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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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‎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
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<!-- solution:start -->
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### 方法一
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### 方法一：分组统计 + 连接查询
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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。
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<!-- tabs:start -->
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#### MySQL
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```sql
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# Write your MySQL query statement below
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WITH
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T AS (
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SELECT
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driver_id,
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AVG(distance_km / fuel_consumed) half_avg,
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CASE
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WHEN MONTH(trip_date) <= 6 THEN 1
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ELSE 2
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END half
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FROM trips
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GROUP BY driver_id, half
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)
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SELECT
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t1.driver_id,
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d.driver_name,
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ROUND(t1.half_avg, 2) first_half_avg,
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ROUND(t2.half_avg, 2) second_half_avg,
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ROUND(t2.half_avg - t1.half_avg, 2) efficiency_improvement
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FROM
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T t1
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JOIN T t2 ON t1.driver_id = t2.driver_id AND t1.half < t2.half AND t1.half_avg < t2.half_avg
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JOIN drivers d ON t1.driver_id = d.driver_id
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ORDER BY efficiency_improvement DESC, d.driver_name;
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```
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#### Pandas
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```python
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import pandas as pd
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def find_improved_efficiency_drivers(
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drivers: pd.DataFrame, trips: pd.DataFrame
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) -> pd.DataFrame:
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trips = trips.copy()
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trips["trip_date"] = pd.to_datetime(trips["trip_date"])
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trips["half"] = trips["trip_date"].dt.month.apply(lambda m: 1 if m <= 6 else 2)
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trips["efficiency"] = trips["distance_km"] / trips["fuel_consumed"]
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half_avg = (
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trips.groupby(["driver_id", "half"])["efficiency"]
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.mean()
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.reset_index(name="half_avg")
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)
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pivot = half_avg.pivot(index="driver_id", columns="half", values="half_avg").rename(
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columns={1: "first_half_avg", 2: "second_half_avg"}
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)
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pivot = pivot.dropna()
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pivot = pivot[pivot["second_half_avg"] > pivot["first_half_avg"]]
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pivot["efficiency_improvement"] = (
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pivot["second_half_avg"] - pivot["first_half_avg"]
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).round(2)
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pivot["first_half_avg"] = pivot["first_half_avg"].round(2)
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pivot["second_half_avg"] = pivot["second_half_avg"].round(2)
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result = pivot.reset_index().merge(drivers, on="driver_id")
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result = result.sort_values(
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by=["efficiency_improvement", "driver_name"], ascending=[False, True]
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)
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return result[
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[
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"driver_id",
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"driver_name",
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"first_half_avg",
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"second_half_avg",
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"efficiency_improvement",
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]
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]
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```
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<!-- tabs:end -->

