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TST: groupby string dtype #52599

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Merged
merged 2 commits into from
Apr 12, 2023
Merged

TST: groupby string dtype #52599

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TST: groupby string dtype
srkds Apr 11, 2023
8764061
compare dataframe and formatting
srkds Apr 12, 2023
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9 changes: 9 additions & 0 deletions pandas/tests/groupby/test_groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2709,6 +2709,15 @@ def test_single_element_list_grouping():
expected = [(1,), (2,)]
assert result == expected

def test_groupby_string_dtype():
#GH 40148
df = DataFrame({"str_col": ["a", "b", "c", "a"], "num_col": [1, 2, 3, 2]})
df["str_col"] = df['str_col'].astype("string")
expected = df.dtypes[0]
grouped = df.groupby("str_col", as_index=False)
avg = grouped.mean()
result = avg.dtypes[0]
tm.assert_equal(result, expected)
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Could you instead compare the resulting DataFrame e.g.

result = df.groupby("str_col", as_index=False)
expected = DataFrame(...)
tm.assert_frame_equal(result, expected)

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@srkds srkds Apr 12, 2023 •
edited
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Got it!

My bad I didn't know about assert_frame_equal. Thanks!
I read about it here, It will compare dtypes. Sounds good 👍

Are these the required changes?

expected = DataFrame({"str_col": ["a", "b", "c",], "num_col": [1.5, 2.0, 3.0]})
expected["str_col"] = expected['str_col'].astype("string")
result=grouped.mean()
tm.assert_frame_equal(result,expected)

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Yes

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Yes

Thanks for confirming.


@pytest.mark.parametrize(
"level_arg, multiindex", [([0], False), ((0,), False), ([0], True), ((0,), True)]
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