how to loop through each row of dataFrame in pyspark

Question

E.g

sqlContext = SQLContext(sc)

sample=sqlContext.sql("select Name ,age ,city from user")
sample.show()

The above statement print entire table on terminal but i want to access each row in that table using for or while to perform further calculations .

Answer 1

To "loop" and take advantage of Spark's parallel computation framework, you could define a custom function and use map.

def customFunction(row):

   return (row.name, row.age, row.city)

sample2 = sample.rdd.map(customFunction)

or

sample2 = sample.rdd.map(lambda x: (x.name, x.age, x.city))

The custom function would then be applied to every row of the dataframe. Note that sample2 will be a RDD, not a dataframe.

Map may be needed if you are going to perform more complex computations. If you just need to add a simple derived column, you can use the withColumn, with returns a dataframe.

sample3 = sample.withColumn('age2', sample.age + 2)