Comments (4)
Yes, indeed, you are right. This is IMO equivalent to #730 if we agree that we should rely on the backend for perfect matches..
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Good catch, this is an important special case.
Ideally, when there are only exact matches, we shouldn't even go down that code path and only use standard matching from the backend (pandas, polars...), as it will be much faster.
from skrub.
this is not the same as when the user requires perfect matches by setting match_score=1
. This division by 0 is triggered when all rows happen to have a perfect match in the joined dataframe. it will be fixed by #802
from skrub.
fixed by #802
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Related Issues (20)
- Polars deprecation inbound HOT 4
- InterpolationJoiner - polars HOT 1
- packaging>=23.1 - can it move to dev instead of install_required ? HOT 2
- incorrect github action matrix description
- Failing fetch ken embeddings test HOT 2
- Should accept pipelines and transformers HOT 1
- TableVectorizer raises when a categorical column contains `pd.NA` HOT 1
- Add features to the `DatetimeEncoder` HOT 16
- Hacky Idea: ifelse in a pipeline HOT 10
- Scikit-learn v1.5 breaks skrub HOT 4
- `drop_nulls` and `is_null` have a different behavior for polars and pandas HOT 1
- Follow-ups after #902
- GapEncoder and MinHashEncoder modify their input inplace HOT 2
- adding `make_learner` to create a default pipeline for a given predictor
- Shorthand for getting only the preprocessing part of the TableVectorizer HOT 2
- AggJoiner raises exceptions when trying to join multiple tables at once HOT 5
- Faster alternative to GapEncoder HOT 1
- Display columns with the HTML representation of the fitted TableVectorizer
- `fetch_ken_embeddings` does not use `suffix` with default parameter HOT 1
- Ken embeddings RAM and disk usage HOT 2
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