There is an isNotNull function on any column.

df._1.isNotNull

or

from pyspark.sql.functions import *
col("myColumn").isNotNull

On Wed, Jul 1, 2015 at 3:07 AM, Olivier Girardot <ssaboum@gmail.com> wrote:
I must admit I've been using the same "back to SQL" strategy for now :p
So I'd be glad to have insights into that too.

Le mar. 30 juin 2015 à 23:28, pedro <ski.rodriguez@gmail.com> a écrit :
I am trying to find what is the correct way to programmatically check for
null values for rows in a dataframe. For example, below is the code using
pyspark and sql:

df = sqlContext.createDataFrame(sc.parallelize([(1, None), (2, "a"), (3,
"b"), (4, None)]))
df.where('_2 is not null').count()

However, this won't work
df.where(df._2 != None).count()

It seems there is no native Python way with DataFrames to do this, but I
find that difficult to believe and more likely that I am missing the "right
way" to do this.



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