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TST: Copy on Write for filter #50589

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Merged
merged 3 commits into from
Jan 6, 2023
Merged

TST: Copy on Write for filter #50589

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TST: Copy on Write for filter
lithomas1 Jan 5, 2023
378b61f
Move iloc inside CoW case
lithomas1 Jan 5, 2023
02bb978
Merge branch 'main' into test-filter-cow
lithomas1 Jan 6, 2023
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21 changes: 21 additions & 0 deletions pandas/tests/copy_view/test_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,6 +174,27 @@ def test_select_dtypes(using_copy_on_write):
tm.assert_frame_equal(df, df_orig)


@pytest.mark.parametrize(
"filter_kwargs", [{"items": ["a"]}, {"like": "a"}, {"regex": "a"}]
)
def test_filter(using_copy_on_write, filter_kwargs):
# Case: selecting columns using `filter()` returns a new dataframe
# + afterwards modifying the result
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
df_orig = df.copy()
df2 = df.filter(**filter_kwargs)
if using_copy_on_write:
assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
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Out of curiosity, do you think that numpy.may_share_memory() function can be faster than using np.shares_memory()? I compared it once, and it seems like it could be the case, but not super important.

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This doesn't really matter for tests, but seems like it could be faster

else:
assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))

# mutating df2 triggers a copy-on-write for that column/block
df2.iloc[0, 0] = 0
if using_copy_on_write:
assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
tm.assert_frame_equal(df, df_orig)


def test_pop(using_copy_on_write):
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
df_orig = df.copy()
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