mar tin mar tin -4 years ago 103
Python Question

Sum operation on PySpark DataFrame giving TypeError when type is fine

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 Source

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

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|
# +-----+-------+
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