How to find count of Null and Nan values for each column in a PySpark dataframe efficiently?

Question

import numpy as np

df = spark.createDataFrame(
    [(1, 1, None), (1, 2, float(5)), (1, 3, np.nan), (1, 4, None), (1, 5, float(10)), (1, 6, float(\'nan\')), (1, 6,          


        

Answer 1

You can use method shown here and replace isNull with isnan:

from pyspark.sql.functions import isnan, when, count, col

df.select([count(when(isnan(c), c)).alias(c) for c in df.columns]).show()
+-------+----------+---+
|session|timestamp1|id2|
+-------+----------+---+
|      0|         0|  3|
+-------+----------+---+

or

df.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in df.columns]).show()
+-------+----------+---+
|session|timestamp1|id2|
+-------+----------+---+
|      0|         0|  5|
+-------+----------+---+