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https://issues.apache.org/jira/browse/SPARK-13946?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-13946:
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Labels: bulk-closed (was: )
> PySpark DataFrames allows you to silently use aggregate expressions derived
> from different table expressions
> ------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-13946
> URL: https://issues.apache.org/jira/browse/SPARK-13946
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Reporter: Wes McKinney
> Priority: Major
> Labels: bulk-closed
>
> In my opinion, this code should raise an exception rather than silently
> discarding the predicate:
> {code}
> import numpy as np
> import pandas as pd
> df = pd.DataFrame({'foo': np.random.randn(1000000),
> 'bar': np.random.randn(1000000)})
> sdf = sqlContext.createDataFrame(df)
> sdf2 = sdf[sdf.bar > 0]
> sdf.agg(F.count(sdf2.foo)).show()
> +----------+
> |count(foo)|
> +----------+
> | 1000000|
> +----------+
> {code}
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