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Posted to issues@spark.apache.org by "Bryan Cutler (JIRA)" <ji...@apache.org> on 2019/04/04 18:52:00 UTC
[jira] [Commented] (SPARK-27387) Replace sqlutils assertPandasEqual
with Pandas assert_frame_equal in tests
[ https://issues.apache.org/jira/browse/SPARK-27387?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16810197#comment-16810197 ]
Bryan Cutler commented on SPARK-27387:
--------------------------------------
This can be done after the upgrade of pyarrow version to avoid conflicts.
> Replace sqlutils assertPandasEqual with Pandas assert_frame_equal in tests
> --------------------------------------------------------------------------
>
> Key: SPARK-27387
> URL: https://issues.apache.org/jira/browse/SPARK-27387
> Project: Spark
> Issue Type: Bug
> Components: PySpark, Tests
> Affects Versions: 2.4.1
> Reporter: Bryan Cutler
> Priority: Major
>
> In PySpark unit tests, sqlutils ReusedSQLTestCase.assertPandasEqual is meant to check if 2 pandas.DataFrames are equal but it seems for later versions of Pandas, this can fail if the DataFrame has an array column. This method can be replaced by {{assert_frame_equal}} from pandas.util.testing. This is what it is meant for and it will give a better assertion message as well.
> The test failure I have seen is:
> {noformat}
> ======================================================================
> ERROR: test_supported_types (pyspark.sql.tests.test_pandas_udf_grouped_map.GroupedMapPandasUDFTests)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File "/home/bryan/git/spark/python/pyspark/sql/tests/test_pandas_udf_grouped_map.py", line 128, in test_supported_types
> self.assertPandasEqual(expected1, result1)
> File "/home/bryan/git/spark/python/pyspark/testing/sqlutils.py", line 268, in assertPandasEqual
> self.assertTrue(expected.equals(result), msg=msg)
> File "/home/bryan/miniconda2/envs/pa012/lib/python3.6/site-packages/pandas
> ...
> File "pandas/_libs/lib.pyx", line 523, in pandas._libs.lib.array_equivalent_object
> ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
> {noformat}
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