From da6d02e6ef970bcccf22a1f9d5e274b93ec0465d Mon Sep 17 00:00:00 2001
From: Vichitravir Dwivedi <93407819+vichitravird@users.noreply.github.com>
Date: Sun, 6 Oct 2024 20:53:49 -0500
Subject: [PATCH] Update README.md

---
 README.md | 38 +++++++++++++++++++++++++++++++++++---
 1 file changed, 35 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index ded7a6c..4640cbf 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,39 @@
-# Pandas5
+## Pandas5
 
-1 Problem 1 : Department Highest Salary (https://leetcode.com/problems/department-highest-salary/ )
+### 1 Problem 1 : Department Highest Salary (https://leetcode.com/problems/department-highest-salary/ )
+<br>
+import pandas as pd
 
-2 Problem 2 : Rank Scores ( https://leetcode.com/problems/rank-scores/ )
+def department_highest_salary(employee: pd.DataFrame, department: pd.DataFrame) -> pd.DataFrame:
+    if employee.empty or department.empty:
+        return pd.DataFrame(columns=['Department','Employee', 'Salary'])
+    
+    # Merge the employee and department DataFrames on 'departmentId' and 'id' columns
+    merged_df = employee.merge(department, left_on='departmentId', right_on='id', suffixes=('_employee', '_department'))
+    
+    # Use groupby to group data by 'departmentId' and apply a lambda function to get employees with highest salary in each group
+    highest_salary_df = merged_df.groupby('departmentId').apply(lambda x: x[x['salary'] == x['salary'].max()])
+    
+    # Drop the duplicate 'departmentId' column and reset the index
+    highest_salary_df = highest_salary_df.reset_index(drop=True)
+    
+    # Select the required columns and return the result
+    result_df = highest_salary_df[['name_department', 'name_employee', 'salary']]
+    
+    # Rename the columns as specified
+    result_df.columns = ['Department','Employee', 'Salary']
+    
+    return result_df
 
+### 2 Problem 2 : Rank Scores ( https://leetcode.com/problems/rank-scores/ )
+<br>
+
+def order_scores(scores: pd.DataFrame) -> pd.DataFrame:
+    # Use the rank method to assign ranks to the scores in descending order with no gaps
+    scores['rank'] = scores['score'].rank(method='dense', ascending=False)
+    
+    # Drop id column & Sort the DataFrame by score in descending order
+    result_df = scores.drop('id',axis=1).sort_values(by='score', ascending=False)
+    
+    return result_df