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):