feat: add solutions to lc problem: No.2117

solution/0100-0199/0175.Combine Two Tables/README.md

@@ -96,15 +96,16 @@ FROM
     LEFT JOIN Address USING (personId);
 ```
 
-```pandas
+### **Pandas**
+
+```python
 import pandas as pd
 
 
 def combine_two_tables(person: pd.DataFrame, address: pd.DataFrame) -> pd.DataFrame:
     return pd.merge(left=person, right=address, how="left", on="personId")[
         ["firstName", "lastName", "city", "state"]
     ]
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0175.Combine Two Tables/README_EN.md

@@ -92,7 +92,9 @@ FROM
     LEFT JOIN Address USING (personId);
 ```
 
-```pandas
+### **Pandas**
+
+```python
 import pandas as pd
 
 

solution/0100-0199/0176.Second Highest Salary/README.md

@@ -113,6 +113,8 @@ WITH T AS (SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rk FROM Em
 SELECT (SELECT DISTINCT salary FROM T WHERE rk = 2) AS SecondHighestSalary;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -134,7 +136,6 @@ def second_highest_salary(employee: pd.DataFrame) -> pd.DataFrame:
     result_df = pd.DataFrame({"SecondHighestSalary": [second_highest]})
 
     return result_df
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0176.Second Highest Salary/README_EN.md

@@ -98,6 +98,8 @@ WITH T AS (SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rk FROM Em
 SELECT (SELECT DISTINCT salary FROM T WHERE rk = 2) AS SecondHighestSalary;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -119,7 +121,6 @@ def second_highest_salary(employee: pd.DataFrame) -> pd.DataFrame:
     result_df = pd.DataFrame({"SecondHighestSalary": [second_highest]})
 
     return result_df
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0177.Nth Highest Salary/README.md

@@ -94,6 +94,8 @@ BEGIN
 END
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -105,7 +107,6 @@ def nth_highest_salary(employee: pd.DataFrame, N: int) -> pd.DataFrame:
     else:
         salary = sorted(unique_salaries, reverse=True)[N - 1]
         return pd.DataFrame([salary], columns=[f"getNthHighestSalary({N})"])
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0177.Nth Highest Salary/README_EN.md

@@ -86,6 +86,8 @@ BEGIN
 END
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -97,7 +99,6 @@ def nth_highest_salary(employee: pd.DataFrame, N: int) -> pd.DataFrame:
     else:
         salary = sorted(unique_salaries, reverse=True)[N - 1]
         return pd.DataFrame([salary], columns=[f"getNthHighestSalary({N})"])
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0178.Rank Scores/README.md

@@ -122,6 +122,8 @@ FROM
     ) s;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -134,7 +136,6 @@ def order_scores(scores: pd.DataFrame) -> pd.DataFrame:
     result_df = scores.drop("id", axis=1).sort_values(by="score", ascending=False)
 
     return result_df
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0178.Rank Scores/README_EN.md

@@ -113,6 +113,8 @@ FROM
     ) s;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -125,7 +127,6 @@ def order_scores(scores: pd.DataFrame) -> pd.DataFrame:
     result_df = scores.drop("id", axis=1).sort_values(by="score", ascending=False)
 
     return result_df
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0180.Consecutive Numbers/README.md

@@ -116,6 +116,8 @@ GROUP BY p
 HAVING COUNT(1) >= 3;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -130,7 +132,6 @@ def consecutive_numbers(logs: pd.DataFrame) -> pd.DataFrame:
         .drop_duplicates()
         .rename(columns={"num": "ConsecutiveNums"})
     )
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0180.Consecutive Numbers/README_EN.md

@@ -112,6 +112,8 @@ GROUP BY p
 HAVING COUNT(1) >= 3;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -126,7 +128,6 @@ def consecutive_numbers(logs: pd.DataFrame) -> pd.DataFrame:
         .drop_duplicates()
         .rename(columns={"num": "ConsecutiveNums"})
     )
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0181.Employees Earning More Than Their Managers/README.md

@@ -81,6 +81,8 @@ FROM
 WHERE e1.salary > e2.salary;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -90,7 +92,6 @@ def find_employees(employee: pd.DataFrame) -> pd.DataFrame:
     emp = df[df["salary_x"] > df["salary_y"]]["name_x"]
 
     return pd.DataFrame({"Employee": emp})
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0181.Employees Earning More Than Their Managers/README_EN.md

@@ -77,6 +77,8 @@ FROM
 WHERE e1.salary > e2.salary;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 

solution/0100-0199/0182.Duplicate Emails/README.md

@@ -83,6 +83,8 @@ FROM
 WHERE p1.id != p2.id AND p1.email = p2.email;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -93,7 +95,6 @@ def duplicate_emails(person: pd.DataFrame) -> pd.DataFrame:
     results = person.loc[person.duplicated(subset=["email"]), ["email"]]
 
     return results.drop_duplicates()
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0182.Duplicate Emails/README_EN.md

@@ -77,6 +77,8 @@ FROM
 WHERE p1.id != p2.id AND p1.email = p2.email;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -87,7 +89,6 @@ def duplicate_emails(person: pd.DataFrame) -> pd.DataFrame:
     results = person.loc[person.duplicated(subset=["email"]), ["email"]]
 
     return results.drop_duplicates()
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0183.Customers Who Never Order/README.md

@@ -105,6 +105,8 @@ FROM
 WHERE o.id IS NULL;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -117,7 +119,6 @@ def find_customers(customers: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFram
     df = df[["name"]].rename(columns={"name": "Customers"})
 
     return df
-
 ```
 
 <!-- tabs:end -->

solution/0100-0199/0183.Customers Who Never Order/README_EN.md

@@ -105,6 +105,8 @@ FROM
 WHERE o.id IS NULL;
 ```
 
+### **Pandas**
+
 ```python
 import pandas as pd
 
@@ -117,7 +119,6 @@ def find_customers(customers: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFram
     df = df[["name"]].rename(columns={"name": "Customers"})
 
     return df
-
 ```
 
 <!-- tabs:end -->