Thanks for the tip. Any idea why the intuitive answer doesn't work ( !=
None)? I inspected the Row columns and they do indeed have a None value. I
would suspect that somehow Python's None is translated to something in jvm
which doesn't equal to null?
I might check out the source code for a better idea as well
Pedro
On Wed, Jul 1, 2015 at 12:18 PM, Michael Armbrust <mich...@databricks.com>
wrote:
> 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 <ssab...@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.rodrig...@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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>
--
Pedro Rodriguez
UCBerkeley 2014 | Computer Science
SnowGeek <http://SnowGeek.org>
pedro-rodriguez.com
ski.rodrig...@gmail.com
208-340-1703
