Adding a new column in Data Frame derived from other columns (Spark)

Question

I\'m using Spark 1.3.0 and Python. I have a dataframe and I wish to add an additional column which is derived from other columns. Like this,

>>old_df.c         


        

Answer 1

Additionally, we can use udf

from pyspark.sql.functions import udf,col
from pyspark.sql.types import IntegerType
from pyspark import SparkContext
from pyspark.sql import SQLContext

sc = SparkContext()
sqlContext = SQLContext(sc)
old_df = sqlContext.createDataFrame(sc.parallelize(
    [(0, 1), (1, 3), (2, 5)]), ('col_1', 'col_2'))
function = udf(lambda col1, col2 : col1-col2, IntegerType())
new_df = old_df.withColumn('col_n',function(col('col_1'), col('col_2')))
new_df.show()

Answer 2

One way to achieve that is to use withColumn method:

old_df = sqlContext.createDataFrame(sc.parallelize(
    [(0, 1), (1, 3), (2, 5)]), ('col_1', 'col_2'))

new_df = old_df.withColumn('col_n', old_df.col_1 - old_df.col_2)

Alternatively you can use SQL on a registered table:

old_df.registerTempTable('old_df')
new_df = sqlContext.sql('SELECT *, col_1 - col_2 AS col_n FROM old_df')