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

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|
+-------+----------+---+

To make sure it does not fail for string, date and timestamp columns:

import pyspark.sql.functions as F
def count_missings(spark_df,sort=True):
    """
    Counts number of nulls and nans in each column
    """
    df = spark_df.select([F.count(F.when(F.isnan(c) | F.isnull(c), c)).alias(c) for (c,c_type) in spark_df.dtypes if c_type not in ('timestamp', 'string', 'date')]).toPandas()

    if len(df) == 0:
        print("There are no any missing values!")
        return None

    if sort:
        return df.rename(index={0: 'count'}).T.sort_values("count",ascending=False)

    return df

If you want to see the columns sorted based on the number of nans and nulls in descending:

count_missings(spark_df)

# | Col_A | 10 |
# | Col_C | 2  |
# | Col_B | 1  | 

If you don't want ordering and see them as a single row:

count_missings(spark_df, False)
# | Col_A | Col_B | Col_C |
# |  10   |   1   |   2   |

Here is my one liner. Here 'c' is the name of the column

df.select('c').withColumn('isNull_c',F.col('c').isNull()).where('isNull_c = True').count()