adith-os opened a new pull request, #55783:
URL: https://github.com/apache/spark/pull/55783
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### What changes were proposed in this pull request?
This PR adds support for PyArrow-backed dtypes (e.g., bool[pyarrow]) in
PySpark's pandas API. The changes include:
- Type detection and conversion: Added is_pyarrow_backed_dtype() function to
detect PyArrow-backed dtypes and enhanced as_spark_type() to convert them to
appropriate Spark types.
- Dtype preservation: Enhanced spark_type_to_pandas_dtype() with a
use_arrow_dtypes parameter to preserve PyArrow dtypes when converting from
Spark types back to pandas dtypes.
- Propagation through operations: Updated comparison operators in
data_type_ops/base.py to preserve PyArrow dtypes in results, and propagated the
use_arrow_dtypes parameter through InternalField.from_struct_field() and
related code paths.
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### Why are the changes needed?
Starting with pandas 3.0, when PyArrow is installed, pandas automatically
uses PyArrow-backed dtypes for string columns. Without this PR, PySpark's
pandas API loses dtype information when working with PyArrow-backed dtypes
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### Does this PR introduce _any_ user-facing change?
Yes. Users working with PyArrow-backed dtypes will now see their dtypes
preserved through PySpark operations
Before the PR:
```
>>> 26/05/09 19:25:26 WARN GarbageCollectionMetrics: To enable non-built-in
garbage collector(s) List(scavenge), users should configure it(them) to
spark.eventLog.gcMetrics.youngGenerationGarbageCollectors or
spark.eventLog.gcMetrics.oldGenerationGarbageCollectors
26/05/09 19:25:26 WARN GarbageCollectionMetrics: To enable non-built-in
garbage collector(s) List(global, scavenge), users should configure it(them) to
spark.eventLog.gcMetrics.youngGenerationGarbageCollectors or
spark.eventLog.gcMetrics.oldGenerationGarbageCollectors
import warnings
>>> warnings.filterwarnings('ignore')
>>>
>>> import pandas as pd
>>> print(f"pandas: {pd.__version__}")
pandas: 3.0.2
>>>
>>> import pyspark.pandas as ps
>>> s1 = ps.Series(['a', 'b', 'c'], dtype='string')
>>> s2 = ps.Series(['a', 'x', 'c'], dtype='string')
>>> result = s1 == s2
>>> print(f"result dtype: {result.dtype}")
result dtype: boolean
>>> print(result)
0 True
1 False
2 True
dtype: boolean
>>>
```
After the PR:
```
>>> 26/05/09 19:19:05 WARN GarbageCollectionMetrics: To enable non-built-in
garbage collector(s) List(scavenge), users should configure it(them) to
spark.eventLog.gcMetrics.youngGenerationGarbageCollectors or
spark.eventLog.gcMetrics.oldGenerationGarbageCollectors
26/05/09 19:19:05 WARN GarbageCollectionMetrics: To enable non-built-in
garbage collector(s) List(global, scavenge), users should configure it(them) to
spark.eventLog.gcMetrics.youngGenerationGarbageCollectors or
spark.eventLog.gcMetrics.oldGenerationGarbageCollectors
>>> import warnings
>>> warnings.filterwarnings('ignore')
>>>
>>> import pandas as pd
>>> print(f"pandas: {pd.__version__}")
pandas: 3.0.2
>>>
>>> import pyspark.pandas as ps
>>> s1 = ps.Series(['a', 'b', 'c'], dtype='string')
s2 = ps.Series(['a', 'x', 'c'], dtype='string')
result = s1 == s2
print(f"result dtype: {result.dtype}")
print(result)
>>> s2 = ps.Series(['a', 'x', 'c'], dtype='string')
>>> result = s1 == s2
>>> print(f"result dtype: {result.dtype}")
result dtype: bool[pyarrow]
>>> print(result)
0 True
1 False
2 True
dtype: bool[pyarrow]
>>>
```
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### How was this patch tested?
New tests in python/pyspark/pandas/tests/test_typedef.py:
- test_as_spark_type_pyarrow_dtypes() - Tests conversion to Spark types
- test_spark_type_to_pandas_dtype_with_arrow_flag() - Tests conversion back
to pandas dtypes
- test_is_str_dtype_with_pyarrow() - Tests string dtype detection
- test_is_pyarrow_backed_dtype() - Tests PyArrow dtype detection
Integration test in
python/pyspark/pandas/tests/data_type_ops/test_string_ops.py:
- test_pyarrow_backed_string_comparisons() - Tests dtype preservation in
comparison operations
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### Was this patch authored or co-authored using generative AI tooling?
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Yes. Generated-by: GPT 5.5
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