[SPARK-40012][PYTHON][DOCS] Make pyspark.sql.dataframe examples self-contained #37702
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cc @zhengruifeng @viirya @xinrong-meng @Yikun @itholic @dcoliversun @khalidmammadov in case you find some time to take a look. |
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@HyukjinKwon, thanks for taking this over.. I hope I helped you guys a bit atleast |
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Thanks guys. Merged to master. |
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Sure, that was a big help @Transurgeon |
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### What changes were proposed in this pull request? This pr upgrade Apache Arrow from 13.0.0 to 14.0.0. ### Why are the changes needed? The Apache Arrow 14.0.0 release brings a number of enhancements and bug fixes. In terms of bug fixes, the release addresses several critical issues that were causing failures in integration jobs with Spark([GH-36332](apache/arrow#36332)) and problems with importing empty data arrays([GH-37056](apache/arrow#37056)). It also optimizes the process of appending variable length vectors([GH-37829](apache/arrow#37829)) and includes C++ libraries for MacOS AARCH 64 in Java-Jars([GH-38076](apache/arrow#38076)). The new features and improvements focus on enhancing the handling and manipulation of data. This includes the introduction of DefaultVectorComparators for large types([GH-25659](apache/arrow#25659)), support for extended expressions in ScannerBuilder([GH-34252](apache/arrow#34252)), and the exposure of the VectorAppender class([GH-37246](apache/arrow#37246)). The release also brings enhancements to the development and testing process, with the CI environment now using JDK 21([GH-36994](apache/arrow#36994)). In addition, the release introduces vector validation consistent with C++, ensuring consistency across different languages([GH-37702](apache/arrow#37702)). Furthermore, the usability of VarChar writers and binary writers has been improved with the addition of extra input methods([GH-37705](apache/arrow#37705)), and VarCharWriter now supports writing from `Text` and `String`([GH-37706](apache/arrow#37706)). The release also adds typed getters for StructVector, improving the ease of accessing data([GH-37863](apache/arrow#37863)). The full release notes as follows: - https://arrow.apache.org/release/14.0.0.html ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Pass GitHub Actions ### Was this patch authored or co-authored using generative AI tooling? No Closes #43650 from LuciferYang/arrow-14. Lead-authored-by: yangjie01 <[email protected]> Co-authored-by: YangJie <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
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What changes were proposed in this pull request?
This PR takes #37444 over with covering all examples in
pyspark.sql.dataframe.This PR proposes to improve the examples in
pyspark.sql.dataframeby making each example self-contained with more realistic examples.Closes #37444
Why are the changes needed?
To make the documentation more readable and able to copy and paste directly in PySpark shell.
Does this PR introduce any user-facing change?
Yes, Documentation changes only
How was this patch tested?
Manually ran each examples.