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feat: add solutions to lc problem: No.3601 (#4543) 

No.3601.Find Drivers with Improved Fuel Efficiency
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Libin YANG 2025-07-03 07:22:35 +08:00 committed by GitHub
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70
solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README.md
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@ -152,14 +152,82 @@ Each row represents a trip made by a driver, including the distance traveled and
<!-- solution:start -->
### 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。
<!-- tabs:start -->
#### 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",
]
]
```
<!-- tabs:end -->

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solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README_EN.md
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@ -152,14 +152,84 @@ Each row represents a trip made by a driver, including the distance traveled and
<!-- solution:start -->
### 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.
<!-- tabs:start -->
#### 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",
]
]
```
<!-- tabs:end -->

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solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.py Normal file
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@ -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",
]
]

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solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.sql Normal file
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@ -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;