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https://issues.apache.org/jira/browse/SPARK-37697?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17627254#comment-17627254
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Douglas Moore commented on SPARK-37697:
---------------------------------------
This fails with the same value error:
{code:java}
spark.createDataFrame(np.array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
[60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
[70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
[80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
[90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])).show() {code}
> Make it easier to convert numpy arrays to Spark Dataframes
> ----------------------------------------------------------
>
> Key: SPARK-37697
> URL: https://issues.apache.org/jira/browse/SPARK-37697
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 3.1.2
> Reporter: Douglas Moore
> Priority: Major
> Attachments: image-2022-10-31-22-49-37-356.png
>
>
> Make it easier to convert numpy arrays to dataframes.
> Often we receive errors:
>
> {code:java}
> df = spark.createDataFrame(numpy.arange(10))
> Can not infer schema for type: <class 'numpy.int64'>
> {code}
>
> OR
> {code:java}
> df = spark.createDataFrame(numpy.arange(10.))
> Can not infer schema for type: <class 'numpy.float64'>
> {code}
>
> Today (Spark 3.x) we have to:
> {code:java}
> spark.createDataFrame(pd.DataFrame(numpy.arange(10.))) {code}
> Make this easier with a direct conversion from Numpy arrays to Spark
> Dataframes.
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