[jira] [Commented] (SPARK-13943) The behavior of sum(booleantype) in Spark DataFrames is not intuitive

["Liang-Chi Hsieh (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-13943?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15203941#comment-15203941 ] 

Liang-Chi Hsieh commented on SPARK-13943:
-----------------------------------------

Currently we seems don't support implicit type conversion between BooleanType and NumericType such as interger or long. Do we want to do that? cc [~rxin]

> The behavior of sum(booleantype) in Spark DataFrames is not intuitive
> ---------------------------------------------------------------------
>
>                 Key: SPARK-13943
>                 URL: https://issues.apache.org/jira/browse/SPARK-13943
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>            Reporter: Wes McKinney
>
> In NumPy and pandas, summing boolean data produces an integer indicating the number of True values:
> {code}
> In [1]: import numpy as np
> In [2]: arr = np.random.randn(1000000)
> In [3]: (arr > 0).sum()
> Out[3]: 499065
> {code}
> In PySpark, {{sql.functions.sum(expr)}} results in an error:
> {code}
> AnalysisException: u"cannot resolve 'sum((`data0` > CAST(0 AS DOUBLE)))' due to data type mismatch: function sum requires numeric types, not BooleanType;"
> {code}
> FWIW, R is the same:
> {code}
> > sum(rnorm(1000000) > 0)
> [1] 499139
> {code}
> Spark should consider emulating the behavior of R and Python in those environments. 



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