Simple Sales Prediction

03_averages
20160616

# Calculate the averages: `groupBy(..).agg(avg(..))`

Let's kick off the calculations, first we do the averages of x and y:

``    val a1 = sale_narrow_df.groupBy("shop","product").agg( avg("x"), avg("y") )``

Result:

``````a1.orderBy("shop","product").show()

+----------+-------+------------------+------------------+
|      shop|product|            avg(x)|            avg(y)|
+----------+-------+------------------+------------------+
|  megamart|  bread| 6.454545454545454|461.27272727272725|
|  megamart| cheese|               6.4|              56.4|
|  megamart|   milk|               6.7|              27.9|
|  megamart|   nuts|               6.5|1341.6666666666667|
|  megamart| razors| 6.909090909090909| 519.5454545454545|
|  megamart|   soap|               7.0|               8.0|
|superstore|  bread|6.2727272727272725|398.72727272727275|
|superstore| cheese|               6.9|              56.4|
|superstore|   milk|               7.3|              33.5|
|superstore|   nuts|               6.5|1279.1666666666667|
|superstore| razors|               6.5| 352.3333333333333|
|superstore|   soap|               6.0|               7.2|
+----------+-------+------------------+------------------+``````

## Naming convention

When short name chosen for a dataframe starts with an 'a' (eg a1,a2,...) it's about an aggregate (ie at shop/product level), and when it starts with a 'd' (eg. d1,d2,..) it's about a detail (ie. at the lowest level: shop/product/month~unit)

## Cosmetics

For the sake of this document: let's round off the above averages to retain just 4 digits after the decimal point, just to be able to fit more columns of data onto 1 row! When concerned with precision, please skip this step.

User defined rounding function:

``    def udfRound = udf[Double,Double] { (x) => Math.floor( x*10000.0 + 0.5 )/10000.0 } ``

Round off:

``````    val a2=a1.withColumn("avg_x", udfRound( a1("avg(x)") )).
withColumn("avg_y", udfRound( a1("avg(y)") )).
select("shop","product","avg_x","avg_y")``````

Result:

``````a2.orderBy("shop","product").show()

+----------+-------+------+---------+
|      shop|product| avg_x|    avg_y|
+----------+-------+------+---------+
|  megamart|  bread|6.4545| 461.2727|
|  megamart| cheese|   6.4|     56.4|
|  megamart|   milk|   6.7|     27.9|
|  megamart|   nuts|   6.5|1341.6666|
|  megamart| razors| 6.909| 519.5454|
|  megamart|   soap|   7.0|      8.0|
|superstore|  bread|6.2727| 398.7272|
|superstore| cheese|   6.9|     56.4|
|superstore|   milk|   7.3|     33.5|
|superstore|   nuts|   6.5|1279.1666|
|superstore| razors|   6.5| 352.3333|
|superstore|   soap|   6.0|      7.2|
+----------+-------+------+---------+``````

'Glue' these averages onto dataframe `sale_narrow_df`, using a left-outer join :

``    val d1= sale_narrow_df.join( a2, Seq("shop","product"), "leftouter")``

Result:

``````d1.orderBy("shop","product","x").show()

+--------+-------+---+---+------+--------+
|    shop|product|  x|  y| avg_x|   avg_y|
+--------+-------+---+---+------+--------+
|megamart|  bread|  1|371|6.4545|461.2727|
|megamart|  bread|  2|432|6.4545|461.2727|
|megamart|  bread|  3|425|6.4545|461.2727|
|megamart|  bread|  4|524|6.4545|461.2727|
|megamart|  bread|  5|468|6.4545|461.2727|
|megamart|  bread|  6|414|6.4545|461.2727|
|megamart|  bread|  8|487|6.4545|461.2727|
|megamart|  bread|  9|493|6.4545|461.2727|
|megamart|  bread| 10|517|6.4545|461.2727|
|megamart|  bread| 11|473|6.4545|461.2727|
|megamart|  bread| 12|470|6.4545|461.2727|
|megamart| cheese|  1| 51|   6.4|    56.4|
|megamart| cheese|  2| 56|   6.4|    56.4|
|megamart| cheese|  3| 63|   6.4|    56.4|
..
..``````

Notes by Data Munging Ninja. Generated on nini:/home/willem/sync/20151223_datamungingninja/simplesalesprediction at 2016-06-25 10:02