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59c3872312dde7a4da10485a4b5cf5299dd71c5e
[log]
author
Guja <127162872+GujaLomsadze@users.noreply.github.com>
Sat Jul 18 16:39:29 2026 +0200
committer
GitHub <noreply@github.com>
Sat Jul 18 16:39:29 2026 +0200
tree
af5725414b84556e60fd479300352a40db2178e5
parent
a6897f4a03f52fff8d09df3c4bd38716677fcc54 [diff]
GH-49255: [Python] Fix pandas Categorical DeprecationWarnings in tests (#50543)

### Rationale for this change

pandas 3.0.0 deprecated constructing or casting to a `Categorical` with
non-NA values that are not present in the specified `dtype.categories`. 
This surfaces as DeprecationWarnings in the PyArrow
test suite when run against pandas >= 3.0 (GH-49255), in test_category
and test_category_implicit_from_pandas.

### What changes are included in this PR?

- test_category: remove the `cat_strings_with_na` column (relied on
  silent 'qux' -> NaN; redundant, cat_strings already carries nulls).
- test_category_implicit_from_pandas: use explicit None
  (['a','b',None]) instead of out-of-category 'c'.

### Are these changes tested?
Yes - test-only. Verified no DeprecationWarning under pandas 3.0.3.

### Are there any user-facing changes?
No.

Closes #49255.
* GitHub Issue: #49255

Authored-by: GujaLomsadze <lomsadze.guja@gmail.com>
Signed-off-by: Rok Mihevc <rok@mihevc.org>
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  33. LICENSE.txt
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  35. README.md
README.md

Apache Arrow

Fuzzing Status License BlueSky Follow

Powering In-Memory Analytics

Apache Arrow is a universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics. It contains a set of technologies that enable data systems to efficiently store, process, and move data.

Major components of the project include:

The ↗ icon denotes that this component of the project is maintained in a separate repository.

Arrow is an Apache Software Foundation project. Learn more at arrow.apache.org.

What's in the Arrow libraries?

The reference Arrow libraries contain many distinct software components:

  • Columnar vector and table-like containers (similar to data frames) supporting flat or nested types
  • Fast, language agnostic metadata messaging layer (using Google's FlatBuffers library)
  • Reference-counted off-heap buffer memory management, for zero-copy memory sharing and handling memory-mapped files
  • IO interfaces to local and remote filesystems
  • Self-describing binary wire formats (streaming and batch/file-like) for remote procedure calls (RPC) and interprocess communication (IPC)
  • Integration tests for verifying binary compatibility between the implementations (e.g. sending data from Java to C++)
  • Conversions to and from other in-memory data structures
  • Readers and writers for various widely-used file formats (such as Parquet, CSV)

Implementation status

The official Arrow libraries in this repository are in different stages of implementing the Arrow format and related features. See our current feature matrix on git main.

How to Contribute

Please read our latest project contribution guide.

If you are using AI coding tools, please review our AI-generated code guidance.

Getting involved

Even if you do not plan to contribute to Apache Arrow itself or Arrow integrations in other projects, we'd be happy to have you involved:

Continuous Integration Sponsors

We use runs-on for managing the project self-hosted runners. We use AWS for some of the required infrastructure for the project.