Add hours to datetime of rows in a specific time interval

Let it be the following Python Panda DataFrame:

| country_ID | date                      | counter  | value  |
| --------   | ------------------------- | -------- | ------ |
| USA        | 2022-03-01 09:22:29+00:00 |    1     |  red   |
| UK         | 2022-03-01 11:21:20+00:00 |    1     |  blue  |
| USA        | 2022-04-02 12:15:23+00:00 |    1     |  red   |
| ITL        | 2022-04-03 11:13:31+00:00 |    1     |  red   |
| USA        | 2022-05-05 21:04:42+00:00 |    1     |  green |
| USA        | 2022-05-05 22:01:51+00:00 |    1     |  green |
| ITL        | 2022-06-06 13:00:41+00:00 |    1     |  red   |

Given a date and time range (start and end) and a country_ID, I want to add 2 hours to the rows that are in that range:

Example:

add_hours('USA', '2022-03-01 09:00:00', '2022-05-05 21:30:00', 2)

| country_ID | date                      | counter  | value  |
| --------   | ------------------------- | -------- | ------ |
| USA        | 2022-03-01 11:22:29+00:00 |    1     |  red   |
| UK         | 2022-03-01 11:21:20+00:00 |    1     |  blue  |
| USA        | 2022-04-02 14:15:23+00:00 |    1     |  red   |
| ITL        | 2022-04-03 11:13:31+00:00 |    1     |  red   |
| USA        | 2022-05-05 23:04:42+00:00 |    1     |  green |
| USA        | 2022-05-05 22:01:51+00:00 |    1     |  green |
| ITL        | 2022-06-06 13:00:41+00:00 |    1     |  red   |

Answer

Try your logic with boolean indexing (date must also be a datetime object, not a string):

def add_hours(df, country, start, end):
    is_country = df['country_ID'].eq(country)
    valid_date = df['date'].between(start, end)

    df.loc[is_country & valid_date, 'date'] += pd.Timedelta('2H')