ENH polars support for InterpolationJoiner#1016
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| ).fit_transform(main) | ||
| assert_array_equal(ns.to_list(ns.col(join, "C")), [10, 11, 12]) | ||
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| join = InterpolationJoiner( |
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here why is this check not necessary anymore?
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We were just fitting on two different column names ("A" and "rB"). Now the test checks that the double fitting returns unique column names. Other tests check fitting on left + right keys
| assert_array_equal(ns.column_names(join), ["A", "B", "B_aux"]) | ||
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| def test_mismatched_indexes(df_module): |
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why do we remove this test?
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The case where we join [0, 1] on [1, 0] is tested in test_fit_on_none. I can separate into two tests if necessary
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I readded a test (for pandas only). Note that because of concat_horizontal, the initial index is reset. We could store it and reapply it in the case of pandas
It's possible to store the index of the first arg of concat_horizontal and set the output to have this index, WDYT ?
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@TheooJ could you do |
jeromedockes
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thanks a lot @TheooJ I think it's almost ready
jeromedockes
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very cool, thanks @TheooJ !
Closes #897
As discussed with @jeromedockes, if the joined dataframe could contain duplicate column names, we add random suffixes and don't raise -- to be consistent with the behavior of the other joiners