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https://issues.apache.org/jira/browse/SPARK-22250?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16200309#comment-16200309
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Hyukjin Kwon commented on SPARK-22250:
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{{createDataFrame(... verifySchema=False)}}?
> Be less restrictive on type checking
> ------------------------------------
>
> Key: SPARK-22250
> URL: https://issues.apache.org/jira/browse/SPARK-22250
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.0.0
> Reporter: Fernando Pereira
> Priority: Minor
>
> I find types.py._verify_type() often too restrictive. E.g.
> {code}
> TypeError: FloatType can not accept object 0 in type <type 'int'>
> {code}
> I believe it would be globally acceptable to fill a float field with an int,
> especially since in some formats (json) you don't have a way of inferring the
> type correctly.
> Another situation relates to other equivalent numerical types, like
> array.array or numpy. A numpy scalar int is not accepted as an int, and these
> arrays have always to be converted down to plain lists, which can be
> prohibitively large and computationally expensive.
> Any thoughts?
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