How to return rows with Null values in pyspark dataframe?

Question

I am trying to get the rows with null values from a pyspark dataframe. In pandas, I can achieve this using isnull() on the dataframe:

df = df[df.isnull().any(axis=1)]

But in case of PySpark, when I am running below command it shows Attributeerror:

df.filter(df.isNull())

AttributeError: 'DataFrame' object has no attribute 'isNull'.

How can get the rows with null values without checking it for each column?

Answer 1

You can filter the rows with where, reduce and a list comprehension. For example, given the following dataframe:

df = sc.parallelize([
    (0.4, 0.3),
    (None, 0.11),
    (9.7, None), 
    (None, None)
]).toDF(["A", "B"])

df.show()
+----+----+
|   A|   B|
+----+----+
| 0.4| 0.3|
|null|0.11|
| 9.7|null|
|null|null|
+----+----+

Filtering the rows with some null value could be achieved with:

import pyspark.sql.functions as f
from functools import reduce

df.where(reduce(lambda x, y: x | y, (f.col(x).isNull() for x in df.columns))).show()

Which gives:

+----+----+
|   A|   B|
+----+----+
|null|0.11|
| 9.7|null|
|null|null|
+----+----+

In the condition statement you have to specify if any (or, |), all (and, &), etc.