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Trips and Users

Table: Trips

+-------------+----------+
| Column Name | Type     |
+-------------+----------+
| id          | int      |
| client_id   | int      |
| driver_id   | int      |
| city_id     | int      |
| status      | enum     |
| request_at  | varchar  |     
+-------------+----------+
id is the primary key (column with unique values) for this table.
The table holds all taxi trips. Each trip has a unique id, while client_id and driver_id are foreign keys to the users_id at the Users table.
Status is an ENUM (category) type of ('completed', 'cancelled_by_driver', 'cancelled_by_client').

 

Table: Users

+-------------+----------+
| Column Name | Type     |
+-------------+----------+
| users_id    | int      |
| banned      | enum     |
| role        | enum     |
+-------------+----------+
users_id is the primary key (column with unique values) for this table.
The table holds all users. Each user has a unique users_id, and role is an ENUM type of ('client', 'driver', 'partner').
banned is an ENUM (category) type of ('Yes', 'No').

 

The cancellation rate is computed by dividing the number of canceled (by client or driver) requests with unbanned users by the total number of requests with unbanned users on that day.

Write a solution to find the cancellation rate of requests with unbanned users (both client and driver must not be banned) each day between "2013-10-01" and "2013-10-03". Round Cancellation Rate to two decimal points.

Return the result table in any order.

The result format is in the following example.

 

Example 1:

Input: 
Trips table:
+----+-----------+-----------+---------+---------------------+------------+
| id | client_id | driver_id | city_id | status              | request_at |
+----+-----------+-----------+---------+---------------------+------------+
| 1  | 1         | 10        | 1       | completed           | 2013-10-01 |
| 2  | 2         | 11        | 1       | cancelled_by_driver | 2013-10-01 |
| 3  | 3         | 12        | 6       | completed           | 2013-10-01 |
| 4  | 4         | 13        | 6       | cancelled_by_client | 2013-10-01 |
| 5  | 1         | 10        | 1       | completed           | 2013-10-02 |
| 6  | 2         | 11        | 6       | completed           | 2013-10-02 |
| 7  | 3         | 12        | 6       | completed           | 2013-10-02 |
| 8  | 2         | 12        | 12      | completed           | 2013-10-03 |
| 9  | 3         | 10        | 12      | completed           | 2013-10-03 |
| 10 | 4         | 13        | 12      | cancelled_by_driver | 2013-10-03 |
+----+-----------+-----------+---------+---------------------+------------+
Users table:
+----------+--------+--------+
| users_id | banned | role   |
+----------+--------+--------+
| 1        | No     | client |
| 2        | Yes    | client |
| 3        | No     | client |
| 4        | No     | client |
| 10       | No     | driver |
| 11       | No     | driver |
| 12       | No     | driver |
| 13       | No     | driver |
+----------+--------+--------+
Output: 
+------------+-------------------+
| Day        | Cancellation Rate |
+------------+-------------------+
| 2013-10-01 | 0.33              |
| 2013-10-02 | 0.00              |
| 2013-10-03 | 0.50              |
+------------+-------------------+
Explanation: 
On 2013-10-01:
  - There were 4 requests in total, 2 of which were canceled.
  - However, the request with Id=2 was made by a banned client (User_Id=2), so it is ignored in the calculation.
  - Hence there are 3 unbanned requests in total, 1 of which was canceled.
  - The Cancellation Rate is (1 / 3) = 0.33
On 2013-10-02:
  - There were 3 requests in total, 0 of which were canceled.
  - The request with Id=6 was made by a banned client, so it is ignored.
  - Hence there are 2 unbanned requests in total, 0 of which were canceled.
  - The Cancellation Rate is (0 / 2) = 0.00
On 2013-10-03:
  - There were 3 requests in total, 1 of which was canceled.
  - The request with Id=8 was made by a banned client, so it is ignored.
  - Hence there are 2 unbanned request in total, 1 of which were canceled.
  - The Cancellation Rate is (1 / 2) = 0.50

Solution Explanation

This problem requires calculating the daily cancellation rate of taxi trips, considering only trips where both the client and the driver are not banned. The solution involves joining the Trips and Users tables, filtering based on the specified conditions, and then calculating the cancellation rate.

Approach

  1. Data Filtering: The solution first filters the Trips table to include only trips within the specified date range ("2013-10-01" to "2013-10-03").

  2. Joining Tables and Filtering for Unbanned Users: The Trips table is then joined with the Users table twice: once to check the client's banned status and once for the driver's. This ensures that only trips with unbanned clients and drivers are considered. Rows where either the client or driver is banned are excluded.

  3. Cancellation Status and Aggregation: A new column indicating whether a trip was cancelled (status_cancelled) is created. The data is grouped by day, and the total number of cancelled trips and the total number of trips are aggregated for each day.

  4. Cancellation Rate Calculation: Finally, the cancellation rate is calculated for each day by dividing the total number of cancelled trips by the total number of trips and rounding the result to two decimal places.

Time Complexity Analysis

The time complexity of the solution depends on the size of the Trips and Users tables. The dominant operations are the joins and group by operations.

  • MySQL: The JOIN operations are typically O(M log M + N log N) where M and N are the sizes of the joined tables, and the GROUP BY is O(K log K) where K is the number of unique dates. Therefore, the overall time complexity of the MySQL query is dominated by the joins, giving a time complexity of approximately O(M log M + N log N).

  • Python (Pandas): The pd.merge operations (joins) in Pandas have a time complexity that depends on the implementation details and data structure used, but typically it is O(M log M + N log N) in the average case and O(MN) in the worst case. The groupby and aggregation steps have complexities similar to the MySQL query. The overall time complexity is again dominated by the merge steps.

Space Complexity Analysis

The space complexity depends on the intermediate data structures created during the process.

  • MySQL: The space complexity is primarily determined by the size of the intermediate result sets generated during the joins and GROUP BY operations. This is approximately proportional to the size of the input tables.

  • Python (Pandas): The space complexity is dominated by the size of the Pandas DataFrames created. These DataFrames will hold the intermediate results of the joins, aggregations and final result. The space complexity is approximately proportional to the size of the input data.

Code Implementation Details

The Python code utilizes the Pandas library for efficient data manipulation. The MySQL code uses standard SQL syntax for database querying. Both achieve the same result but through different approaches. The Pandas approach is more convenient for data manipulation and analysis within a Python environment, while the MySQL approach is directly executed within the database. Choosing the most suitable method depends on the context and tools available.