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https://issues.apache.org/jira/browse/SPARK-54936?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ruifeng Zheng updated SPARK-54936:
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Summary: Monitor behaviour changes from upstream (was: Monitor upstream
behaviour changes)
> Monitor behaviour changes from upstream
> ----------------------------------------
>
> Key: SPARK-54936
> URL: https://issues.apache.org/jira/browse/SPARK-54936
> Project: Spark
> Issue Type: Umbrella
> Components: PySpark, Tests
> Affects Versions: 4.2.0
> Reporter: Ruifeng Zheng
> Priority: Major
>
> PySpark suffers a lot from behaviour changes of dependencies, like Pandas,
> PyArrow, Numpy.
> We should add tests to monitor the behaviour of key functions/features, like:
> * pa.array
> * pa.scalar
> * pa.Array.from_pandas
> * pa.Array.to_pandas
> * pa.Array.cast
> * pa.Table.from_pandas
> * pa.Table.from_batches
> * pa.Table.from_arrays
> * pa.Table.from_pydict
> * pa.Table.to_pandas
> * pa.Table.cast
> * pa.RecordBatch.from_arrays
> * pa.RecordBatch.from_struct_array
> * pa.RecordBatch.from_pylist
> * pd.Series(data=arrow_data)
> * time zone handling in pyarrow, pandas
> * zero copy in pandas<->pyarrow data conversions
> * etc
>
> The new tests should be dedicated for upstream, spark stuffs should not be
> involved.
>
> The test data should consider:
> 1, Missing values, like Nullable data, NaN, pd.Nat, etc;
> 2, Empty instance (e.g. empty list);
> 3, invalid values to check the support and error;
> 4, Python instance (list,tuple,array,etc);
> 5, Pandas instance
> 6, Numpy instance
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