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https://issues.apache.org/jira/browse/SPARK-13946?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15270151#comment-15270151
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Niranjan Molkeri` commented on SPARK-13946:
-------------------------------------------
Hi, I ran the following code.
{noformat}
import numpy as np
import pandas as pd
from pyspark import SparkContext
from pyspark.sql import SQLContext
sc = SparkContext(appName="fooAPP")
sqlContext = SQLContext(sc)
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()
sdfCount = sdf.count()
sdf2Count = sdf2.count()
{noformat}
sdf.count() returns 1000000
sdf2.count() returns avg around 500000
can you tell me what is "F" in
{noformat}
sdf.agg(F.count(sdf2.foo)).show()
{noformat}
So that I can further test have a look into the issue.
Thank you.
> 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
>
> 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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