Pyspark replace NaN with NULL

I use Spark to perform data transformations that I load into Redshift. Redshift does not support NaN values, so I need to replace all occurrences of NaN with NULL.

I tried something like this:

some_table = sql('SELECT * FROM some_table')
some_table = some_table.na.fill(None)

But I got the following error:

ValueError: value should be a float, int, long, string, bool or dict

So it seems like na.fill() doesn't support None. I specifically need to replace with NULL, not some other value, like 0.

I finally found the answer after Googling around a bit.

import pyspark.sql.functions as F
df = spark.createDataFrame([(1, float('nan')), (None, 1.0)], ("a", "b"))
df.show()

+----+---+
|   a|  b|
+----+---+
|   1|NaN|
|null|1.0|
+----+---+

for column in df.columns:
    df = (
         # usage of "isnan()" function to test and replace values
         df.withColumn(column,
                   F.when(F.isnan(F.col(column)), F.lit(None))
                   .otherwise(F.col(column)))
    )

sqlContext.registerDataFrameAsTable(df, "df2")
sql('select * from df2').show()

+----+----+
|   a|   b|
+----+----+
|   1|null|
|null| 1.0|
+----+----+

It doesn't use na.fill(), but it accomplished the same result, so I'm happy.