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DOC: Add docstrings to fixtures in /series/methods/test_drop_duplicates.py #59265

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Merged
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Jul 18, 2024
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DOC: Add docstrings to fixtures in /series/methods/test_drop_duplicates.py #59265

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Add docstrings to fixtures in /series/methods/test_drop_duplicates.py
ivonastojanovic Jul 17, 2024
9745485
fixup! Add docstrings to fixtures in /series/methods/test_drop_duplic…
ivonastojanovic Jul 18, 2024
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20 changes: 19 additions & 1 deletion pandas/tests/series/methods/test_drop_duplicates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,10 +75,22 @@ class TestSeriesDropDuplicates:
params=["int_", "uint", "float64", "str_", "timedelta64[h]", "datetime64[D]"]
)
def dtype(self, request):
"""
Fixture that provides different data types for testing.
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Could you ensure these docstrings just fit on one line?

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Do you mean to write a shorter description or the same description in just one line?

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Both I would say. These docstrings aren't entirely critical so it's best to be succinct.

The parameterized fixture returns various numpy data types including
integer, unsigned integer, float, string, timedelta, and datetime.
"""
return request.param

@pytest.fixture
def cat_series_unused_category(self, dtype, ordered):
"""
Fixture that creates a Categorical Series with some unused categories.
This fixture creates a Categorical Series based on the given dtype and
ordered parameters. The input series contains some categories that are
not used in the actual data, allowing the testing of categorical
behavior with unused categories.
"""
# Test case 1
cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype))

Expand Down Expand Up @@ -141,7 +153,13 @@ def test_drop_duplicates_categorical_non_bool_keepfalse(

@pytest.fixture
def cat_series(self, dtype, ordered):
# no unused categories, unlike cat_series_unused_category
"""
Fixture that creates a Categorical Series with no unused categories.
This fixture creates a Categorical Series based on the given dtype and
ordered parameters. The input series contains categories that are all
used in the actual data, allowing the testing of categorical behavior
without unused categories.
"""
cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype))

input2 = np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype(dtype))
Expand Down