fixups

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -234,7 +234,6 @@ def test_aggregate_item_by_item(df):
     K = len(result.columns)
 
     # GH5782
-    # odd comparisons can result here, so cast to make easy
     exp = Series(np.array([foo] * K), index=list("BCD"), name="foo")
     tm.assert_series_equal(result.xs("foo"), exp)
 

pandas/tests/resample/test_datetime_index.py

@@ -1205,7 +1205,7 @@ def test_resample_median_bug_1688():
         result = df.resample("T").apply(lambda x: x.mean())
         exp = df.asfreq("T")
         if dtype == "float32":
-            # Empty groups cause x.mean() to return float64
+            # TODO: Empty groups cause x.mean() to return float64
             exp = exp.astype("float64")
         tm.assert_frame_equal(result, exp)
 
@@ -1687,7 +1687,7 @@ def f(data, add_arg):
     df = DataFrame({"A": 1, "B": 2}, index=date_range("2017", periods=10))
     result = df.groupby("A").resample("D").agg(f, multiplier)
     expected = df.groupby("A").resample("D").mean().multiply(multiplier)
-    # TODO: resample(...).mean should be a float64
+    # TODO: resample(...).mean should be a float instead of int
     expected = expected.astype("float64")
     tm.assert_frame_equal(result, expected)
 

pandas/tests/reshape/test_crosstab.py

@@ -559,6 +559,7 @@ def test_crosstab_with_numpy_size(self):
         expected = DataFrame(
             expected_data, index=expected_index, columns=expected_column
         )
+        # aggfunc is np.size, resulting in integers
         expected["All"] = expected["All"].astype("int64")
         tm.assert_frame_equal(result, expected)