Complex conditional aggregation in Pandas - python-3.x

Question

In this table, I want to find the Average number of days between actions per each user.
What I mean here is, I want to group by user_id and then I want to subtract each date directly from the date before it by days per each user. and then find the average number of these days per each user (the average number of No_Action days per each user).
+---------+-----------+----------------------+
| User_ID | Action_ID | Action_At |
+---------+-----------+----------------------+
| 1 | 11 | 2019-01-31T23:00:37Z |
+---------+-----------+----------------------+
| 2 | 12 | 2019-01-31T23:11:12Z |
+---------+-----------+----------------------+
| 3 | 13 | 2019-01-31T23:14:53Z |
+---------+-----------+----------------------+
| 1 | 14 | 2019-02-01T00:00:30Z |
+---------+-----------+----------------------+
| 2 | 15 | 2019-02-01T00:01:03Z |
+---------+-----------+----------------------+
| 3 | 16 | 2019-02-01T00:02:32Z |
+---------+-----------+----------------------+
| 1 | 17 | 2019-02-06T11:30:28Z |
+---------+-----------+----------------------+
| 2 | 18 | 2019-02-06T11:30:28Z |
+---------+-----------+----------------------+
| 3 | 19 | 2019-02-07T09:09:16Z |
+---------+-----------+----------------------+
| 1 | 20 | 2019-02-11T15:37:24Z |
+---------+-----------+----------------------+
| 2 | 21 | 2019-02-18T10:02:07Z |
+---------+-----------+----------------------+
| 3 | 22 | 2019-02-26T12:01:31Z |
+---------+-----------+----------------------+

Answer 1

You can do it like this (and next time, please provide the data so that it is easy to help you; it took me much longer to enter the data than to get to the solution):
df = pd.DataFrame({'User_ID': [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3],
'Action_ID': [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22],
'Action_At': ['2019-01-31T23:00:37Z', '2019-01-31T23:11:12Z', '2019-01-31T23:14:53Z', '2019-02-01T00:00:30Z', '2019-02-01T00:01:03Z', '2019-02-01T00:02:32Z', '2019-02-06T11:30:28Z', '2019-02-06T11:30:28Z', '2019-02-07T09:09:16Z', '2019-02-11T15:37:24Z', '2019-02-18T10:02:07Z', '2019-02-26T12:01:31Z']})
df.Action_At = pd.to_datetime(df.Action_At)
df.groupby('User_ID').apply(lambda x: (x.Action_At - x.Action_At.shift()).mean())
## User_ID
## 1 3 days 13:32:15.666666
## 2 5 days 19:36:58.333333
## 3 8 days 12:15:32.666666
## dtype: timedelta64[ns]
Or, if you want the solution in days:
df.groupby('User_ID').apply(lambda x: (x.Action_At - x.Action_At.shift()).dt.days.mean())
## User_ID
## 1 3.333333
## 2 5.333333
## 3 8.333333
## dtype: float64