[FIXED] How to pivot on multiple columns in Spark SQL?

Issue

I need to pivot more than one column in a PySpark dataframe. Sample dataframe:

from pyspark.sql import functions as F
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|
# +---+---+-----+-----+t

Now, if I need to get price column into a row for each id based on day, then I can use pivot method:

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, I've 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|
# +---+-------+-------+-------+-------+-------+-------+-------+-------+

Is it a good practice to do so and is there any other better way of doing it?

Solution

Here's a non-UDF way involving a single pivot (hence, just a single column scan to identify all the unique dates).

dff = mydf.groupBy('id').pivot('day').agg(F.first('price').alias('price'),F.first('units').alias('unit'))

Here's the result (apologies for the non-matching ordering and naming):

+---+-------+------+-------+------+-------+------+-------+------+               
| id|1_price|1_unit|2_price|2_unit|3_price|3_unit|4_price|4_unit|
+---+-------+------+-------+------+-------+------+-------+------+
|100|     23|    10|     45|    11|     67|    12|     78|    13|
|101|     23|    10|     45|    13|     67|    14|     78|    15|
|102|     23|    10|     45|    11|     67|    16|     78|    18|
+---+-------+------+-------+------+-------+------+-------+------+

We just aggregate both on the price and the unit column after pivoting on the day.

If naming required as in question,

dff.select([F.col(c).name('_'.join(x for x in c.split('_')[::-1])) for c in dff.columns]).show()

+---+-------+------+-------+------+-------+------+-------+------+
| id|price_1|unit_1|price_2|unit_2|price_3|unit_3|price_4|unit_4|
+---+-------+------+-------+------+-------+------+-------+------+
|100|     23|    10|     45|    11|     67|    12|     78|    13|
|101|     23|    10|     45|    13|     67|    14|     78|    15|
|102|     23|    10|     45|    11|     67|    16|     78|    18|
+---+-------+------+-------+------+-------+------+-------+------+

Answered By - Jedi

Answer Checked By - Robin (PHPFixing Admin)