chroe: refactor ArrowDataFrame.with_columns
#7476
FBruzzesirelease-drafter.yml
on: pull_request
update_release_draft
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update_release_draft
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body: '{"body":"## Changes\\n\\n## ✨ Enhancements\\n\\n- feat: add `DataFrame.pivot` for pandas like and Polars backend (#546)\\n\\n## 🐞 Bug fixes\\n\\n- fix: pandas and arrow `to_dummies` with nulls (#1040)\\n\\n## 🛠️ Other improvements\\n\\n- test: unxfail `test_to_dummies_drop_first_na` for cudf (#1364)\\n- chore: More typing in _polars (#1363)\\n- chore: Improve typing in _polars/utils (#1358)\\n- chore: add issue_deprecation_warning to generate_unique_token function (#1359)\\n\\nThank you to all our contributors for making this release possible!\\n@FBruzzesi, @MUKESHRAJMAHENDRAN, @MarcoGorelli and @raisadz\\n","draft":true,"prerelease":false,"make_latest":"true","name":"Narwhals v1.13.5","tag_name":"untagged-a7560488ad26559bf327","target_commitish":"refs/pull/1345/merge"}',
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'While profiling for plotly, py-spy indicates that we spend a lot of time in `validate_dataframe_
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update_release_draft
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'This PR proposed two changes:\r\n' +
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'- Creates constant array via `np.full` in `validate_dataframe_comparand` instead of `[const] * length`\r\n' +
'- Changes the logic in `with_columns` to use pyarrow native methods to insert a column value. I expect this to be faster than the current approach of concatenating the already existing columns with the new ones - caveat is if the number of new columns is order(s) of magnitude greater than the existing ones. In _majority_ of scenarios I would expect this to not be the case, but this is the reason I am opening this as **RFC**\r\n'
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