CI: typo fixup for ResourceWarning (pandas-dev#40088)

Author: jbrockmendel

pandas/core/dtypes/cast.py

@@ -94,6 +94,7 @@
     PeriodDtype,
 )
 from pandas.core.dtypes.generic import (
+    ABCDataFrame,
     ABCExtensionArray,
     ABCSeries,
 )
@@ -238,6 +239,9 @@ def maybe_downcast_to_dtype(
     try to cast to the specified dtype (e.g. convert back to bool/int
     or could be an astype of float64->float32
     """
+    if isinstance(result, ABCDataFrame):
+        # see test_pivot_table_doctest_case
+        return result
     do_round = False
 
     if isinstance(dtype, str):
@@ -307,7 +311,7 @@ def maybe_downcast_numeric(
     -------
     ndarray or ExtensionArray
     """
-    if not isinstance(dtype, np.dtype):
+    if not isinstance(dtype, np.dtype) or not isinstance(result.dtype, np.dtype):
         # e.g. SparseDtype has no itemsize attr
         return result
 

pandas/tests/io/parser/common/test_chunksize.py

@@ -193,7 +193,7 @@ def test_warn_if_chunks_have_mismatched_type(all_parsers, request):
         # 2021-02-21 this occasionally fails on the CI with an unexpected
         #  ResourceWarning that we have been unable to track down,
         #  see GH#38630
-        if "ResourceError" not in str(err) or parser.engine != "python":
+        if "ResourceWarning" not in str(err) or parser.engine != "python":
             raise
 
         # Check the main assertion of the test before re-raising

pandas/tests/reshape/test_pivot.py

@@ -2063,6 +2063,55 @@ def agg(arr):
         with pytest.raises(KeyError, match="notpresent"):
             foo.pivot_table("notpresent", "X", "Y", aggfunc=agg)
 
+    def test_pivot_table_doctest_case(self):
+        # TODO: better name.  the relevant characteristic is that
+        #  the call to maybe_downcast_to_dtype(agged[v], data[v].dtype) in
+        #  __internal_pivot_table has `agged[v]` a DataFrame instead of Series,
+        #  i.e agged.columns is not unique
+        df = DataFrame(
+            {
+                "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
+                "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
+                "C": [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+                "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+                "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
+            }
+        )
+
+        table = pivot_table(
+            df,
+            values=["D", "E"],
+            index=["A", "C"],
+            aggfunc={"D": np.mean, "E": [min, max, np.mean]},
+        )
+        cols = MultiIndex.from_tuples(
+            [("D", "mean"), ("E", "max"), ("E", "mean"), ("E", "min")]
+        )
+        index = MultiIndex.from_tuples(
+            [("bar", "large"), ("bar", "small"), ("foo", "large"), ("foo", "small")],
+            names=["A", "C"],
+        )
+        vals = np.array(
+            [
+                [5.5, 9.0, 7.5, 6.0],
+                [5.5, 9.0, 8.5, 8.0],
+                [2.0, 5.0, 4.5, 4.0],
+                [2.33333333, 6.0, 4.33333333, 2.0],
+            ]
+        )
+        expected = DataFrame(vals, columns=cols, index=index)
+        tm.assert_frame_equal(table, expected)
+
 
 class TestPivot:
     def test_pivot(self):