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dongjoon-hyun pushed a commit to branch branch-4.1
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The following commit(s) were added to refs/heads/branch-4.1 by this push:
     new 23c653101084 [SPARK-58047][4.2][DOC] Fix the required NumPy version to 
1.21 in MLlib guide
23c653101084 is described below

commit 23c653101084b0e8d7a5f5386dd03b38d11e8da4
Author: Dongjoon Hyun <[email protected]>
AuthorDate: Wed Jul 8 16:04:02 2026 -0700

    [SPARK-58047][4.2][DOC] Fix the required NumPy version to 1.21 in MLlib 
guide
    
    ### What changes were proposed in this pull request?
    
    This PR updates the minimum NumPy version in `docs/ml-guide.md` from `1.4` 
to `1.21`.
    
    ### Why are the changes needed?
    
    SPARK-45179 raised the minimum NumPy version to `1.21` at Apache Spark 
4.0.0.
    - https://github.com/apache/spark/pull/42944
    
    ### Does this PR introduce _any_ user-facing change?
    
    No.
    
    ### How was this patch tested?
    
    Manual review.
    
    ### Was this patch authored or co-authored using generative AI tooling?
    
    Generated-by: Claude Fable 5
    
    Closes #57133 from dongjoon-hyun/SPARK-58047.
    
    Authored-by: Dongjoon Hyun <[email protected]>
    Signed-off-by: Dongjoon Hyun <[email protected]>
    (cherry picked from commit 2a4349fac95c614f298badedc80f524d162dc4a9)
    Signed-off-by: Dongjoon Hyun <[email protected]>
---
 docs/ml-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 132805e7bcd6..918c2fc557c2 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -69,7 +69,7 @@ However, native acceleration libraries can't be distributed 
with Spark. See [MLl
 WARNING: Failed to load implementation from:dev.ludovic.netlib.blas.JNIBLAS
 ```
 
-To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 
1.4 or newer.
+To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 
1.21 or newer.
 
 [^1]: To learn more about the benefits and background of system optimised 
natives, you may wish to
     watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in 
Scala](http://fommil.github.io/scalax14/).


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