Pandas3

Calculate Special Bonus

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+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'])
+    #df =employees['name']
+
+#df[''] ==df[''] is an iterable object -->Series
+#from df extract a list 
+      employees['bonus'] =employees.apply(lambda x: x['salary'] if x ['employee_id'] % 2 and not x['name'].startswith('M') else 0, axis =1)
+      return employees[['employee_id','bonus']].sort_values(by ='employee_id')

Fix Names in a Table

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+import pandas as pd
+
+def fix_names(users: pd.DataFrame) -> pd.DataFrame:
+    # users['name'] = users['name'].str[0].str.upper() + users['name'].str[1:].str.lower()
+    # return users.sort_values(by=['user_id'])
+
+    #easy way - use capitalize method. 
+    users['name'] = users['name'].str.capitalize()
+    return users.sort_values(by=['user_id'])

Patients With a Condition

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+import pandas as pd
+
+def find_patients(patients: pd.DataFrame) -> pd.DataFrame:
+    #condition - diab1 is there for diabetes 
+    #follwoing is a long way of solving the query
+    #1
+
+    # 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 conditions.startswith('DIAB1'):
+    #             result.append([p_id,p_name,condition])
+    #             break
+    # return pd.DataFrame(result,columns =['patient_id', 'patient_name','conditions'])
+
+    #a little shorter approach
+
+    #2
+   df = patients[(patients['conditions'].str.startswith('DIAB1')) | (patients['conditions'].str.contains('DIAB1'))]
+   return df
+
+    #the most simplest way  - 
+    #3
+    #return patients[patients['conditions'].str.contains(r"(^|\s)DIAB1", regex=True)]
+