I need to pivot more than one column in a pyspark dataframe. Sample dataframe,
>>> d = [(100,1,23,10),(100,2,45,11),(100,3,67,12),(100,4,78,13),(101,1,23,10),(101,2,45,13),(101,3,67,14),(101,4,78,15),(102,1,23,10),(102,2,45,11),(102,3,67,16),(102,4,78,18)]
>>> mydf = spark.createDataFrame(d,['id','day','price','units'])
>>> mydf.show()
+---+---+-----+-----+
| id|day|price|units|
+---+---+-----+-----+
|100| 1| 23| 10|
|100| 2| 45| 11|
|100| 3| 67| 12|
|100| 4| 78| 13|
|101| 1| 23| 10|
|101| 2| 45| 13|
|101| 3| 67| 14|
|101| 4| 78| 15|
|102| 1| 23| 10|
|102| 2| 45| 11|
|102| 3| 67| 16|
|102| 4| 78| 18|
+---+---+-----+-----+
Now,if I need to get price column into a row for each id based on day, then I can use pivot method as,
>>> pvtdf = mydf.withColumn('combcol',F.concat(F.lit('price_'),mydf['day'])).groupby('id').pivot('combcol').agg(F.first('price'))
>>> pvtdf.show()
+---+-------+-------+-------+-------+
| id|price_1|price_2|price_3|price_4|
+---+-------+-------+-------+-------+
|100| 23| 45| 67| 78|
|101| 23| 45| 67| 78|
|102| 23| 45| 67| 78|
+---+-------+-------+-------+-------+
so when I need units column as well to be transposed as price, either I got to create one more dataframe as above for units and then join both using id.But, when I have more columns as such, I tried a function to do it,
>>> def pivot_udf(df,*cols):
... mydf = df.select('id').drop_duplicates()
... for c in cols:
... mydf = mydf.join(df.withColumn('combcol',F.concat(F.lit('{}_'.format(c)),df['day'])).groupby('id').pivot('combcol').agg(F.first(c)),'id')
... return mydf
...
>>> pivot_udf(mydf,'price','units').show()
+---+-------+-------+-------+-------+-------+-------+-------+-------+
| id|price_1|price_2|price_3|price_4|units_1|units_2|units_3|units_4|
+---+-------+-------+-------+-------+-------+-------+-------+-------+
|100| 23| 45| 67| 78| 10| 11| 12| 13|
|101| 23| 45| 67| 78| 10| 13| 14| 15|
|102| 23| 45| 67| 78| 10| 11| 16| 18|
+---+-------+-------+-------+-------+-------+-------+-------+-------+
Need suggestions on ,if it is good practice to do so and if any other better way of doing it. Thanks in advance!