‎solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/README_EN.md

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Original file line numberDiff line numberDiff line change
@@ -152,14 +152,84 @@ Each row represents a trip made by a driver, including the distance traveled and
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<!-- solution:start -->
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### Solution 1
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### Solution 1: Group Aggregation + Join Query
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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.
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Then, we join the results with the `drivers` table, filter out the drivers whose fuel efficiency has improved, and calculate the amount of improvement.
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<!-- tabs:start -->
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#### MySQL
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```sql
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# Write your MySQL query statement below
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WITH
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T AS (
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SELECT
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driver_id,
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AVG(distance_km / fuel_consumed) half_avg,
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CASE
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WHEN MONTH(trip_date) <= 6 THEN 1
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ELSE 2
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END half
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FROM trips
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GROUP BY driver_id, half
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)
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SELECT
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t1.driver_id,
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d.driver_name,
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ROUND(t1.half_avg, 2) first_half_avg,
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ROUND(t2.half_avg, 2) second_half_avg,
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ROUND(t2.half_avg - t1.half_avg, 2) efficiency_improvement
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FROM
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T t1
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JOIN T t2 ON t1.driver_id = t2.driver_id AND t1.half < t2.half AND t1.half_avg < t2.half_avg
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JOIN drivers d ON t1.driver_id = d.driver_id
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ORDER BY efficiency_improvement DESC, d.driver_name;
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```
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#### Pandas
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```python
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import pandas as pd
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def find_improved_efficiency_drivers(
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drivers: pd.DataFrame, trips: pd.DataFrame
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) -> pd.DataFrame:
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trips = trips.copy()
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trips["trip_date"] = pd.to_datetime(trips["trip_date"])
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trips["half"] = trips["trip_date"].dt.month.apply(lambda m: 1 if m <= 6 else 2)
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trips["efficiency"] = trips["distance_km"] / trips["fuel_consumed"]
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half_avg = (
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trips.groupby(["driver_id", "half"])["efficiency"]
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.mean()
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.reset_index(name="half_avg")
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)
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pivot = half_avg.pivot(index="driver_id", columns="half", values="half_avg").rename(
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columns={1: "first_half_avg", 2: "second_half_avg"}
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)
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pivot = pivot.dropna()
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pivot = pivot[pivot["second_half_avg"] > pivot["first_half_avg"]]
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pivot["efficiency_improvement"] = (
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pivot["second_half_avg"] - pivot["first_half_avg"]
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).round(2)
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pivot["first_half_avg"] = pivot["first_half_avg"].round(2)
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pivot["second_half_avg"] = pivot["second_half_avg"].round(2)
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result = pivot.reset_index().merge(drivers, on="driver_id")
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result = result.sort_values(
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by=["efficiency_improvement", "driver_name"], ascending=[False, True]
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)
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return result[
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[
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"driver_id",
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"driver_name",
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"first_half_avg",
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"second_half_avg",
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"efficiency_improvement",
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]
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]
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```
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<!-- tabs:end -->

‎solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.py

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import pandas as pd
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def find_improved_efficiency_drivers(
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drivers: pd.DataFrame, trips: pd.DataFrame
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) -> pd.DataFrame:
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trips = trips.copy()
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trips["trip_date"] = pd.to_datetime(trips["trip_date"])
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trips["half"] = trips["trip_date"].dt.month.apply(lambda m: 1 if m <= 6 else 2)
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trips["efficiency"] = trips["distance_km"] / trips["fuel_consumed"]
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half_avg = (
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trips.groupby(["driver_id", "half"])["efficiency"]
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.mean()
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.reset_index(name="half_avg")
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)
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pivot = half_avg.pivot(index="driver_id", columns="half", values="half_avg").rename(
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columns={1: "first_half_avg", 2: "second_half_avg"}
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)
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pivot = pivot.dropna()
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pivot = pivot[pivot["second_half_avg"] > pivot["first_half_avg"]]
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pivot["efficiency_improvement"] = (
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pivot["second_half_avg"] - pivot["first_half_avg"]
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).round(2)
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pivot["first_half_avg"] = pivot["first_half_avg"].round(2)
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pivot["second_half_avg"] = pivot["second_half_avg"].round(2)
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result = pivot.reset_index().merge(drivers, on="driver_id")
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result = result.sort_values(
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by=["efficiency_improvement", "driver_name"], ascending=[False, True]
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)
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return result[
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[
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"driver_id",
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"driver_name",
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"first_half_avg",
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"second_half_avg",
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"efficiency_improvement",
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]
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]

‎solution/3600-3699/3601.Find Drivers with Improved Fuel Efficiency/Solution.sql

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Original file line numberDiff line numberDiff line change
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# Write your MySQL query statement below
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WITH
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T AS (
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SELECT
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driver_id,
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AVG(distance_km / fuel_consumed) half_avg,
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CASE
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WHEN MONTH(trip_date) <= 6 THEN 1
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ELSE 2
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END half
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FROM trips
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GROUP BY driver_id, half
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)
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SELECT
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t1.driver_id,
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d.driver_name,
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ROUND(t1.half_avg, 2) first_half_avg,
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ROUND(t2.half_avg, 2) second_half_avg,
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ROUND(t2.half_avg - t1.half_avg, 2) efficiency_improvement
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FROM
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T t1
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JOIN T t2 ON t1.driver_id = t2.driver_id AND t1.half < t2.half AND t1.half_avg < t2.half_avg
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JOIN drivers d ON t1.driver_id = d.driver_id
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ORDER BY efficiency_improvement DESC, d.driver_name;

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