pyspark dataframe, groupby and compute variance of a column

Question

I would like to groupby a pyspark dataframe and compute the variance of a specific column. For the average this is quite easy and can be done like this

from pyspark.sql import functions as func
AVERAGES=df.groupby('country').agg(func.avg('clicks').alias('avg_clicks')).collect()

however for the variance there seem not to be any aggregation function in the function sub-module (I am also wondering why since this is quite a common operation)

Answer 1

What you can do is convert the dataframe to an RDD object and then use the provided variance function for RDDs.

df1 = df.groupby('country').agg(func.avg('clicks').alias('avg_clicks'))
rdd = df1.rdd
rdd.variance()

Answer 2

As standard deviation is square root of variance a pure PySpark dataframe solution is :

df = sc.parallelize(((.1, 2.0), (.3, .2))).toDF()
df.show()
varianceDF = df.select(stddev('_1') * stddev('_1'))
varianceDF.show()