diff --git a/solution/3600-3699/3657.Find Loyal Customers/README.md b/solution/3600-3699/3657.Find Loyal Customers/README.md
index 5dc6309322af3..7c5d9f6060fef 100644
--- a/solution/3600-3699/3657.Find Loyal Customers/README.md	
+++ b/solution/3600-3699/3657.Find Loyal Customers/README.md	
@@ -20,7 +20,7 @@ tags:
 
 <pre>
 +------------------+---------+
-| Column Name      | Type    | 
+| Column Name      | Type    |
 +------------------+---------+
 | transaction_id   | int     |
 | customer_id      | int     |
@@ -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 -->
diff --git a/solution/3600-3699/3657.Find Loyal Customers/README_EN.md b/solution/3600-3699/3657.Find Loyal Customers/README_EN.md
index bdec9dcde0275..f9f93ebcd6716 100644
--- a/solution/3600-3699/3657.Find Loyal Customers/README_EN.md	
+++ b/solution/3600-3699/3657.Find Loyal Customers/README_EN.md	
@@ -20,7 +20,7 @@ tags:
 
 <pre>
 +------------------+---------+
-| Column Name      | Type    | 
+| Column Name      | Type    |
 +------------------+---------+
 | transaction_id   | int     |
 | customer_id      | int     |
@@ -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 -->
diff --git a/solution/3600-3699/3657.Find Loyal Customers/Solution.py b/solution/3600-3699/3657.Find Loyal Customers/Solution.py
new file mode 100644
index 0000000000000..05d8b46f5c0ae
--- /dev/null
+++ b/solution/3600-3699/3657.Find Loyal Customers/Solution.py	
@@ -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
diff --git a/solution/3600-3699/3657.Find Loyal Customers/Solution.sql b/solution/3600-3699/3657.Find Loyal Customers/Solution.sql
new file mode 100644
index 0000000000000..8e2763b7412a0
--- /dev/null
+++ b/solution/3600-3699/3657.Find Loyal Customers/Solution.sql	
@@ -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;