问题:

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.columns
[col_1, col_2, ..., col_m]

>>new_df.columns
[col_1, col_2, ..., col_m, col_n]

where

col_n = col_3 - col_4

How do I do this in PySpark?

回答1:

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')

回答2:

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()