I have a pyspark dataframe with a column of numbers. I need to sum that column and then have the result return as an int in a python variable.

df = spark.createDataFrame([("A", 20), ("B", 30), ("D", 80)],["Letter", "Number"])

I do the following to sum the column.

df.groupBy().sum()

But I get a dataframe back.

+-----------+
|sum(Number)|
+-----------+
| 130|
+-----------+

I would 130 returned as an int stored in a variable to be used else where in the program.

result = 130

9 Answers

I think the simplest way:

df.groupBy().sum().collect()

will return a list. In your example:

In [9]: df.groupBy().sum().collect()[0][0]
Out[9]: 130

1

The simplest way really :

df.groupBy().sum().collect()

But it is very slow operation: Avoid groupByKey, you should use RDD and reduceByKey:

df.rdd.map(lambda x: (1,x[1])).reduceByKey(lambda x,y: x + y).collect()[0][1]

I tried on a bigger dataset and i measured the processing time:

RDD and ReduceByKey : 2.23 s

GroupByKey: 30.5 s

1

If you want a specific column :

import pyspark.sql.functions as F
df.agg(F.sum("my_column")).collect()[0][0]

4

This is another way you can do this. using agg and collect:

sum_number = df.agg({"Number":"sum"}).collect()[0]
result = sum_number["sum(Number)"]

Similar to other answers, but without the use of a groupby or agg. I just select the column in question, sum it, collect it, and then grab the first two indices to return an int. The only reason I chose this over the accepted answer is I am new to pyspark and was confused that the 'Number' column was not explicitly summed in the accepted answer. If I had to come back after sometime and try to understand what was happening, syntax such as below would be easier for me to follow.

import pyspark.sql.functions as f
df.select(f.sum('Number')).collect()[0][0]

1

You can also try using first() function. It returns the first row from the dataframe, and you can access values of respective columns using indices.

df.groupBy().sum().first()[0]

In your case, the result is a dataframe with single row and column, so above snippet works.

Select column as RDD, abuse keys() to get value in Row (or use .map(lambda x: x[0])), then use RDD sum:

df.select("Number").rdd.keys().sum()

SQL sum using selectExpr:

df.selectExpr("sum(Number)").first()[0]

The following should work:

df.groupBy().sum().rdd.map(lambda x: x[0]).collect()

sometimes read a csv file to pyspark Dataframe, maybe the numeric column change to string type '23',like this, you should use pyspark.sql.functions.sum to get the result as int , not sum()

import pyspark.sql.functions as F
df.groupBy().agg(F.sum('Number')).show()

0

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