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The following commit(s) were added to refs/heads/master by this push:
     new 9823daf2cf90 [SPARK-53931][INFRA][PYTHON] Fix scheduled job for numpy 
2.1.3
9823daf2cf90 is described below

commit 9823daf2cf90f7c0b4978c0ae574b580a54ef8c2
Author: Ruifeng Zheng <[email protected]>
AuthorDate: Thu Oct 16 13:26:56 2025 +0800

    [SPARK-53931][INFRA][PYTHON] Fix scheduled job for numpy 2.1.3
    
    ### What changes were proposed in this pull request?
    Fix scheduled job for numpy 2.1.3
    
    ### Why are the changes needed?
    to fix 
https://github.com/apache/spark/actions/runs/18538043179/job/52838303733
    
    it was caused by a bug in 19.0.0, see 
https://github.com/apache/arrow/issues/45283
    
    ### Does this PR introduce _any_ user-facing change?
    no, infra-only
    
    ### How was this patch tested?
    PR builder with
    ```
    default: '{"PYSPARK_IMAGE_TO_TEST": "numpy-213", "PYTHON_TO_TEST": 
"python3.11"}'
    ```
    
    see 
https://github.com/zhengruifeng/spark/actions/runs/18527303212/job/52801019275
    
    ### Was this patch authored or co-authored using generative AI tooling?
    no
    
    Closes #52633 from zhengruifeng/restore_numpy_213.
    
    Authored-by: Ruifeng Zheng <[email protected]>
    Signed-off-by: Ruifeng Zheng <[email protected]>
---
 dev/spark-test-image/numpy-213/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/spark-test-image/numpy-213/Dockerfile 
b/dev/spark-test-image/numpy-213/Dockerfile
index af1a19c4859c..116154b663b0 100644
--- a/dev/spark-test-image/numpy-213/Dockerfile
+++ b/dev/spark-test-image/numpy-213/Dockerfile
@@ -68,7 +68,7 @@ RUN apt-get update && apt-get install -y \
 
 
 # Pin numpy==2.1.3
-ARG BASIC_PIP_PKGS="numpy==2.1.3 pyarrow==19.0.0 six==1.16.0 pandas==2.2.3 
scipy plotly<6.0.0 mlflow>=2.8.1 coverage matplotlib openpyxl 
memory-profiler>=0.61.0 scikit-learn>=1.3.2"
+ARG BASIC_PIP_PKGS="numpy==2.1.3 pyarrow>=21.0.0 six==1.16.0 pandas==2.2.3 
scipy plotly<6.0.0 mlflow>=2.8.1 coverage matplotlib openpyxl 
memory-profiler>=0.61.0 scikit-learn>=1.3.2"
 # Python deps for Spark Connect
 ARG CONNECT_PIP_PKGS="grpcio==1.67.0 grpcio-status==1.67.0 protobuf==5.29.5 
googleapis-common-protos==1.65.0 graphviz==0.20.3"
 


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