Count datetime between 2 datetime based on another datetime

here is one way to do it using pandassql

while it is possible to do with the pandas merge as well, but it will requires to create a Cartesian product of two DF, and then filtering out the rows that meet the criteria.

using pandasql, if one is familiar with SQL, makes it simpler to solve it

# https://pypi.org/project/pandasql/
pysqldf = lambda q: sqldf(q, globals())

# Query to select where the created time fall inbetween the login and logout
qry = """
select *
from df2
left join df
on df2.created_time between df.login_time and df.logout_time
"""
pysqldf = lambda q: sqldf(q, globals())
result=pysqldf(qry)
result  #capture the result

# do a groupby to take the count of logged in users
df3=result.groupby(['created_time'])['Login_time'].agg(online_user='count').reset_index()


# logged out is the total number of users minus the logged in users
cnt=df['Login_time'].count()
df3['offline_user'] = cnt - df3['online_user'] 
df3

created_time    online_user     offline_user
0   2022-08-01 11:45:00     3   0
1   2022-08-01 11:51:00     2   1
2   2022-08-01 11:56:00     1   2
3   2022-08-01 11:57:00     1   2
4   2022-08-01 12:00:00     0   3

The question can be solved by using a range join, which is a common type of inequality join.

This can be solved with the conditional_join from pyjanitor, which is efficient, and for large data, should be more performant than a naive cartesian join:

# pip install pyjanitor
# for more performance, 
# if you have numba installed,
# you can install the development version: 
# pip git+https://github.com/pyjanitor-devs/pyjanitor.git
import pandas as pd
import janitor as jn

Compute the range join, followed by the count of matches, where the date from df2 is within df1:

out = (df1
       .conditional_join(
            df2, 
            # variable arguments
            # left column, right column, operator
           ('Login time', 'created time', '<'), 
           ('Logout time', 'created time', '>'),
           how = "inner")
       .groupby('created time')
       .size()
       .rename('online_users'))

out

created time
2022-08-01 11:45:00    3
2022-08-01 11:51:00    2
2022-08-01 11:56:00    1
Name: online_users, dtype: int64

Join back to df2, to get the offline users as well:

(
df2
.merge(
   out, 
   on = 'created time', 
   how = 'left')
.assign(
   online_users = lambda df: df.online_users.fillna(0, downcast='infer'), 
   offline_users = lambda df: len(df1) - df.online_users)
)

         created time  online_users  offline_users
0 2022-08-01 11:45:00             3              0
1 2022-08-01 11:51:00             2              1
2 2022-08-01 11:56:00             1              2
3 2022-08-01 11:57:00             0              3
4 2022-08-01 12:00:00             0              3