Comments (1)
We switched to an implementation that uses a logical query planning layer, which means that we re-implemented all the methods and functions. Anything that is not imported from dask.dataframe as dd won't work anymore, for example using
dd.multi.foo will use the legacy implementation and not the new one.
You can only use stuff that is accessible from dask.dataframe.init directly
from dask.
Related Issues (20)
- Issues with da.linalg.inv using CuPy-backed Dask arrays HOT 1
- BUG: `array.asarray` does not respect `dtype` arg HOT 1
- `2024.8.0` breaks `sparse` array indexing HOT 6
- order: dask order returns suboptimal ordering for xr.rechunk().groupby().reduce("cohorts")
- Error when saving joined dataframe with index name to parquet
- Error in addition of dask dataframe and array when reading from parquet HOT 3
- `2024.08.0` array slicing does not preserve masks HOT 1
- Dask 2024.8 started failing when indexing result of numpy.flatnonzero HOT 1
- DataFrame object vs string datatype HOT 3
- Columns are missing after rename HOT 2
- ⚠️ Upstream CI failed ⚠️ HOT 1
- Out of memory HOT 12
- Unexpected Behavior When Using `dask.delayed` with `xarray` to Load a Chunked Dataset HOT 3
- gpuCI broken HOT 2
- Expose a blockwise - reshape operation that doesn't guarantee to keep the ordering consistent for downstream libraries HOT 5
- ⚠️ Upstream CI failed ⚠️ HOT 3
- Requested dask.distributed scheduler but no Client active HOT 4
- cannot access local variable 'divisions' where it is not associated with a value
- Bump mindeps for pyarrow and numpy HOT 3
- read_sql_table would throw an exception when calling for unique values of a column
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from dask.