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https://issues.apache.org/jira/browse/ARROW-12751?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17349477#comment-17349477
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David Li commented on ARROW-12751:
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Just to clarify, do we want to propagate nulls or ignore them? (NumPy raises an
error which is also an option)
{noformat}
minimum([1, 2, 3], [null, 1, 1]) = [null, 1, 1] # Propagate
minimum([1, 2, 3], Scalar(null)) = [null, null, null]
minimum([1, 2, 3], [null, 1, 1]) = [1, 1, 1] # Ignore
minimum([1, 2, 3], Scalar(null)) = [1, 2, 3]{noformat}
Also, what do we do with 0 arguments?
{noformat}
minimum(Scalar(null)) = null or error?
minimum() = [], NullScalar, or error? {noformat}
> [C++] Add variadic row-wise min/max kernels (least/greatest)
> ------------------------------------------------------------
>
> Key: ARROW-12751
> URL: https://issues.apache.org/jira/browse/ARROW-12751
> Project: Apache Arrow
> Issue Type: Improvement
> Components: C++
> Reporter: Ian Cook
> Assignee: David Li
> Priority: Major
>
> Add a pair of variadic functions equivalent to SQL's {{least}}/{{greatest}}
> or R's {{pmin}}/{{pmax}}. Should take 0, 1, 2, ... same-length numeric arrays
> as input and return an array giving the minimum/maximum of the values found
> in each position of the input arrays. For example, in the case of these 2
> input arrays:
> {code:java}
> Array<double> Array<double>
> [ [
> 1, 2,
> 4 3
> ] ]
> {code}
> {{least}} would return:
> {code:java}
> Array<double>
> [
> 1,
> 3
> ]
> {code}
> and {{greatest}} would return
> {code:java}
> Array<double>
> [
> 2,
> 4
> ]
> {code}
> The returned array should have the same data type as the input arrays, or
> follow promotion rules if the numeric types of the input arrays differ.
> Should also accept scalar numeric inputs and recycle their values.
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