Pandas5: Problem Solutions_RN32MAY2025DS

DepartmentHighestSalary.py

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+# Method 1
+import pandas as pd
+
+def department_highest_salary(employee: pd.DataFrame, department: pd.DataFrame) -> pd.DataFrame:
+    # creating a dictionary with department ID and it's max salary
+    dept_sal = {}
+    for i in range(len(employee)):
+        d = employee['departmentId'][i]
+        s = employee['salary'][i]
+
+        if d in dept_sal.keys():
+            if dept_sal[d] < s:
+                dept_sal[d] = s
+        else:
+            dept_sal[d] = s
+
+    # print(dept_sal)
+
+    # Checking which emloyees have max salary for each department
+    result = []
+    for i in range(len(employee)):
+        d = employee['departmentId'][i]
+        s = employee['salary'][i]
+        n = employee['name'][i]
+
+        if s == dept_sal[d]:
+            result.append([d,n,s])
+
+    # print(result)
+
+    # Capturing the department name from department table
+    final_result = []
+    for i in range(len(department)):
+        id = department['id'][i]
+        name = department['name'][i]
+
+        for e in result:
+            if id == e[0]:
+                final_result.append([name,e[1],e[2]])
+
+    print(final_result)
+
+    return pd.DataFrame(final_result, columns = ['department','employee','salary'])
+
+# Method 2
+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', how = 'inner')
+    max_sal = df.groupby('departmentId')['salary'].transform('max') # SELECT MAX(salary) FROM df GROUP BY departmentId
+
+    # print(max_sal)
+
+    condition = df['salary'] == max_sal
+    df = df[condition]
+
+    return df[['name_y','name_x','salary']].rename(columns = {'name_y':'department','name_x':'employee','salary':'salary'})

RankScores.py

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+import pandas as pd
+
+def order_scores(scores: pd.DataFrame) -> pd.DataFrame:
+    scores['rank'] = scores['score'].rank(method = 'dense', ascending = False)
+    df = scores[['score','rank']].sort_values('score',ascending = False)
+
+    return df