Replace empty strings with None/null values in DataFrame

It is as simple as this:

from pyspark.sql.functions import col, when

def blank_as_null(x):
    return when(col(x) != "", col(x)).otherwise(None)

dfWithEmptyReplaced = testDF.withColumn("col1", blank_as_null("col1"))

dfWithEmptyReplaced.show()
## +----+----+
## |col1|col2|
## +----+----+
## | foo|   1|
## |null|   2|
## |null|null|
## +----+----+

dfWithEmptyReplaced.na.drop().show()
## +----+----+
## |col1|col2|
## +----+----+
## | foo|   1|
## +----+----+

If you want to fill multiple columns you can for example reduce:

to_convert = set([...]) # Some set of columns

reduce(lambda df, x: df.withColumn(x, blank_as_null(x)), to_convert, testDF)

or use comprehension:

exprs = [
    blank_as_null(x).alias(x) if x in to_convert else x for x in testDF.columns]

testDF.select(*exprs)

If you want to specifically operate on string fields please check the answer by robin-loxley.