diff --git a/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README.md b/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README.md index 7434a2befc998..77f86b85e4aa2 100644 --- a/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README.md +++ b/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README.md @@ -152,14 +152,82 @@ Each row represents a trip made by a driver, including the distance traveled and -### 方法一 +### 方法一：分组统计 + 连接查询 + +我们先对 `trips` 表进行分组统计，计算每个司机在上半年和下半年的平均燃油效率。然后通过连接查询将结果与 `drivers` 表关联，筛选出燃油效率有提升的司机，并计算提升的幅度。 #### MySQL ```sql +# Write your MySQL query statement below +WITH + T AS ( + SELECT + driver_id, + AVG(distance_km / fuel_consumed) half_avg, + CASE + WHEN MONTH(trip_date) <= 6 THEN 1 + ELSE 2 + END half + FROM trips + GROUP BY driver_id, half + ) +SELECT + t1.driver_id, + d.driver_name, + ROUND(t1.half_avg, 2) first_half_avg, + ROUND(t2.half_avg, 2) second_half_avg, + ROUND(t2.half_avg - t1.half_avg, 2) efficiency_improvement +FROM + T t1 + JOIN T t2 ON t1.driver_id = t2.driver_id AND t1.half < t2.half AND t1.half_avg < t2.half_avg + JOIN drivers d ON t1.driver_id = d.driver_id +ORDER BY efficiency_improvement DESC, d.driver_name; +``` +#### Pandas + +```python +import pandas as pd + + +def find_improved_efficiency_drivers( + drivers: pd.DataFrame, trips: pd.DataFrame +) -> pd.DataFrame: + trips = trips.copy() + trips["trip_date"] = pd.to_datetime(trips["trip_date"]) + trips["half"] = trips["trip_date"].dt.month.apply(lambda m: 1 if m <= 6 else 2) + trips["efficiency"] = trips["distance_km"] / trips["fuel_consumed"] + half_avg = ( + trips.groupby(["driver_id", "half"])["efficiency"] + .mean() + .reset_index(name="half_avg") + ) + pivot = half_avg.pivot(index="driver_id", columns="half", values="half_avg").rename( + columns={1: "first_half_avg", 2: "second_half_avg"} + ) + pivot = pivot.dropna() + pivot = pivot[pivot["second_half_avg"] > pivot["first_half_avg"]] + pivot["efficiency_improvement"] = ( + pivot["second_half_avg"] - pivot["first_half_avg"] + ).round(2) + pivot["first_half_avg"] = pivot["first_half_avg"].round(2) + pivot["second_half_avg"] = pivot["second_half_avg"].round(2) + result = pivot.reset_index().merge(drivers, on="driver_id") + result = result.sort_values( + by=["efficiency_improvement", "driver_name"], ascending=[False, True] + ) + return result[ + [ + "driver_id", + "driver_name", + "first_half_avg", + "second_half_avg", + "efficiency_improvement", + ] + ] ``` diff --git a/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README_EN.md b/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README_EN.md index 5fbccf0cd02c4..9519f5f463081 100644 --- a/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README_EN.md +++ b/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README_EN.md @@ -152,14 +152,84 @@ Each row represents a trip made by a driver, including the distance traveled and -### Solution 1 +### Solution 1: Group Aggregation + Join Query + +First, we perform group aggregation on the `trips` table to calculate the average fuel efficiency for each driver in the first half and the second half of the year. + +Then, we join the results with the `drivers` table, filter out the drivers whose fuel efficiency has improved, and calculate the amount of improvement. #### MySQL ```sql +# Write your MySQL query statement below +WITH + T AS ( + SELECT + driver_id, + AVG(distance_km / fuel_consumed) half_avg, + CASE + WHEN MONTH(trip_date) <= 6 THEN 1 + ELSE 2 + END half + FROM trips + GROUP BY driver_id, half + ) +SELECT + t1.driver_id, + d.driver_name, + ROUND(t1.half_avg, 2) first_half_avg, + ROUND(t2.half_avg, 2) second_half_avg, + ROUND(t2.half_avg - t1.half_avg, 2) efficiency_improvement +FROM + T t1 + JOIN T t2 ON t1.driver_id = t2.driver_id AND t1.half < t2.half AND t1.half_avg < t2.half_avg + JOIN drivers d ON t1.driver_id = d.driver_id +ORDER BY efficiency_improvement DESC, d.driver_name; +``` +#### Pandas + +```python +import pandas as pd + + +def find_improved_efficiency_drivers( + drivers: pd.DataFrame, trips: pd.DataFrame +) -> pd.DataFrame: + trips = trips.copy() + trips["trip_date"] = pd.to_datetime(trips["trip_date"]) + trips["half"] = trips["trip_date"].dt.month.apply(lambda m: 1 if m <= 6 else 2) + trips["efficiency"] = trips["distance_km"] / trips["fuel_consumed"] + half_avg = ( + trips.groupby(["driver_id", "half"])["efficiency"] + .mean() + .reset_index(name="half_avg") + ) + pivot = half_avg.pivot(index="driver_id", columns="half", values="half_avg").rename( + columns={1: "first_half_avg", 2: "second_half_avg"} + ) + pivot = pivot.dropna() + pivot = pivot[pivot["second_half_avg"] > pivot["first_half_avg"]] + pivot["efficiency_improvement"] = ( + pivot["second_half_avg"] - pivot["first_half_avg"] + ).round(2) + pivot["first_half_avg"] = pivot["first_half_avg"].round(2) + pivot["second_half_avg"] = pivot["second_half_avg"].round(2) + result = pivot.reset_index().merge(drivers, on="driver_id") + result = result.sort_values( + by=["efficiency_improvement", "driver_name"], ascending=[False, True] + ) + return result[ + [ + "driver_id", + "driver_name", + "first_half_avg", + "second_half_avg", + "efficiency_improvement", + ] + ] ``` diff --git a/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.py b/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.py new file mode 100644 index 0000000000000..395937add9ab8 --- /dev/null +++ b/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.py @@ -0,0 +1,38 @@ +import pandas as pd + + +def find_improved_efficiency_drivers( + drivers: pd.DataFrame, trips: pd.DataFrame +) -> pd.DataFrame: + trips = trips.copy() + trips["trip_date"] = pd.to_datetime(trips["trip_date"]) + trips["half"] = trips["trip_date"].dt.month.apply(lambda m: 1 if m <= 6 else 2) + trips["efficiency"] = trips["distance_km"] / trips["fuel_consumed"] + half_avg = ( + trips.groupby(["driver_id", "half"])["efficiency"] + .mean() + .reset_index(name="half_avg") + ) + pivot = half_avg.pivot(index="driver_id", columns="half", values="half_avg").rename( + columns={1: "first_half_avg", 2: "second_half_avg"} + ) + pivot = pivot.dropna() + pivot = pivot[pivot["second_half_avg"] > pivot["first_half_avg"]] + pivot["efficiency_improvement"] = ( + pivot["second_half_avg"] - pivot["first_half_avg"] + ).round(2) + pivot["first_half_avg"] = pivot["first_half_avg"].round(2) + pivot["second_half_avg"] = pivot["second_half_avg"].round(2) + result = pivot.reset_index().merge(drivers, on="driver_id") + result = result.sort_values( + by=["efficiency_improvement", "driver_name"], ascending=[False, True] + ) + return result[ + [ + "driver_id", + "driver_name", + "first_half_avg", + "second_half_avg", + "efficiency_improvement", + ] + ] diff --git a/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.sql b/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.sql new file mode 100644 index 0000000000000..d08d77edeeaf4 --- /dev/null +++ b/solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.sql @@ -0,0 +1,24 @@ +# Write your MySQL query statement below +WITH + T AS ( + SELECT + driver_id, + AVG(distance_km / fuel_consumed) half_avg, + CASE + WHEN MONTH(trip_date) <= 6 THEN 1 + ELSE 2 + END half + FROM trips + GROUP BY driver_id, half + ) +SELECT + t1.driver_id, + d.driver_name, + ROUND(t1.half_avg, 2) first_half_avg, + ROUND(t2.half_avg, 2) second_half_avg, + ROUND(t2.half_avg - t1.half_avg, 2) efficiency_improvement +FROM + T t1 + JOIN T t2 ON t1.driver_id = t2.driver_id AND t1.half < t2.half AND t1.half_avg < t2.half_avg + JOIN drivers d ON t1.driver_id = d.driver_id +ORDER BY efficiency_improvement DESC, d.driver_name;