Completed_S30_Pandas3

S30_Pandas_03.py

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+#Pandas_03
+
+# 1873. Calculate Special Bonus_Solution_Q1
+
+import pandas as pd
+
+def calculate_special_bonus(employees: pd.DataFrame) -> pd.DataFrame:
+
+    result= []
+    for i in range(len(employees)):
+        e_id= employees['employee_id'][i]
+        name= employees['name'][i]
+        if (e_id%2 != 0) and (name[0] != 'M'):
+            result.append([e_id, employees['salary'][i]])
+        else:
+            result.append([e_id, 0])
+    return pd.DataFrame(result, columns= ['employee_id', 'bonus']).sort_values(by= 'employee_id')
+
+#Alternative1
+
+import pandas as pd
+
+def calculate_special_bonus(employees: pd.DataFrame) -> pd.DataFrame:
+
+    for i in range(len(employees)):
+        e_id= employees['employee_id'][i]
+        name= employees['name'][i]
+        if (e_id %2 == 0) or (name[0]=='M'):
+            employees['salary'][i]=0
+    df = employees[['employee_id', 'salary']]
+    return df.rename(columns ={'salary': 'bonus'}).sort_values(by =['employee_id'])
+
+______________________________________________________________________________________________________________________________________
+
+# 1667. Fix Names in a Table_Solution_Q2
+
+import pandas as pd
+
+def fix_names(users: pd.DataFrame) -> pd.DataFrame:
+    users['name'] = users['name'].str.capitalize()
+    return users.sort_values(by= ['user_id'])
+
+______________________________________________________________________________________________________________________________________
+
+# 1527. Patients With a Condition_Solution_Q3
+
+import pandas as pd
+def find_patients(patients: pd.DataFrame) -> pd.DataFrame:
+    return patients[patients["conditions"].str.startswith("DIAB1") | patients["conditions"].str.contains(" DIAB1", regex=False)]
+
+#Alternative1
+
+import pandas as pd
+def find_patients(patients: pd.DataFrame) -> pd.DataFrame:
+    result= []
+    for i in range(len(patients)):
+        p_id = patients['patient_id'][i]
+        p_name= patients['patient_name'][i]
+        conditions = patients['conditions'][i]
+        for condition in conditions.split():
+            if condition.startswith('DIAB1'):
+                result.append([p_id, p_name, conditions])
+    return pd.DataFrame(result, columns =['patient_id', 'patient_name', 'conditions'])