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https://issues.apache.org/jira/browse/SPARK-39405?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xinrong Meng updated SPARK-39405:
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Description:
NumPy is the fundamental package for scientific computing with Python. It is
very commonly used, especially in the data science world. For example, Pandas
is backed by NumPy, and Tensors also supports interchangeable conversion
from/to NumPy arrays.
However, PySpark only supports Python built-in types with the exception of
“SparkSession.createDataFrame(pandas.DataFrame)” and “DataFrame.toPandas”.
This issue has been raised multiple times internally and externally, see also
SPARK-2012, SPARK-37697, SPARK-31776, and SPARK-6857.
With the NumPy support in SQL, we expect more adaptations from naive data
scientists and newcomers leveraging their existing background and codebase with
NumPy.
See more at [NumPy support in
SQL|https://docs.google.com/document/d/1ZC3e-GpvpoQFtEFnwct0me1XPsiwFf_qu4nRdKCpMBg/edit#].
was:
NumPy is the fundamental package for scientific computing with Python. It is
very commonly used, especially in the data science world. For example, Pandas
is backed by NumPy, and Tensors also supports interchangeable conversion
from/to NumPy arrays.
However, PySpark only supports Python built-in types with the exception of
“SparkSession.createDataFrame(pandas.DataFrame)” and “DataFrame.toPandas”.
This issue has been raised multiple times internally and externally, see also
SPARK-2012, SPARK-37697, SPARK-31776, and SPARK-6857.
With the NumPy support in SQL, we expect more adaptations from naive data
scientists and newcomers leveraging their existing background and codebase with
NumPy.
See more at [].
> NumPy support in SQL
> --------------------
>
> Key: SPARK-39405
> URL: https://issues.apache.org/jira/browse/SPARK-39405
> Project: Spark
> Issue Type: Umbrella
> Components: PySpark
> Affects Versions: 3.4.0
> Reporter: Xinrong Meng
> Priority: Major
>
> NumPy is the fundamental package for scientific computing with Python. It is
> very commonly used, especially in the data science world. For example, Pandas
> is backed by NumPy, and Tensors also supports interchangeable conversion
> from/to NumPy arrays.
>
> However, PySpark only supports Python built-in types with the exception of
> “SparkSession.createDataFrame(pandas.DataFrame)” and “DataFrame.toPandas”.
>
> This issue has been raised multiple times internally and externally, see also
> SPARK-2012, SPARK-37697, SPARK-31776, and SPARK-6857.
>
> With the NumPy support in SQL, we expect more adaptations from naive data
> scientists and newcomers leveraging their existing background and codebase
> with NumPy.
>
> See more at [NumPy support in
> SQL|https://docs.google.com/document/d/1ZC3e-GpvpoQFtEFnwct0me1XPsiwFf_qu4nRdKCpMBg/edit#].
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