Set sequential number on entries with specific condition - pandas

Question

Situation
I'm preparing the migration of user data and I have a list of user subscriptions, for which I try to give every user a member id.
We only want to migrate active subscriptions, which are identified with the value 1 in the row "active". The oldest user should get the lowest number and count upwards from there.
Problem
The oldest users already have renewed their subscription. Please have a look at this image:
If I order the dataset by date and set a sequential number from there, the oldest user doesn't get the lowest number since we only migrate active subscriptions. In the above image, the oldest user "one" should have the id 1 but gets the id 5 under my current setting.
Possible Solution
I'm struggling to find a solution to solve this problem. I was thinking about finding a way to write:
a) sort by date and set an ongoing member id
b) check for each user mail adress with active = 1, if there is already an entry existing with active = 0 and if yes, overwrite the member id.
afterwards, it should look like this:

Answer 1

Create sample dataset:
df = pd.DataFrame({
"member_id":[1,2,3,4,5,6],
"active":[0,0,1,1,1,1],
"date": ["Jan 2020","Feb 2020","Mar 2020","Apr 2020","Jan 2021","Feb 2021"],
"mail": ["one#user.com","two#user.com","three#user.com","four#user.com","one#user.com","two#user.com"]})
Then find unique users and their ids:
# Change date column to datetime
df["date"] = pd.to_datetime(df["date"])
# Sort rows by active and date columns
df = df.sort_values(by=["active","date"])
# Find unique users by order of appearance
users = df["mail"].unique()
# Find id of each user
users2id = {u:i+1 for i, u in enumerate(users)}
# Update id
df["member_id"] = df["mail"].apply(lambda u: users2id[u])