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merged 1 commit into from
Jun 9, 2025
Merged

feat: update solution to lc problem: No.3580 #4473

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82 changes: 36 additions & 46 deletions solution/3500-3599/3580.Find Consistently Improving Employees/README.md
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -174,33 +174,27 @@ WITH
recent AS (
SELECT
employee_id,
rating,
review_date,
ROW_NUMBER() OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) AS rn,
LAG(rating) OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) AS prev_rating
(
LAG(rating) OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) - rating
) AS delta
FROM performance_reviews
),
deltas AS (
SELECT
employee_id,
prev_rating - rating AS delta,
rn
FROM recent
WHERE rn > 1 AND rn <= 3
)
SELECT
employee_id,
name,
SUM(delta) AS improvement_score
FROM
deltas
recent
JOIN employees USING (employee_id)
WHERE rn > 1 AND rn <= 3
GROUP BY 1
HAVING COUNT(*) = 2 AND MIN(delta) > 0
ORDER BY 3 DESC, 2;
Expand All @@ -215,42 +209,38 @@ import pandas as pd
def find_consistently_improving_employees(
employees: pd.DataFrame, performance_reviews: pd.DataFrame
) -> pd.DataFrame:
recent = (
performance_reviews.sort_values(
["employee_id", "review_date"], ascending=[True, False]
)
.groupby("employee_id")
.head(3)
performance_reviews = performance_reviews.sort_values(
["employee_id", "review_date"], ascending=[True, False]
)

three_reviews_ids = recent["employee_id"].value_counts().loc[lambda s: s == 3].index
recent = recent[recent["employee_id"].isin(three_reviews_ids)]
recent = recent.sort_values(["employee_id", "review_date"])

def strictly_increasing(ratings: pd.Series) -> bool:
return (ratings.diff().dropna() > 0).all()

improving_ids = (
recent.groupby("employee_id")["rating"]
.apply(strictly_increasing)
.loc[lambda s: s]
.index
performance_reviews["rn"] = (
performance_reviews.groupby("employee_id").cumcount() + 1
)
improving = recent[recent["employee_id"].isin(improving_ids)]

scores = (
improving.groupby("employee_id")["rating"]
.agg(lambda x: x.iloc[-1] - x.iloc[0])
.reset_index(name="improvement_score")
performance_reviews["lag_rating"] = performance_reviews.groupby("employee_id")[
"rating"
].shift(1)
performance_reviews["delta"] = (
performance_reviews["lag_rating"] - performance_reviews["rating"]
)

result = (
scores.merge(employees, on="employee_id")
.loc[:, ["employee_id", "name", "improvement_score"]]
.sort_values(["improvement_score", "name"], ascending=[False, True])
.reset_index(drop=True)
recent = performance_reviews[
(performance_reviews["rn"] > 1) & (performance_reviews["rn"] <= 3)
]
improvement = (
recent.groupby("employee_id")
.agg(
improvement_score=("delta", "sum"),
count=("delta", "count"),
min_delta=("delta", "min"),
)
.reset_index()
)
improvement = improvement[
(improvement["count"] == 2) & (improvement["min_delta"] > 0)
]
result = improvement.merge(employees[["employee_id", "name"]], on="employee_id")
result = result.sort_values(
by=["improvement_score", "name"], ascending=[False, True]
)
return result
return result[["employee_id", "name", "improvement_score"]]
```

<!-- tabs:end -->
Expand Down
82 changes: 36 additions & 46 deletions solution/3500-3599/3580.Find Consistently Improving Employees/README_EN.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -174,33 +174,27 @@ WITH
recent AS (
SELECT
employee_id,
rating,
review_date,
ROW_NUMBER() OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) AS rn,
LAG(rating) OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) AS prev_rating
(
LAG(rating) OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) - rating
) AS delta
FROM performance_reviews
),
deltas AS (
SELECT
employee_id,
prev_rating - rating AS delta,
rn
FROM recent
WHERE rn > 1 AND rn <= 3
)
SELECT
employee_id,
name,
SUM(delta) AS improvement_score
FROM
deltas
recent
JOIN employees USING (employee_id)
WHERE rn > 1 AND rn <= 3
GROUP BY 1
HAVING COUNT(*) = 2 AND MIN(delta) > 0
ORDER BY 3 DESC, 2;
Expand All @@ -215,42 +209,38 @@ import pandas as pd
def find_consistently_improving_employees(
employees: pd.DataFrame, performance_reviews: pd.DataFrame
) -> pd.DataFrame:
recent = (
performance_reviews.sort_values(
["employee_id", "review_date"], ascending=[True, False]
)
.groupby("employee_id")
.head(3)
performance_reviews = performance_reviews.sort_values(
["employee_id", "review_date"], ascending=[True, False]
)

three_reviews_ids = recent["employee_id"].value_counts().loc[lambda s: s == 3].index
recent = recent[recent["employee_id"].isin(three_reviews_ids)]
recent = recent.sort_values(["employee_id", "review_date"])

def strictly_increasing(ratings: pd.Series) -> bool:
return (ratings.diff().dropna() > 0).all()

improving_ids = (
recent.groupby("employee_id")["rating"]
.apply(strictly_increasing)
.loc[lambda s: s]
.index
performance_reviews["rn"] = (
performance_reviews.groupby("employee_id").cumcount() + 1
)
improving = recent[recent["employee_id"].isin(improving_ids)]

scores = (
improving.groupby("employee_id")["rating"]
.agg(lambda x: x.iloc[-1] - x.iloc[0])
.reset_index(name="improvement_score")
performance_reviews["lag_rating"] = performance_reviews.groupby("employee_id")[
"rating"
].shift(1)
performance_reviews["delta"] = (
performance_reviews["lag_rating"] - performance_reviews["rating"]
)

result = (
scores.merge(employees, on="employee_id")
.loc[:, ["employee_id", "name", "improvement_score"]]
.sort_values(["improvement_score", "name"], ascending=[False, True])
.reset_index(drop=True)
recent = performance_reviews[
(performance_reviews["rn"] > 1) & (performance_reviews["rn"] <= 3)
]
improvement = (
recent.groupby("employee_id")
.agg(
improvement_score=("delta", "sum"),
count=("delta", "count"),
min_delta=("delta", "min"),
)
.reset_index()
)
improvement = improvement[
(improvement["count"] == 2) & (improvement["min_delta"] > 0)
]
result = improvement.merge(employees[["employee_id", "name"]], on="employee_id")
result = result.sort_values(
by=["improvement_score", "name"], ascending=[False, True]
)
return result
return result[["employee_id", "name", "improvement_score"]]
```

