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https://issues.apache.org/jira/browse/SPARK-22250?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16266642#comment-16266642
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Fernando Pereira commented on SPARK-22250:
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[~bryanc] It could help, but it doesn't solve the problem. If we have SQL field
that is an Array, the best equivalent representation from the Python side would
be a plain Numpy array, given that lists are not efficient.
When building a dataframe in our projects we have use-cases that would
immensely benefit from such support.
>From dataframe to Python returning Array fields as Numpy IMHO would be better,
>but also changes behavior, so it might be trickier to support. We could
>eventually control that by detecting if Numpy is available in the system,
>otherwise raise a warning and fall back to use plain lists.
What do the developers think?
> 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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