Completed_S30_Pandas_05

S30_Pandas_05.py

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+# Pandas_05
+
+# 184. Department Highest Salary_Solution_Q1
+
+import pandas as pd
+
+def department_highest_salary(employee: pd.DataFrame, department: pd.DataFrame) -> pd.DataFrame:
+    salaryDict={}
+    for i in range(len(employee)):
+        salary = employee['salary'][i]
+        d_id = employee['departmentId'][i]
+        if d_id in salaryDict:
+            if salary > salaryDict[d_id]:
+                salaryDict[d_id]= salary
+        else:
+            salaryDict[d_id] = salary
+
+    deptDict = {}
+    for i in range(len(department)):
+        name = department['name'][i]
+        d_id = department['id'][i]
+        deptDict[d_id]= name
+
+    result =[]
+    for i in range(len(employee)):
+        salary = employee['salary'][i]
+        d_id = employee['departmentId'][i]
+        name = employee['name'][i]
+        if salary == salaryDict[d_id]:
+            result.append([d_id, name, salary])
+
+    for element in result:
+        d_id = element[0]
+        element[0] = deptDict[d_id]
+
+    return pd.DataFrame(result, columns = ['Department', 'Employee', 'Salary'])
+
+#Alternative1
+
+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_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'})
+
+______________________________________________________________________________________________________________________________________
+
+# 178. Rank Scores_Solution_Q2
+
+import pandas as pd
+
+def order_scores(scores: pd.DataFrame) -> pd.DataFrame:
+
+    scores['rank'] = scores['score'].rank(method='dense', ascending=False)
+    return scores[['score', 'rank']].sort_values('score', ascending=False)
+
+#Alternative1
+
+import pandas as pd
+
+def order_scores(scores: pd.DataFrame) -> pd.DataFrame:
+    allScore=[]
+
+    for i in range(len(scores)):
+        s= scores['score'][i]
+        allScore.append(s)
+    allScore.sort(reverse= True)
+    if(len(allScore)) ==0:
+        return pd.DataFrame([], columns= ['score', 'rank'])
+    result=[]
+    rnk=1
+    result.append([allScore[0],1])
+    for i in range(1, len(allScore)):
+        if allScore[i] != allScore[i-1]:
+            rnk= rnk+1
+        result.append([allScore[i], rnk])
+    return pd.DataFrame(result, columns= ['score', 'rank'])
+
+______________________________________________________________________________________________________________________________________
+
+
+