<!-- tabs:end -->
Expand Down
60 changes: 28 additions & 32 deletions solution/3500-3599/3580.Find Consistently Improving Employees/Solution.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,39 +4,35 @@
def find_consistently_improving_employees(
employees: pd.DataFrame, performance_reviews: pd.DataFrame
) -> pd.DataFrame:
recent = (
performance_reviews.sort_values(
["employee_id", "review_date"], ascending=[True, False]
)
.groupby("employee_id")
.head(3)
performance_reviews = performance_reviews.sort_values(
["employee_id", "review_date"], ascending=[True, False]
)

three_reviews_ids = recent["employee_id"].value_counts().loc[lambda s: s == 3].index
recent = recent[recent["employee_id"].isin(three_reviews_ids)]
recent = recent.sort_values(["employee_id", "review_date"])

def strictly_increasing(ratings: pd.Series) -> bool:
return (ratings.diff().dropna() > 0).all()

improving_ids = (
recent.groupby("employee_id")["rating"]
.apply(strictly_increasing)
.loc[lambda s: s]
.index
performance_reviews["rn"] = (
performance_reviews.groupby("employee_id").cumcount() + 1
)
improving = recent[recent["employee_id"].isin(improving_ids)]

scores = (
improving.groupby("employee_id")["rating"]
.agg(lambda x: x.iloc[-1] - x.iloc[0])
.reset_index(name="improvement_score")
performance_reviews["lag_rating"] = performance_reviews.groupby("employee_id")[
"rating"
].shift(1)
performance_reviews["delta"] = (
performance_reviews["lag_rating"] - performance_reviews["rating"]
)

result = (
scores.merge(employees, on="employee_id")
.loc[:, ["employee_id", "name", "improvement_score"]]
.sort_values(["improvement_score", "name"], ascending=[False, True])
.reset_index(drop=True)
recent = performance_reviews[
(performance_reviews["rn"] > 1) & (performance_reviews["rn"] <= 3)
]
improvement = (
recent.groupby("employee_id")
.agg(
improvement_score=("delta", "sum"),
count=("delta", "count"),
min_delta=("delta", "min"),
)
.reset_index()
)
improvement = improvement[
(improvement["count"] == 2) & (improvement["min_delta"] > 0)
]
result = improvement.merge(employees[["employee_id", "name"]], on="employee_id")
result = result.sort_values(
by=["improvement_score", "name"], ascending=[False, True]
)
return result
return result[["employee_id", "name", "improvement_score"]]
22 changes: 8 additions & 14 deletions solution/3500-3599/3580.Find Consistently Improving Employees/Solution.sql
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,33 +2,27 @@ WITH
recent AS (
SELECT
employee_id,
rating,
review_date,
ROW_NUMBER() OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) AS rn,
LAG(rating) OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) AS prev_rating
(
LAG(rating) OVER (
PARTITION BY employee_id
ORDER BY review_date DESC
) - rating
) AS delta
FROM performance_reviews
),
deltas AS (
SELECT
employee_id,
prev_rating - rating AS delta,
rn
FROM recent
WHERE rn > 1 AND rn <= 3
)
SELECT
employee_id,
name,
SUM(delta) AS improvement_score
FROM
deltas
recent
JOIN employees USING (employee_id)
WHERE rn > 1 AND rn <= 3
GROUP BY 1
HAVING COUNT(*) = 2 AND MIN(delta) > 0
ORDER BY 3 DESC, 2;