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https://issues.apache.org/jira/browse/BEAM-11628?focusedWorklogId=544495&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-544495
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ASF GitHub Bot logged work on BEAM-11628:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 29/Jan/21 21:32
Start Date: 29/Jan/21 21:32
Worklog Time Spent: 10m
Work Description: TheNeuralBit opened a new pull request #13843:
URL: https://github.com/apache/beam/pull/13843
This PR adds support for
[GroupBy.apply](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#flexible-apply).
In general this is a pretty simple operation, similar to an unliftable
aggregation, but there's one major complication: the frames passed to the apply
function include the _original_ index, while our groupby(..) implementation
overrides any existing index with just the grouping indexes/columns so we can
conveniently use Index partitioning.
There are a couple possible approaches to get the original index through,
but neither is perfect:
1. Move the original indexes into columns, use set_index to move them back
after grouping.
2. Don't remove the original indexes in groupby(..), instead just add any
grouped columns, and keep all the original indexes. Then use
Index(grouping_indexes + grouping_columns) partitioning.
I spent some time trying to get (1) to work, but the book-keeping proved to
be too painful. Handling grouped Series is tough, since they need to be turned
into DataFrames and back, and projections on the DeferredGroupBy are tricky as
well.
This PR uses option (2), which was simpler to implement, but it would be the
first application of `Index([i,j])` partitioning, which hasn't been well
defined. In order to limit the effect the original to_group and ungrouped
expressions are still used for operations that don't need the original index.
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Issue Time Tracking
-------------------
Worklog Id: (was: 544495)
Remaining Estimate: 0h
Time Spent: 10m
> Implement GroupBy.apply
> -----------------------
>
> Key: BEAM-11628
> URL: https://issues.apache.org/jira/browse/BEAM-11628
> Project: Beam
> Issue Type: Improvement
> Components: sdk-py-core
> Reporter: Brian Hulette
> Assignee: Brian Hulette
> Priority: P2
> Labels: dataframe-api
> Time Spent: 10m
> Remaining Estimate: 0h
>
> https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.apply.html
> We can partition by key and then apply func on each key. It should be
> possible to create a proxy by passing a proxy to the function.
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