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To access a value by position, use ser.iloc[pos] #2750

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To access a value by position, use ser.iloc[pos] #2750

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2 changes: 1 addition & 1 deletion featuretools/primitives/standard/transform/time_series/expanding/expanding_count.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,7 @@ def expanding_count(datetime_series):
min_periods=self.min_periods,
).count()
num_nans = self.gap + self.min_periods - 1
count_series[range(num_nans)] = np.nan
count_series.iloc[range(num_nans)] = np.nan
return count_series

return expanding_count
6 changes: 3 additions & 3 deletions featuretools/tests/computational_backend/test_feature_set_calculator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -517,7 +517,7 @@ def test_make_3_stacked_agg_feats(df):
feature_set = FeatureSet([sum_3])
calculator = FeatureSetCalculator(es, time_last=None, feature_set=feature_set)
df = calculator.run(np.array(["z"]))
v = df[sum_3.get_name()][0]
v = df[sum_3.get_name()].iloc[0]
assert v == 5


Expand Down Expand Up @@ -635,7 +635,7 @@ def test_deep_agg_feat_chain(es):
calculator = FeatureSetCalculator(es, time_last=None, feature_set=feature_set)
df = calculator.run(np.array(["United States"]))

v = df[region_avg_feat.get_name()][0]
v = df[region_avg_feat.get_name()].iloc[0]
assert v == 17 / 3.0


Expand Down Expand Up @@ -696,7 +696,7 @@ def test_direct_squared(es):
calculator = FeatureSetCalculator(es, time_last=None, feature_set=feature_set)
df = calculator.run(np.array([0, 1, 2]))
for i, row in df.iterrows():
assert (row[0] * row[0]) == row[1]
assert (row.iloc[0] * row.iloc[0]) == row.iloc[1]


def test_agg_empty_child(es):
Expand Down
4 changes: 2 additions & 2 deletions featuretools/tests/primitive_tests/transform_primitive_tests/test_expanding_primitives.py
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Expand Up @@ -28,7 +28,7 @@ def test_expanding_count_series(window_series, min_periods, gap):
test = window_series.shift(gap)
expected = test.expanding(min_periods=min_periods).count()
num_nans = gap + min_periods - 1
expected[range(num_nans)] = np.nan
expected.iloc[range(num_nans)] = np.nan
primitive_instance = ExpandingCount(min_periods=min_periods, gap=gap).get_function()
actual = primitive_instance(window_series.index)
pd.testing.assert_series_equal(pd.Series(actual), expected)
Expand All @@ -47,7 +47,7 @@ def test_expanding_count_date_range(window_date_range, min_periods, gap):
test = _apply_gap_for_expanding_primitives(gap=gap, x=window_date_range)
expected = test.expanding(min_periods=min_periods).count()
num_nans = gap + min_periods - 1
expected[range(num_nans)] = np.nan
expected.iloc[range(num_nans)] = np.nan
primitive_instance = ExpandingCount(min_periods=min_periods, gap=gap).get_function()
actual = primitive_instance(window_date_range)
pd.testing.assert_series_equal(pd.Series(actual), expected)
Expand Down