Watch time by genre
easyWatch time by genre
Netflix Pandas Interview Question
Netflix's content team wants to see which genres members spend the most time watching.
Using the viewing_activity and titles DataFrames, return each genre with the total minutes watched as minutes_watched. Sort the rows by minutes_watched, highest first. Assign the answer to result.
Asked of
- Data Analyst
- Product Analyst
- Business Analyst
- Analytics Engineer
- Data Scientist
viewing_activityDataFrame16 rows
| Column Name | Type |
|---|---|
| view_id | int64 |
| profile_id | int64 |
| title_id | int64 |
| country | str |
| watch_date | str |
| minutes_watched | int64 |
titlesDataFrame6 rows
| Column Name | Type |
|---|---|
| title_id | int64 |
| title_name | str |
| content_type | str |
| genre | str |
| runtime_minutes | int64 |
viewing_activityExample Input
| view_id | profile_id | title_id | country | watch_date | minutes_watched |
|---|---|---|---|---|---|
| 1 | 1001 | 1 | United States | 2024-08-01 | 118 |
| 2 | 1002 | 4 | United States | 2024-08-01 | 58 |
| 3 | 1003 | 4 | United States | 2024-08-02 | 30 |
| 4 | 1004 | 3 | United States | 2024-08-02 | 45 |
| 5 | 1005 | 1 | Brazil | 2024-08-02 | 60 |
| 6 | 1006 | 6 | Brazil | 2024-08-03 | 52 |
| 7 | 1007 | 6 | Brazil | 2024-08-03 | 52 |
| 8 | 1008 | 2 | Brazil | 2024-08-03 | 92 |
titlesExample Input
| title_id | title_name | content_type | genre | runtime_minutes |
|---|---|---|---|---|
| 1 | Midnight Heist | Movie | Thriller | 118 |
| 2 | Ocean Deep | Movie | Documentary | 92 |
| 3 | Kitchen Wars | Series | Reality | 45 |
| 4 | The Last Colony | Series | Sci-Fi | 58 |
| 5 | Laugh Track | Movie | Comedy | 101 |
| 6 | Crown of Ash | Series | Drama | 52 |
Example Output
| genre | minutes_watched |
|---|---|
| Thriller | 178 |
| Drama | 104 |
| Documentary | 92 |
| Sci-Fi | 88 |
| Reality | 45 |
Explanation
In the example, Thriller comes first with 178 minutes: 118 and 60 minutes of Midnight Heist. The Sci-Fi series The Last Colony was watched for 58 and 30 minutes, 88 in total.
The example above is a small slice of the data. Your code runs against the full DataFrames.
Company
Netflix
Difficulty
easy
Topic
joins
Language
Pandas
Your code
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