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, float('nan'))],
('session', "timestamp1", "id2"))
Expected output
dataframe with count of nan/null for each column
Note: The previous questions I found in stack overflow only checks for null & not nan. Thats why i have created a new question.
I know i can use isnull() function in spark to find number of Null values in Spark column but how to find Nan values in Spark dataframe?
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|
+-------+----------+---+