Pyspark - Looking to create a normalized version of a Double column

Question

As the title states, I'd like to create a normalized version of an existing Double column.

As I'm quite new to pyspark, this was my attempt at solving this:

df2 = df.groupBy('id').count().toDF(*['id','count_trans'])
df2 = df2.withColumn('count_trans_norm', F.col('count_trans) / (F.max(F.col('count_trans'))))

When I do this, I get the following error:

"grouping expressions sequence is empty, and '`movie_id`' is not an aggregate function.

Any help would be much appreciated.

mck · Accepted Answer · 2021-01-27 18:48:27Z

1

You need to specify an empty window if you want to get the maximum of count_trans in df2:

df2 = df.groupBy('id').count().toDF(*['id','count_trans'])
df3 = df2.selectExpr('*', 'count_trans / max(count_trans) over () as count_trans_norm')

Or if you prefer pyspark syntax:

from pyspark.sql import functions as F, Window

df3 = df2.withColumn('count_trans_norm', F.col('count_trans') / F.max(F.col('count_trans')).over(Window.orderBy()))

answered Jan 27, 2021 at 18:48

mck

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