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https://issues.apache.org/jira/browse/SPARK-34544?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17291040#comment-17291040
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Daniel Himmelstein commented on SPARK-34544:
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SPARK-34540 is an example.
{{[DataFrameLike|https://github.com/apache/spark/blob/4a3200b08ac3e7733b5a3dc7271d35e6872c5967/python/pyspark/sql/pandas/_typing/protocols/frame.pyi#L37-L428]}}
is missing the {{pd.DataFrame.convert_dtypes}} method. It's also missing
{{pd.DataFrame.head}} and column attribute access
({{pd.DataFrame.my_column_name}}).
Keeping up with all upstream pandas.DataFrame API changes seems like an
impossible task? And can't accommodate the different pandas versions in use by
end users.
> pyspark toPandas() should return pd.DataFrame
> ---------------------------------------------
>
> Key: SPARK-34544
> URL: https://issues.apache.org/jira/browse/SPARK-34544
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.0.1
> Reporter: Rafal Wojdyla
> Priority: Critical
>
> Right now {{toPandas()}} returns {{DataFrameLike}}, which is an incomplete
> "view" of pandas {{DataFrame}}. Which leads to cases like mypy reporting that
> certain pandas methods are not present in {{DataFrameLike}}, even tho those
> methods are valid methods on pandas {{DataFrame}}, which is the actual type
> of the object. This requires type ignore comments or asserts.
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