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Commit 94174b4

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feat: add solution to lc problem: No.3657 (#4665)
No.3657.Find Loyal Customers
1 parent cf894ad commit 94174b4

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4 files changed

+115
-2
lines changed

4 files changed

+115
-2
lines changed

‎solution/3600-3699/3657.Find Loyal Customers/README.md

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2020

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<pre>
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+------------------+---------+
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| Column Name | Type |
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| Column Name | Type |
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+------------------+---------+
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| transaction_id | int |
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| customer_id | int |
@@ -146,7 +146,46 @@ transaction_type can be either &#39;purchase&#39; or &#39;refund&#39;.
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#### MySQL
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```sql
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# Write your MySQL query statement below
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SELECT customer_id
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FROM customer_transactions
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GROUP BY 1
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HAVING
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COUNT(1) >= 3
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AND SUM(transaction_type = 'refund') / COUNT(1) < 0.2
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AND DATEDIFF(MAX(transaction_date), MIN(transaction_date)) >= 30
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ORDER BY 1;
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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_loyal_customers(customer_transactions: pd.DataFrame) -> pd.DataFrame:
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customer_transactions["transaction_date"] = pd.to_datetime(
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customer_transactions["transaction_date"]
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)
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grouped = customer_transactions.groupby("customer_id")
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agg_df = grouped.agg(
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total_transactions=("transaction_type", "size"),
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refund_count=("transaction_type", lambda x: (x == "refund").sum()),
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min_date=("transaction_date", "min"),
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max_date=("transaction_date", "max"),
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).reset_index()
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agg_df["date_diff"] = (agg_df["max_date"] - agg_df["min_date"]).dt.days
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agg_df["refund_ratio"] = agg_df["refund_count"] / agg_df["total_transactions"]
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result = (
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agg_df[
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(agg_df["total_transactions"] >= 3)
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& (agg_df["refund_ratio"] < 0.2)
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& (agg_df["date_diff"] >= 30)
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][["customer_id"]]
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.sort_values("customer_id")
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.reset_index(drop=True)
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)
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return result
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```
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<!-- tabs:end -->

‎solution/3600-3699/3657.Find Loyal Customers/README_EN.md

Lines changed: 40 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -20,7 +20,7 @@ tags:
2020

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<pre>
2222
+------------------+---------+
23-
| Column Name | Type |
23+
| Column Name | Type |
2424
+------------------+---------+
2525
| transaction_id | int |
2626
| customer_id | int |
@@ -146,7 +146,46 @@ transaction_type can be either &#39;purchase&#39; or &#39;refund&#39;.
146146
#### MySQL
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```sql
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# Write your MySQL query statement below
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SELECT customer_id
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FROM customer_transactions
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GROUP BY 1
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HAVING
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COUNT(1) >= 3
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AND SUM(transaction_type = 'refund') / COUNT(1) < 0.2
156+
AND DATEDIFF(MAX(transaction_date), MIN(transaction_date)) >= 30
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ORDER BY 1;
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```
149159

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#### Pandas
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```python
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import pandas as pd
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def find_loyal_customers(customer_transactions: pd.DataFrame) -> pd.DataFrame:
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customer_transactions["transaction_date"] = pd.to_datetime(
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customer_transactions["transaction_date"]
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)
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grouped = customer_transactions.groupby("customer_id")
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agg_df = grouped.agg(
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total_transactions=("transaction_type", "size"),
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refund_count=("transaction_type", lambda x: (x == "refund").sum()),
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min_date=("transaction_date", "min"),
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max_date=("transaction_date", "max"),
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).reset_index()
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agg_df["date_diff"] = (agg_df["max_date"] - agg_df["min_date"]).dt.days
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agg_df["refund_ratio"] = agg_df["refund_count"] / agg_df["total_transactions"]
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result = (
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agg_df[
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(agg_df["total_transactions"] >= 3)
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& (agg_df["refund_ratio"] < 0.2)
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& (agg_df["date_diff"] >= 30)
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][["customer_id"]]
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.sort_values("customer_id")
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.reset_index(drop=True)
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)
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return result
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```
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<!-- tabs:end -->
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@@ -0,0 +1,26 @@
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import pandas as pd
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def find_loyal_customers(customer_transactions: pd.DataFrame) -> pd.DataFrame:
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customer_transactions["transaction_date"] = pd.to_datetime(
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customer_transactions["transaction_date"]
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)
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grouped = customer_transactions.groupby("customer_id")
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agg_df = grouped.agg(
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total_transactions=("transaction_type", "size"),
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refund_count=("transaction_type", lambda x: (x == "refund").sum()),
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min_date=("transaction_date", "min"),
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max_date=("transaction_date", "max"),
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).reset_index()
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agg_df["date_diff"] = (agg_df["max_date"] - agg_df["min_date"]).dt.days
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agg_df["refund_ratio"] = agg_df["refund_count"] / agg_df["total_transactions"]
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result = (
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agg_df[
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(agg_df["total_transactions"] >= 3)
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& (agg_df["refund_ratio"] < 0.2)
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& (agg_df["date_diff"] >= 30)
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][["customer_id"]]
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.sort_values("customer_id")
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.reset_index(drop=True)
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)
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return result
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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,9 @@
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# Write your MySQL query statement below
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SELECT customer_id
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FROM customer_transactions
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GROUP BY 1
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HAVING
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COUNT(1) >= 3
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AND SUM(transaction_type = 'refund') / COUNT(1) < 0.2
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AND DATEDIFF(MAX(transaction_date), MIN(transaction_date)) >= 30
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ORDER BY 1;

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