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Merged
merged 1 commit into from
Aug 24, 2025
Merged

feat: add solution to lc problem: No.3657 #4665

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41 changes: 40 additions & 1 deletion solution/3600-3699/3657.Find Loyal Customers/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ tags:

<pre>
+------------------+---------+
| Column Name | Type |
| Column Name | Type |
+------------------+---------+
| transaction_id | int |
| customer_id | int |
Expand Down Expand Up @@ -146,7 +146,46 @@ transaction_type can be either &#39;purchase&#39; or &#39;refund&#39;.
#### MySQL

```sql
# Write your MySQL query statement below
SELECT customer_id
FROM customer_transactions
GROUP BY 1
HAVING
COUNT(1) >= 3
AND SUM(transaction_type = 'refund') / COUNT(1) < 0.2
AND DATEDIFF(MAX(transaction_date), MIN(transaction_date)) >= 30
ORDER BY 1;
```

#### Pandas

```python
import pandas as pd


def find_loyal_customers(customer_transactions: pd.DataFrame) -> pd.DataFrame:
customer_transactions["transaction_date"] = pd.to_datetime(
customer_transactions["transaction_date"]
)
grouped = customer_transactions.groupby("customer_id")
agg_df = grouped.agg(
total_transactions=("transaction_type", "size"),
refund_count=("transaction_type", lambda x: (x == "refund").sum()),
min_date=("transaction_date", "min"),
max_date=("transaction_date", "max"),
).reset_index()
agg_df["date_diff"] = (agg_df["max_date"] - agg_df["min_date"]).dt.days
agg_df["refund_ratio"] = agg_df["refund_count"] / agg_df["total_transactions"]
result = (
agg_df[
(agg_df["total_transactions"] >= 3)
& (agg_df["refund_ratio"] < 0.2)
& (agg_df["date_diff"] >= 30)
][["customer_id"]]
.sort_values("customer_id")
.reset_index(drop=True)
)
return result
```

<!-- tabs:end -->
Expand Down
41 changes: 40 additions & 1 deletion solution/3600-3699/3657.Find Loyal Customers/README_EN.md
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ tags:

<pre>
+------------------+---------+
| Column Name | Type |
| Column Name | Type |
+------------------+---------+
| transaction_id | int |
| customer_id | int |
Expand Down Expand Up @@ -146,7 +146,46 @@ transaction_type can be either &#39;purchase&#39; or &#39;refund&#39;.
#### MySQL

```sql
# Write your MySQL query statement below
SELECT customer_id
FROM customer_transactions
GROUP BY 1
HAVING
COUNT(1) >= 3
AND SUM(transaction_type = 'refund') / COUNT(1) < 0.2
AND DATEDIFF(MAX(transaction_date), MIN(transaction_date)) >= 30
ORDER BY 1;
```

#### Pandas

```python
import pandas as pd


def find_loyal_customers(customer_transactions: pd.DataFrame) -> pd.DataFrame:
customer_transactions["transaction_date"] = pd.to_datetime(
customer_transactions["transaction_date"]
)
grouped = customer_transactions.groupby("customer_id")
agg_df = grouped.agg(
total_transactions=("transaction_type", "size"),
refund_count=("transaction_type", lambda x: (x == "refund").sum()),
min_date=("transaction_date", "min"),
max_date=("transaction_date", "max"),
).reset_index()
agg_df["date_diff"] = (agg_df["max_date"] - agg_df["min_date"]).dt.days
agg_df["refund_ratio"] = agg_df["refund_count"] / agg_df["total_transactions"]
result = (
agg_df[
(agg_df["total_transactions"] >= 3)
& (agg_df["refund_ratio"] < 0.2)
& (agg_df["date_diff"] >= 30)
][["customer_id"]]
.sort_values("customer_id")
.reset_index(drop=True)
)
return result
```

<!-- tabs:end -->
Expand Down
26 changes: 26 additions & 0 deletions solution/3600-3699/3657.Find Loyal Customers/Solution.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,26 @@
import pandas as pd


def find_loyal_customers(customer_transactions: pd.DataFrame) -> pd.DataFrame:
customer_transactions["transaction_date"] = pd.to_datetime(
customer_transactions["transaction_date"]
)
grouped = customer_transactions.groupby("customer_id")
agg_df = grouped.agg(
total_transactions=("transaction_type", "size"),
refund_count=("transaction_type", lambda x: (x == "refund").sum()),
min_date=("transaction_date", "min"),
max_date=("transaction_date", "max"),
).reset_index()
agg_df["date_diff"] = (agg_df["max_date"] - agg_df["min_date"]).dt.days
agg_df["refund_ratio"] = agg_df["refund_count"] / agg_df["total_transactions"]
result = (
agg_df[
(agg_df["total_transactions"] >= 3)
& (agg_df["refund_ratio"] < 0.2)
& (agg_df["date_diff"] >= 30)
][["customer_id"]]
.sort_values("customer_id")
.reset_index(drop=True)
)
return result
9 changes: 9 additions & 0 deletions solution/3600-3699/3657.Find Loyal Customers/Solution.sql
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Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,9 @@
# Write your MySQL query statement below
SELECT customer_id
FROM customer_transactions
GROUP BY 1
HAVING
COUNT(1) >= 3
AND SUM(transaction_type = 'refund') / COUNT(1) < 0.2
AND DATEDIFF(MAX(transaction_date), MIN(transaction_date)) >= 30
ORDER BY 1;