Aggregation of a data frame based on condition (Pyspark)

I need help with following task:
I have obtained a groupby results whereby I have a list of multiple addresses (here just a cut out with one address) with people occupying those addresses. I need to calculate ratio of usage of the app, so that I am dividing [name] + Active Count / [name] + Passive Count and creating a new dataframe with [address][name][usage_ratio]

I have never done aggregation alike and I have no idea where to start or how to loop it to execute it. Can anyone help?

+------------+--------------------+----------------+-----+
|     address|                name|      use_of_app|count|
+------------+--------------------+----------------+-----+
|          33|                Mark|          active|   35|
|          33|                Mark|         passive|    4|
|          33|               Abbie|          active|   30|
|          33|               Abbie|         passive|    2|
|          33|                Anna|         passive|    3|
|          33|                Anna|          active|   32|
|          33|                 Tom|         passive|   38|
|          33|                 Tom|          active|   50|
|          33|             Patrick|         passive|   40|
|          33|             Patrick|          active|   57|
+------------+--------------------+----------------+-----+

Another option would be a pivot-step:

from pyspark.sql import function as F

(
    df.groupby("address", "name")
    .pivot("use_of_app", values=["active", "passive"])
    .agg(F.sum("count"))
    .withColumn("ratio", F.col("active") / F.col("passive"))
    .show()
)
# Output
+-------+-----+------+-------+------------------+
|address| name|active|passive|             ratio|
+-------+-----+------+-------+------------------+
|     33|Abbie|    30|      2|              15.0|
|     33| Anna|    32|      3|10.666666666666666|
|     33| Mark|    35|      4|              8.75|
+-------+-----+------+-------+------------------+

+++ Updated according to the suggestion by Steven: .pivot("use_of_app", values=["active", "passive"]).

Here is my code - I use a sum on count column because I am not sure how many lines of each use_of_app you will have :

from pyspark.sql import functions as F

df = df.groupBy("address", "name").agg(
    (
        F.sum(F.when(F.col("use_of_app") == "active", F.col("count")))
        / F.sum(F.when(F.col("use_of_app") == "passive", F.col("count")))
    ).alias("usage_ratio")
)

df.show()
+-------+-------+------------------+
|address|   name|       usage_ratio|
+-------+-------+------------------+
|     33|  Abbie|              15.0|
|     33|   Mark|              8.75|
|     33|    Tom|1.3157894736842106|
|     33|   Anna|10.666666666666666|
|     33|Patrick|             1.425|
+-------+-------+------------------+