Comments (2)
Eh.. sorry, can you try in English?
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When using Polars to read excel table data, only all data can be read at one time, but some columns are not needed, if all reading will be very performance loss, it is strongly recommended that you can freely choose the required columns to read data like pandas, thank you
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Related Issues (20)
- `with_columns(dict)` fail silently if key exists as column HOT 5
- Polars write database - Rest API call failing with AWS Lambda trigger
- Change `read_csv` and `read_ipc` to use object store instead of fsspec
- `.over()` fails with `.top_k_by` HOT 3
- `join_nulls` in "asof" join HOT 3
- exception on numpy slicing literal column with object column
- Scanning cloud paths with percentages '%' fails
- Make pl.Enum(...) return type rather than instance HOT 2
- Elementwise check on `join` expressions is too restrictive HOT 1
- Built-in datasets and a function to load them HOT 1
- Python test workflows may fail due to failure to download `torch` dependency HOT 1
- `scan_parquet` does not optimise `slice` or `tail` operations HOT 3
- Apply function to rows of dictionaries in `map_rows`
- De-duplicate decompression in CSV/NDJSON scans
- import numpy with initial null value HOT 2
- Eager/Lazy API alignment: LazyGroupBy vs DynamicGroupBy
- list.any() and list.all() behavior with all null list looks incorrect HOT 3
- pl.from_numpy produces a DataFrame with the wrong values if a schema is given
- Assigning multiple columns on same condition, splitting struct into multiple columns? HOT 2
- Add a `replace` method to the `expr.name` attribute
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