How can I unpivot two sets of columns in Spark?

I'm looking to unpivot two sets of columns so that this:

Is transformed into this:

I have tried using the stack function to unpivot the two sets of columns, but I can only unpivot one set. Whenever I use two stack functions, an exception is raised saying I can only use one generator function:

unpivotedDf = df.selectExpr(
    "date",
    "stack(2, 'day', sales_day, 'night', sales_night) as (timeOfDay, sales)",
    "stack(2, 'day', customers_day, 'night', customers_night) as (timeOfDay, customers)"
)

I can also unpivot both sets of columns separately and then join the separate tables on date and on timeOfDay, but this is really inefficient for large datasets. Is there an alternative?

one way is to unpivot the whole data, and then pivot the columns required.

here's an example

data_sdf. \
    selectExpr('date', 
               'stack(4, \
                      "sales_day", sales_day, \
                      "sales_night", sales_night, \
                      "cust_day", cust_day, \
                      "cust_night", cust_night\
                      ) as (key, val)'
               ). \
    withColumn('key_split', func.split('key', '_')). \
    selectExpr('date', 'key_split[0] as attr', 'key_split[1] as time_of_day', 'val'). \
    groupBy('date', 'time_of_day'). \
    pivot('attr', values=['cust', 'sales']). \
    agg(func.first('val')). \
    orderBy('date', 'time_of_day'). \
    show()

# +-------+-----------+----+-----+
# |   date|time_of_day|cust|sales|
# +-------+-----------+----+-----+
# |june 15|        day|  11|    1|
# |june 15|      night|  12|    2|
# |june 16|        day|  13|    3|
# |june 16|      night|  14|    4|
# |june 17|        day|  15|    5|
# |june 17|      night|  15|    5|
# +-------+-----------+----+-----+

Lets create a map of time of day to the corresponding columns. Then we can explode the map and select the required columns.

from itertools import chain

c = chain(*[(F.lit(c), F.array(f'sales_{c}', f'customers_{c}')) for c in ('day', 'night')])

result = (
    df
    .select('date', F.explode(F.create_map(*c)))
    .selectExpr('date', 'key as time_of_day', 'value[0] as sales', 'value[1] as customers')
)

Result

+-------+-----------+-----+---------+
|   date|time_of_day|sales|customers|
+-------+-----------+-----+---------+
|June 15|        day|    1|       11|
|June 15|      night|    2|       12|
|June 16|        day|    3|       13|
|June 16|      night|    4|       14|
|June 17|        day|    5|       15|
|June 17|      night|    6|       16|
+-------+-----------+-----+---------+