solution to both the problems

01-DepartmentHighestSalary.py

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+# Problem 1: Department Highest Salary (https://leetcode.com/problems/department-highest-salary/)
+
+import pandas as pd
+
+# solution 1 using groupby and merge
+def department_highest_salary(employee: pd.DataFrame, department: pd.DataFrame) -> pd.DataFrame:
+    max_salary = employee.groupby('departmentId')['salary'].max().reset_index()
+    result = pd.merge(max_salary, employee)
+    output = pd.merge(result, department, left_on=['departmentId'], right_on=['id'])
+    return output[['name_y', 'name_x', 'salary']].rename(columns={'name_y':'Department', 'name_x':'Employee', 'salary':'Salary'})
+
+# solution 2 using transform and merge
+def department_highest_salary(employee: pd.DataFrame, department: pd.DataFrame) -> pd.DataFrame:
+    df = pd.merge(employee, department, left_on='departmentId', right_on='id')
+    max_salary = df.groupby('departmentId')['salary'].transform('max')
+    df = df[df['salary']==max_salary]
+    return df[['name_y', 'name_x', 'salary']].rename(columns={'name_y':'Department', 'name_x':'Employee', 'salary':'Salary'})
+
+"""
+Note:
+- transform returns a series (it is like partition by in SQL), so it returns the same number of rows as the original DataFrame.
+- the default merge is an inner join, so it is fine to omit how='inner', it will only return the rows that have matching values in both DataFrames.
+"""

02-RankScores.py

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+# Problem 2: Rank Scores (https://leetcode.com/problems/rank-scores/)
+
+import pandas as pd
+
+# solution 1 using rank
+def order_scores(scores: pd.DataFrame) -> pd.DataFrame:
+    scores['rank'] = scores['score'].rank(method='dense', ascending=False)
+    return scores[['score', 'rank']].sort_values(by='score', ascending=False)