Q: Calculate number of columns with missing values per each row in PySpark Let see we have the following data set
columns = ['id', 'dogs', 'cats']
values = [(1, 2, 0),(2, None, None),(3, None,9)]
df = spark.createDataFrame(values,columns)
df.show()
+----+----+----+
|  id|dogs|cats|
+----+----+----+
|   1|   2|   0|
|   2|null|null|
|   3|null|   9|
+----+----+----+

I would like to calculate number ("miss_nb") and percents ("miss_pt") of columns with missing values per rows and get the following table
+----+-------+-------+
|  id|miss_nb|miss_pt|
+----+-------+-------+
|   1|      0|   0.00|
|   2|      2|   0.67|
|   3|      1|   0.33|
+----+-------+-------+

The number of columns should be any (non-fixed list).
How to do it?
Thanks!
