[jira] [Created] (SPARK-42647) Remove aliases from deprecated numpy data types

["Aimilios Tsouvelekakis (Jira)" <ji...@apache.org>]

Aimilios Tsouvelekakis created SPARK-42647:
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             Summary: Remove aliases from deprecated numpy data types
                 Key: SPARK-42647
                 URL: https://issues.apache.org/jira/browse/SPARK-42647
             Project: Spark
          Issue Type: Improvement
          Components: PySpark
    Affects Versions: 3.3.2, 3.3.1, 3.3.0, 3.3.3, 3.4.0, 3.4.1
            Reporter: Aimilios Tsouvelekakis


Numpy has started changing the alias to some of its data-types. This means that users with the latest version of numpy they will face either warnings or errors according to the type that they are using. This affects all the users using numoy > 1.20.0. One of the types was fixed back in September with this [pull|https://github.com/apache/spark/pull/37817] request.

The problem can be split into 2 types:

[numpy 1.24.0|https://github.com/numpy/numpy/pull/22607]: The scalar type aliases ending in a 0 bit size: np.object0, np.str0, np.bytes0, np.void0, np.int0, np.uint0 as well as np.bool8 are now deprecated and will eventually be removed. At this point in numpy 1.25.0 they give a awarning
[numpy 1.20.0|https://github.com/numpy/numpy/pull/14882]: Using the aliases of builtin types like np.int is deprecated and removed since numpy version 1.24.0

The changes are needed so pyspark can be compatible with the latest numpy and avoid
 * attribute errors on data types being deprecated from version 1.20.0: [https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations]
 * warnings on deprecated data types from version 1.24.0: [https://numpy.org/devdocs/release/1.24.0-notes.html#deprecations]

 

From my main research I see the following:

The only changes that are functional are related with the conversion.py file. The rest of the changes are inside tests in the user_guide or in some docstrings describing specific functions. Since I am not an expert in these tests I wait for the reviewer and some people with more experience in the pyspark code.

These types are aliases for classic python types so yes they should work with all the numpy versions [1|https://numpy.org/devdocs/release/1.20.0-notes.html], [2|https://stackoverflow.com/questions/74844262/how-can-i-solve-error-module-numpy-has-no-attribute-float-in-python]. The error or warning comes from the call to the numpy.

 

The pull request: [https://github.com/apache/spark/pull/40220]

Best Regards,
Aimilios



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