From 22458b4de7c8f5968452b698ae085e550e86af45 Mon Sep 17 00:00:00 2001 From: Punya Ira Anand Date: Sun, 2 Mar 2025 18:52:07 -0600 Subject: [PATCH 1/4] Create Department Highest Salary --- Department Highest Salary | 1 + 1 file changed, 1 insertion(+) create mode 100644 Department Highest Salary diff --git a/Department Highest Salary b/Department Highest Salary new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Department Highest Salary @@ -0,0 +1 @@ + From a65bbe1fbd2c273cf53c6984d62a7eab93e70e04 Mon Sep 17 00:00:00 2001 From: Punya Ira Anand Date: Sun, 2 Mar 2025 18:52:24 -0600 Subject: [PATCH 2/4] Create Rank Scores --- Rank Scores | 1 + 1 file changed, 1 insertion(+) create mode 100644 Rank Scores diff --git a/Rank Scores b/Rank Scores new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Rank Scores @@ -0,0 +1 @@ + From dc5b1f9166ec6b8944306bc2328aab40dc32ec54 Mon Sep 17 00:00:00 2001 From: Punya Ira Anand Date: Mon, 3 Mar 2025 18:12:25 -0600 Subject: [PATCH 3/4] Update Department Highest Salary --- Department Highest Salary | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/Department Highest Salary b/Department Highest Salary index 8b13789..df83349 100644 --- a/Department Highest Salary +++ b/Department Highest Salary @@ -1 +1,8 @@ +import pandas as pd + +def department_highest_salary(employee: pd.DataFrame, department: pd.DataFrame) -> pd.DataFrame: + df = employee.merge(department, left_on='departmentId', right_on='id') #creating the dataframe + result = df[df.salary == df.groupby('name_y')['salary'].transform('max')][['name_y', 'name_x', 'salary']] #highest salary + result.columns = ['Department', 'Employee', 'Salary'] #renaming the columns + return result From 5a7db08bf768835ce7c2da01e92078a4968cb5a3 Mon Sep 17 00:00:00 2001 From: Punya Ira Anand Date: Mon, 10 Mar 2025 21:06:36 -0500 Subject: [PATCH 4/4] Update Rank Scores --- Rank Scores | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/Rank Scores b/Rank Scores index 8b13789..e632a31 100644 --- a/Rank Scores +++ b/Rank Scores @@ -1 +1,11 @@ +import pandas as pd + +def order_scores(scores: pd.DataFrame) -> pd.DataFrame: + # Rank, including the duplicates + scores['rank'] = scores['score'].rank(method='dense', ascending=False).astype(int) + + # Sort + scores = scores.sort_values(by='score', ascending=False) + + return scores[['score', 'rank']]