Comments (1)
Resolved with #70
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Related Issues (20)
- Add more granular lazy execution flags in query compiler
- why so slow compare to dask HOT 1
- Modin read_csv not loading the complete file (memory leak in file reading) HOT 1
- BUG: groupby().apply() raise numpy ValueError when Series has multi index HOT 1
- BUG: Apply on axis=1 causes "daemonic processes are not allowed to have children" on some operations on Dask engine, or launches Ray instance HOT 1
- modin with ray engine hang HOT 5
- Possible issue with `dropna(how="all")` not deleting data from partition on ray. HOT 1
- `_ATTRS_NO_LOOKUP` in pandas API should include ipython canary methods
- BUG: Cpu count would be set incorrectly on a cluster.
- AttributeError: 'DataFrame' object has no attribute 'copy' on polars API
- BUG: Behaviour differs if ray is imported and or initialized manually. Without manual import: it fails HOT 2
- [RAY] to_parquet() fails when spilled objects reach 64gig... Also my data is just 40gig HOT 10
- BUG: [RAY] ray initialisation sets _memory and object_store_memory to the same value, leading to crashes and less flexibility HOT 2
- to_parquet() needs option of how many files to create, or like rays implementation: num_rows_per_file HOT 2
- BUG: outofmemory read from big file and dump to a new one HOT 1
- BUG: `Dataframe.astype(...)` Fails With `'bool' object has no attribute 'all'` HOT 2
- Use pre-commit library to run linters
- Improve the user facing DataFrame and Series constructor by hide the query_compiler parameter HOT 3
- df.max()/min() on 1 column df leads to "could not broadcast input array from shape (6,) into shape (5,)" what from parquet loaded with ray
- Add a new environment variable for using dynamic partitioning
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