Multiple group counts within data base

I have been presented with a very small dataset that has the date of each time a user logs into a system, I have to use this data set to create a table where I can show for each log-in the cumulative monthly counts of logs and the overall cumulative counts of logs, this is the data set I have:

and this is my desired output:

Why? Let’s look at the row number 5. This is the first time the user Mark has logged into the system during the month 3 (March) but it is the 5th overall login in the data set (for the purpose of learning there will only be one year (2022).

I have no idea as to how to get the monthly and overall count together. I can groupby user and sort by date to count how many times in total a user has logged in, but I know that in order to achive my desired output I will have to group by date and user and then make counts based on month but I will have to somehow group the data by user (only) to get the overall count and I dont think I could group twice the data.

>Solution :

First you need to convert date to actual datetime values with to_datetime. The rest is simple with groupby and cumcount:

df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')
df['monthly_count'] = df.groupby([df['user'], df['date'].dt.year, df['date'].dt.month]).cumcount() + 1
df['acum_count'] = df.groupby('user').cumcount() + 1

Output:

>>> df
        date  user  monthly_count  acum_count
0 2022-01-01  Mark              1           1
1 2022-01-02  Mark              2           2
2 2022-02-03  Mark              1           3
3 2022-02-04  Mark              2           4
4 2022-03-05  Mark              1           5
5 2022-03-06  Mark              2           6
6 2022-03-07  Mark              3           7
7 2022-03-08  Mark              4           8
8 2022-03-09  Mark              5           9