Hi @vinita shinde​ 

I don't know if It meets your requirement, but you can try something like that

import pyspark.sql.functions as F
from pyspark.sql import Row
 
def is_different(col1, col2, cols):
  if col2 in cols:
    return F.col(col1) == F.col(col2)
  else:
    return F.col(col1).alias(col1)
 
def find_difference(data1, data2, id_columns):
  cols = data1.columns
  data_difference = data1.subtract(data2) # First you find the difference in rows
  data_difference = data_difference.join(data2.select(*[F.col(i).alias(f'b_{i}') if i not in id_columns else F.col(i).alias(i) for i in cols]), on = id_columns, how = 'left')
  # you join the differences with the second dataframe
  return data_difference.select(*[is_different(x, f"b_{x}",data_difference.columns) for x in cols]) # then you find out which columns cause the differences
 
# df1_spark first dataframe
# df2_spark second datafame
# third option is a list of ids of the Row
display(find_difference(df1_spark, df2_spark, ['ID'] ))
  

The results will be somethong like that

Let me know if It meets your requirements