TST: add copy/view test for setting columns with an array/series #47070
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jorisvandenbossche
added
Testing
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Yes, should be fixed now! |
jreback
approved these changes
May 21, 2022
| def test_set_column_with_series(using_copy_on_write): | ||
| # Case: setting a series as a new column (df[col] = s) copies that data | ||
| df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) | ||
| s = Series([1, 2, 3]) |
| assert not np.shares_memory(df["c"].values, s.values) | ||
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| # and modifying the series does not modify the DataFrame | ||
| s.iloc[0] = 0 |
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could also assert s.iloc[0] == 0, as failure for that to hold would be another way the next assertion could pass
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nitpicks, otherwise LGTM. i really liked the comments @jorisvandenbossche ! |
mroeschke
approved these changes
May 21, 2022
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Thanks @jorisvandenbossche |
yehoshuadimarsky
pushed a commit
to yehoshuadimarsky/pandas
that referenced
this pull request
Jul 13, 2022
…das-dev#47070) * TST: add copy/view test for setting columns with an array/series * Update pandas/tests/copy_view/test_setitem.py * address feedback
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Similarly as #46979, this is adding some test cases explicitly testing copy/view behaviour for a specific topic: if we set an array or series as a column, is the new column a copy or a view of the original data?
At this moment, we actually already do make a copy of that data (with one exception being the cached RangeIndex values). So the tests I added here are confirming that current behaviour in tests, and adding a few checks where Copy-on-Write could delay the copy.