Comments (1)
Is this one similar to enabling extension arrays? That would be amazing of extension arrays followed this.
Back to this one, are you going to have meta data stored in parquet, loosely like this maybe https://stackoverflow.com/a/77642404/1818713?
That is not really the focus of this effort. This effort is really to utilize and track metadata or statistics internally that can speed up computations. E.g. if we know the minimum value already, doing a min
can be free. We already track various kinds of metadata (sorting, null_count, fast_explode), but we would like to track a lot more and effectively utilize that information.
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Related Issues (20)
- Using `scan_csv` with `with_column_names` argument disables schema validation
- `strict` parameter not applied correctly during row-wise DataFrame construction HOT 5
- Fix doc of GroupBy.__iter__ HOT 1
- SchemaMismatch during `collect`
- Segmentation fault with `to_numpy` with Numpy 2 and datetime column HOT 1
- Entered unreachable code on valid SQL HOT 3
- Using pandas `Timestamp` in DataFrame constructor drops timezone info
- Add instructions for building from source for non polars developers
- Aggregate the whole dataframe HOT 2
- Panic when filtering by non-boolean
- Issue in global variable discovery in `polars.sql` HOT 3
- struct selector HOT 2
- Indexing a Series object with a string raises a confusing TypeError. HOT 1
- pl.from_pandas fails when NaNs are in object columns HOT 1
- Iter_batches does not work with a Snowflake connection in pl.read_database()
- Possible bug at `when().then().otherwise()` HOT 3
- read_excel (calamine engine) Boolean schema override not working
- `from_arrow` is not zero-cost HOT 1
- `DataFrame.cast` and `Series.cast` with nested dtype casts to `null` without error HOT 1
- str.split with list.get is not detecting the indexes
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