[
https://issues.apache.org/jira/browse/BEAM-13421?focusedWorklogId=697528&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-697528
]
ASF GitHub Bot logged work on BEAM-13421:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 16/Dec/21 21:57
Start Date: 16/Dec/21 21:57
Worklog Time Spent: 10m
Work Description: codecov[bot] edited a comment on pull request #16258:
URL: https://github.com/apache/beam/pull/16258#issuecomment-996201095
#
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@@ Coverage Diff @@
## master #16258 +/- ##
===========================================
+ Coverage 46.13% 83.66% +37.52%
===========================================
Files 197 447 +250
Lines 19519 61705 +42186
===========================================
+ Hits 9006 51626 +42620
- Misses 9542 10079 +537
+ Partials 971 0 -971
```
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Issue Time Tracking
-------------------
Worklog Id: (was: 697528)
Time Spent: 0.5h (was: 20m)
> Python DeferredDataFrame.xs differs from Pandas
> -----------------------------------------------
>
> Key: BEAM-13421
> URL: https://issues.apache.org/jira/browse/BEAM-13421
> Project: Beam
> Issue Type: Bug
> Components: dsl-dataframe
> Affects Versions: 2.34.0
> Environment: Tested in Jupyter Notebook running in Docker.
> The docker file is produced by a modified version of
> https://github.com/fozziethebeat/gpu-jupyter/blob/master/.build/Dockerfile
> Reporter: Keith Stevens
> Assignee: Brian Hulette
> Priority: P2
> Fix For: 2.36.0
>
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> When testing the `xs` method on DeferredDataFrames I'm seeing a few
> inconsistent results. I have two minimal examples that showcase the errors.
>
> First inconsistency: Beam's `xs` requries one left over index field while
> Pandas does not.
> {code:java}
> with beam.Pipeline(options=PipelineOptions()) as pipeline:
> df = pd.DataFrame(
> np.array([
> ['state', 'day1', 12],
> ['state', 'day1', 1],
> ['state', 'day2', 14],
> ['county', 'day1', 9],
> ]),
> columns=['provider', 'time', 'value'])
> # Create just one index field
> df = df.set_index(['provider'])
> df.to_parquet('test.parquet')
>
> # Should print out
> # time value
> # provider
> # state day1 12
> # state day1 1
> # state day2 14
> print(df.xs('state'))
>
> # Should emit the same data to a csv but instead dies due to
> # Cannot remove 1 levels from an index with 1 levels: at least one level
> must be left.
> test_df = (pipeline | read_parquet('test.parquet'))
> (
> test_df.xs('state').to_csv('test.csv')
> ) {code}
> Second inconsistency: Beam dies for no clear reason
> {code:java}
> import pandas as pd
> import numpy as npwith beam.Pipeline(options=PipelineOptions()) as pipeline:
> df = pd.DataFrame(
> np.array([
> ['state', 'day1', 12],
> ['state', 'day1', 1],
> ['state', 'day2', 14],
> ['county', 'day1', 9],
> ]),
> columns=['provider', 'time', 'value'])
> # Create two index fields to satisfy Beam
> df = df.set_index(['provider', 'time'])
> df.to_parquet('test.parquet')
>
> # Should print out
> # value
> # time
> # day1 12
> # day1 1
> # day2 14
> print(df.xs('state'))
>
> # Dies with no clear error at
> #
> /opt/conda/lib/python3.9/site-packages/apache_beam/dataframe/transforms.py in
> output_partitioning_in_stage(expr, stage)
> # 305
> # 306 # Anything that's not an input must have arguments
> # 307 assert len(expr.args())
> # 308
> # 309 arg_partitionings = set(
> test_df = (pipeline | read_parquet('test.parquet'))
> (
> test_df.xs('state').to_csv('test.csv')
> ) {code}
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