Here is one way (basically melt the DF, then pivot)

# First combine price and units into a map column
mydf = mydf.withColumn("price_units", F.create_map(F.lit("price"), "price", F.lit("units"), "units"))

# Now explode to get a melted dataframe
mydf = mydf.select("id", "day", F.explode("price_units").alias("name", "value"))

+---+---+-----+-----+
| id|day| name|value|
+---+---+-----+-----+
|100|  1|price|   23|
|100|  1|units|   10|
|100|  2|price|   45|
|100|  2|units|   11|
|100|  3|price|   67|
etc

# Then pivot
mydf.groupby("id", "name").pivot("day").agg(F.mean("value")).show()

+---+-----+----+----+----+----+
| id| name|   1|   2|   3|   4|
+---+-----+----+----+----+----+
|100|price|23.0|45.0|67.0|78.0|
|101|price|23.0|45.0|67.0|78.0|
|102|units|10.0|11.0|16.0|18.0|
|100|units|10.0|11.0|12.0|13.0|
|101|units|10.0|13.0|14.0|15.0|
|102|price|23.0|45.0|67.0|78.0|
+---+-----+----+----+----+----+