly with MultiIndex (:issue:`28518`)
+- :func:`qcut` and :func:`cut` now handle boolean input (:issue:`20303`)
+- Fix to ensure all int dtypes can be used in :func:`merge_asof` when using a tolerance value. Previously every non-int64 type would raise an erroneous ``MergeError`` (:issue:`28870`).
+- Better error message in :func:`get_dummies` when `columns` isn't a list-like value (:issue:`28383`)
+- Bug :meth:`Series.pct_change` where supplying an anchored frequency would throw a ValueError (:issue:`28664`)
+- Bug where :meth:`DataFrame.equals` returned True incorrectly in some cases when two DataFrames had the same columns in different orders (:issue:`28839`)
+- Bug in :meth:`DataFrame.replace` that caused non-numeric replacer's dtype not respected (:issue:`26632`)
+
 
 Sparse
 ^^^^^^

diff --git a/pandas/tests/reshape/test_pivot.py b/pandas/tests/reshape/test_pivot.py
@@ -1674,6 +1674,51 @@ def test_margins_casted_to_float(self, observed):
         )
         tm.assert_frame_equal(result, expected)
 
+    def test_pivot_with_categorical(self, observed, ordered_fixture):
+        # gh-21370
+        idx = [np.nan, "low", "high", "low", np.nan]
+        col = [np.nan, "A", "B", np.nan, "A"]
+        df = pd.DataFrame(
+            {
+                "In": pd.Categorical(
+                    idx, categories=["low", "high"], ordered=ordered_fixture
+                ),
+                "Col": pd.Categorical(
+                    col, categories=["A", "B"], ordered=ordered_fixture
+                ),
+                "Val": range(1, 6),
+            }
+        )
+        # case with index/columns/value
+        result = df.pivot_table(
+            index="In", columns="Col", values="Val", observed=observed
+        )
+
+        expected_cols = pd.CategoricalIndex(
+            ["A", "B"], ordered=ordered_fixture, name="Col"
+        )
+
+        expected = pd.DataFrame(
+            data=[[2.0, np.nan], [np.nan, 3.0]], columns=expected_cols
+        )
+        expected.index = Index(
+            pd.Categorical(
+                ["low", "high"], categories=["low", "high"], ordered=ordered_fixture
+            ),
+            name="In",
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        # case with columns/value
+        result = df.pivot_table(columns="Col", values="Val", observed=observed)
+
+        expected = pd.DataFrame(
+            data=[[3.5, 3.0]], columns=expected_cols, index=Index(["Val"])
+        )
+
+        tm.assert_frame_equal(result, expected)
+
     def test_categorical_aggfunc(self, observed):
         # GH 9534
         df = pd.DataFrame(