Aggregating two columns with Pyspark - apache-spark

Question

Learning Apache Spark through PySpark and having issues.
I have the following DF:
+----------+------------+-----------+----------------+
| game_id|posteam_type|total_plays|total_touchdowns|
+----------+------------+-----------+----------------+
|2009092003| home| 90| 3|
|2010091912| home| 95| 0|
|2010112106| home| 75| 0|
|2010121213| home| 85| 3|
|2009092011| null| 9| null|
|2010110703| null| 2| null|
|2010112111| null| 6| null|
|2011100909| home| 102| 3|
|2011120800| home| 72| 2|
|2012010110| home| 74| 6|
|2012110410| home| 68| 1|
|2012120911| away| 91| 2|
|2011103008| null| 6| null|
|2012111100| null| 3| null|
|2013092212| home| 86| 6|
|2013112407| home| 73| 4|
|2013120106| home| 99| 3|
|2014090705| home| 94| 3|
|2014101203| home| 77| 4|
|2014102611| home| 107| 6|
+----------+------------+-----------+----------------+
I'm attempting to find the average number of plays it takes to score a TD or sum(total_plays)/sum(total_touchdowns).
I figured out the code to get the sums but can't figure out how to get the total average:
plays = nfl_game_play.groupBy().agg({'total_plays': 'sum'}).collect()
touchdowns = nfl_game_play.groupBy().agg({'total_touchdowns',: 'sum'}).collect()
As you can see I tried storing each as a variable but beyond just remembering what each value is and manually doing it.

Answer 1

Try with below code:
Example:
df.show()
#+-----------+----------------+
#|total_plays|total_touchdowns|
#+-----------+----------------+
#| 90| 3|
#| 95| 0|
#| 9| null|
#+-----------+----------------+
from pyspark.sql.functions import *
total_avg=df.groupBy().agg(sum("total_plays")/sum("total_touchdowns")).collect()[0][0]
#64.66666666666667