updated in response to PR feedback

arkouda/dataframe.py

@@ -106,7 +106,7 @@ class DataFrameGroupBy:
         If True the grouped values of the aggregation keys will be treated as an index.
     """
 
-    def __init__(self, gb, df, gb_key_names=None, as_index=True, dropna=True):
+    def __init__(self, gb, df, gb_key_names=None, as_index=True):
 
         self.gb = gb
         self.df = df
@@ -115,11 +115,11 @@ def __init__(self, gb, df, gb_key_names=None, as_index=True, dropna=True):
         for attr in ["nkeys", "permutation", "unique_keys", "segments"]:
             setattr(self, attr, getattr(gb, attr))
 
-        self.dropna = dropna
+        self.dropna = self.gb.dropna
         self.where_not_nan = None
         self.all_non_nan = False
 
-        if dropna:
+        if self.dropna:
             from arkouda import all as ak_all
             from arkouda import isnan
 
@@ -2714,7 +2714,7 @@ def GroupBy(self, keys, use_series=False, as_index=True, dropna=True):
 
         gb = akGroupBy(cols, dropna=dropna)
         if use_series:
-            gb = DataFrameGroupBy(gb, self, gb_key_names=keys, as_index=as_index, dropna=dropna)
+            gb = DataFrameGroupBy(gb, self, gb_key_names=keys, as_index=as_index)
         return gb
 
     def memory_usage(self, index=True, unit="B") -> Series:

tests/dataframe_test.py

@@ -650,16 +650,17 @@ def test_gb_aggregations_example_numeric_types(self, agg):
         pd_result = getattr(pd_df.groupby(group_on), agg)()
         assert_frame_equal(ak_result.to_pandas(retain_index=True), pd_result)
 
+    @pytest.mark.parametrize("dropna", [True, False])
     @pytest.mark.parametrize("agg", ["count", "max", "mean", "median", "min", "std", "sum", "var"])
-    def test_gb_aggregations_with_nans(self, agg):
+    def test_gb_aggregations_with_nans(self, agg, dropna):
         df = self.build_ak_df_with_nans()
         # @TODO handle bool columns correctly
         df.drop("bools", axis=1, inplace=True)
         pd_df = df.to_pandas()
 
         group_on = ["key1", "key2"]
-        ak_result = getattr(df.groupby(group_on), agg)()
-        pd_result = getattr(pd_df.groupby(group_on, as_index=False), agg)()
+        ak_result = getattr(df.groupby(group_on, dropna=dropna), agg)()
+        pd_result = getattr(pd_df.groupby(group_on, as_index=False, dropna=dropna), agg)()
         assert_frame_equal(ak_result.to_pandas(retain_index=True), pd_result)
 
         # TODO aggregations of string columns not currently supported (even for count)
@@ -668,27 +669,28 @@ def test_gb_aggregations_with_nans(self, agg):
         pd_df = df.to_pandas()
 
         group_on = ["nums1", "nums2"]
-        ak_result = getattr(df.groupby(group_on), agg)()
-        pd_result = getattr(pd_df.groupby(group_on, as_index=False), agg)()
+        ak_result = getattr(df.groupby(group_on, dropna=dropna), agg)()
+        pd_result = getattr(pd_df.groupby(group_on, as_index=False, dropna=dropna), agg)()
         assert_frame_equal(ak_result.to_pandas(retain_index=True), pd_result)
 
         # TODO aggregation mishandling NaN see issue #3765
         df.drop("nums2", axis=1, inplace=True)
         pd_df = df.to_pandas()
         group_on = "nums1"
-        ak_result = getattr(df.groupby(group_on), agg)()
-        pd_result = getattr(pd_df.groupby(group_on), agg)()
+        ak_result = getattr(df.groupby(group_on, dropna=dropna), agg)()
+        pd_result = getattr(pd_df.groupby(group_on, dropna=dropna), agg)()
         assert_frame_equal(ak_result.to_pandas(retain_index=True), pd_result)
 
-    def test_count_nan_bug(self):
+    @pytest.mark.parametrize("dropna", [True, False])
+    def test_count_nan_bug(self, dropna):
         # verify reproducer for #3762 is fixed
         df = ak.DataFrame({"A": [1, 2, 2, np.nan], "B": [3, 4, 5, 6], "C": [1, np.nan, 2, 3]})
-        ak_result = df.groupby("A").count()
-        pd_result = df.to_pandas().groupby("A").count()
+        ak_result = df.groupby("A", dropna=dropna).count()
+        pd_result = df.to_pandas().groupby("A", dropna=dropna).count()
         assert_frame_equal(ak_result.to_pandas(retain_index=True), pd_result)
 
-        ak_result = df.groupby(["A", "C"], as_index=False).count()
-        pd_result = df.to_pandas().groupby(["A", "C"], as_index=False).count()
+        ak_result = df.groupby(["A", "C"], as_index=False, dropna=dropna).count()
+        pd_result = df.to_pandas().groupby(["A", "C"], as_index=False, dropna=dropna).count()
         assert_frame_equal(ak_result.to_pandas(retain_index=True), pd_result)
 
     def test_gb_aggregations_return_dataframe(self):