Sum operation on PySpark DataFrame giving TypeError when type is fine

Question

I have such DataFrame in PySpark (this is the result of a take(3), the dataframe is very big):

sc = SparkContext() df = [Row(owner=u'u1', a_d=0.1), Row(owner=u'u2', a_d=0.0), Row(owner=u'u1', a_d=0.3)] 

the same owner will have more rows. What I need to do is summing the values of the field a_d per owner, after grouping, as

b = df.groupBy('owner').agg(sum('a_d').alias('a_d_sum')) 

but this throws error

TypeError: unsupported operand type(s) for +: 'int' and 'str'

However, the schema contains double values, not strings (this comes from a printSchema()):

root |-- owner: string (nullable = true) |-- a_d: double (nullable = true) 

So what is happening here?

Answer 1

You are not using the correct sum function but the built-in function sum (by default).

So the reason why the build-in function won't work is that's it takes an iterable as an argument where as here the name of the column passed is a string and the built-in function can't be applied on a string. Ref. Python Official Documentation.

You'll need to import the proper function from pyspark.sql.functions :

from pyspark.sql import Row from pyspark.sql.functions import sum as _sum  df = sqlContext.createDataFrame(     [Row(owner=u'u1', a_d=0.1), Row(owner=u'u2', a_d=0.0), Row(owner=u'u1', a_d=0.3)] )  df2 = df.groupBy('owner').agg(_sum('a_d').alias('a_d_sum')) df2.show()  # +-----+-------+ # |owner|a_d_sum| # +-----+-------+ # |   u1|    0.4| # |   u2|    0.0| # +-----+-------+