Average time between actions by user (SQL and pandas)

Question

Let's say I have a table like this with user_id and the time difference between actions already calculated.

   **|user_id |sec_between_actions|**
   |     329|                  1|
   |     329|                211|
   |     329|                911|
   |     329|                 11|
   |     329|                  9|
   |      12|                  2|
   |      12|                  3|
   |      12|                  8|
   |      12|                  7|
   |      12|                  7|
   |       1|                  1|
   |       1|                  1|
   |     111|                  3|
   |     111|                 11|
   |      18|                  4|
   |      29|                  5|
   |      29|                  1|

(imagine a lot of records and lots of users)

My desire output would be something like that (using SQL):

   **|user_id |avg_time_between_actions|**
   |     329|                   228,6|
   |      12|                     5,4|
   |       1|                       1|
   |     111|                       7|
   |      18|                       4|
   |      29|                       3|

Answer 1

For doing this in SQL, you have to use the group by function to group same user ids and then use the aggregation function to find the average of the grouped numbers.

SQL Code:

SELECT user_id, AVG(sec_between_actions) as avg_time_between_actions,
FROM table_name
GROUP BY user_id;

I am not sure why you have used , instead of ., but you can do this as well by changing the output but it doesn't seem logical.