Related Issues (20)
- Large hike in memory for .unique on array fields HOT 1
- polars.testing.assert_series_equal has incorrect logic for infinities
- Providing nightly builds ? HOT 2
- Increase the verbosity of Schema and Catalog/DB input arguments to the write_database function.
- polars.exceptions.SchemaError: type Int64 is incompatible with expected type Int32 HOT 4
- pl.DataFrame.corr(method = "spearman") HOT 1
- Add to `.eq_missing()` a comment about what happens in the case of `Null.eq_missing(Number)`
- Add `df1.eq_missing(df2)` function
- Serialize UUIDs or show a better error when panicking on object columns
- Feature request: Faster backward- and forward_fill() functions HOT 3
- Nested array list combination gives invalid type
- It is not possible to create Arrays with zero width HOT 3
- Difference with pandas left join caused by new coalesce flag HOT 1
- SQLSyntaxError: invalid 'part' for EXTRACT/DATE_PART: 'year' HOT 1
- `scan_csv` limited to fewer encoding options than `read_csv` HOT 4
- Panic on SchemaMismatch HOT 5
- docs specify outer join will change default coalesce behaviour, but in python I get additional warnings for left join changing behavior
- Improve error messaging when wrong using a wrong schema/dtype
- Schema validation is deferred until an explicit schema/columns call as opposed to at definition time HOT 3
- Polars fails to recognize the benefits of an already sorted rle_id() HOT 1
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