Comments (1)
The current state of affairs is that we do not use backend information anywhere. The main place where this is needed is when accessing the type, through fields Series.dtype
and DataFrame.dtypes
. The simplest way would be to obtain information from partitions, but this would lead to poorer performance.
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Related Issues (20)
- Possibility of `unique` function optimization
- Add extra check for `NPartitions` and `CpuCount` classes
- Change "master" branch to "main"
- Fix ASV after changing default branch: "master" -> "main"
- Recheck that all methods with `numeric_only=True` works correctly
- Implement DataFrame API standard HOT 1
- Update docs on how to enable Modin logs for high-level API and low-level API
- Use custom resources for Ray to schedule a task on a concrete node
- BUG: df.melt cannot does not handle duplicate value_vars correctly
- BUG: Falling back to standard Pandas implementation when assigning a dataframe to a columnar selection HOT 1
- Find a way to limit parallelism for tasks running in parallel for different engines
- Wrong dtypes after `merge` op HOT 2
- Update docs as to when Modin operators work best HOT 4
- Update `dataframe-api-compat` version
- Consider remove `use_legacy_dataset=False`
- BUG: Importing `modin.pandas.api.extensions` overwrites re-export of `pandas.api` submodules
- Do `right._modin_frame.combine()` for merge and join only when necessary
- Don't preserve bad partition for `merge`
- Remove Series.dt.to_timestamp HOT 3
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