我如何根据部门汇总员工并使用 groupby pandas 显示每个部门的平均工资？

Question

这是我的代码，其中包含我想使用pandas.DataFrame.groupby执行任务的数据

import pandas as pd
data = {'employees_no':  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
        'employees_name': ['Jugal Sompura', 'Maya Rajput', 'Chaitya Panchal', 'Sweta Rampariya', 'Prakshal Patel', 'Dhruv Panchal', 'Prachi Desai', 'Krunal Gosai', 'Hemil Soni', 'Gopal Pithadia', 'Jatin Shah', 'Raj Patel', 'Shreya Desai'],
        'department_name': ['HR', 'Administrative Assistant', 'Production', 'Accountant', 'Production', 'Engineer', 'Finance', 'Engineer', 'Quality Assurance', 'Engineer', 'Engineer', 'Customer Service', 'CEO'],
        'salary': [130000.0, 65000.0, 45000.0, 65000.0, 47000.0, 40000.0, 90000.0, 45000.0, 35000.0, 45000.0, 30000.0, 40000.0, 250000.0]
        }

df = pd.DataFrame (data, columns = ['employees_no', 'employees_name', 'department_name', 'salary'])
print(df)
---------------------------------------------------------------------
employees_no   employees_name           department_name      salary
0              1    Jugal Sompura                        HR  130000.0
1              2      Maya Rajput  Administrative Assistant   65000.0
2              3  Chaitya Panchal                Production   45000.0
3              4  Sweta Rampariya                Accountant   65000.0
4              5   Prakshal Patel                Production   47000.0
5              6    Dhruv Panchal                  Engineer   40000.0
6              7     Prachi Desai                   Finance   90000.0
7              8     Krunal Gosai                  Engineer   45000.0
8              9       Hemil Soni         Quality Assurance   35000.0
9             10   Gopal Pithadia                  Engineer   45000.0
10            11       Jatin Shah                  Engineer   30000.0
11            12        Raj Patel          Customer Service   40000.0
12            13     Shreya Desai                       CEO  250000.0
---------------------------------------------------------------------

我试过了，只能得到这个输出。

print(df.groupby('department_name').agg({'salary':'mean'}))
---------------------------------------------------------------------
department_name             salary
Accountant                 65000.0
Administrative Assistant   65000.0
CEO                       250000.0
Customer Service           40000.0
Engineer                   40000.0
Finance                    90000.0
HR                        130000.0
Production                 46000.0
Quality Assurance          35000.0
---------------------------------------------------------------------

我无法获得这样的输出...

department_name          employees_name     avg_salary        
Accountant               Sweta Rampariya     65000.0
Administrative Assistant Maya Rajput         65000.0
CEO                      Shreya Desai       250000.0
Customer Service         Raj Patel           40000.0
Engineer                 Dhruv Panchal       40000.0
                         Gopal Pithadia      
                         Krunal Gosai        
                         Jatin Shah     
Finance                  Prachi Desai        90000.0
HR                       Jugal Sompura      130000.0
Production               Chaitya Panchal     46000.0
                         Prakshal Patel      
Quality Assurance        Hemil Soni          35000.0

你能帮我解决这个问题吗？

Answer 1

你需要pandas.DataFrame.groupby.transform：

df["avg_salary"] = df.groupby("department_name")["salary"].transform("mean")
new_df = df.set_index(["department_name", "employees_name"]).sort_index()
print(new_df["avg_salary"])

输出：

department_name           employees_name 
Accountant                Sweta Rampariya     65000.0
Administrative Assistant  Maya Rajput         65000.0
CEO                       Shreya Desai       250000.0
Customer Service          Raj Patel           40000.0
Engineer                  Dhruv Panchal       40000.0
                          Gopal Pithadia      40000.0
                          Jatin Shah          40000.0
                          Krunal Gosai        40000.0
Finance                   Prachi Desai        90000.0
HR                        Jugal Sompura      130000.0
Production                Chaitya Panchal     46000.0
                          Prakshal Patel      46000.0
Quality Assurance         Hemil Soni          35000.0
Name: avg_salary, dtype: float64

Answer 2

扩展@Chris 所做的事情并添加如果部门名称相同则删除平均工资值的部分。

这是完整的代码：

import pandas as pd
data = {'employees_no':  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
        'employees_name': ['Jugal Sompura', 'Maya Rajput', 'Chaitya Panchal', 'Sweta Rampariya', 'Prakshal Patel', 'Dhruv Panchal', 'Prachi Desai', 'Krunal Gosai', 'Hemil Soni', 'Gopal Pithadia', 'Jatin Shah', 'Raj Patel', 'Shreya Desai'],
        'department_name': ['HR', 'Administrative Assistant', 'Production', 'Accountant', 'Production', 'Engineer', 'Finance', 'Engineer', 'Quality Assurance', 'Engineer', 'Engineer', 'Customer Service', 'CEO'],
        'salary': [130000.0, 65000.0, 45000.0, 65000.0, 47000.0, 40000.0, 90000.0, 45000.0, 35000.0, 45000.0, 30000.0, 40000.0, 250000.0]
        }

df = pd.DataFrame (data)
df['avg_sal'] = df.groupby('department_name')['salary'].transform('mean')
new_df = df.set_index(["department_name", "employees_name"]).sort_index()
new_df.loc[new_df.index.get_level_values(0).duplicated()==True,'avg_sal']=''
print (new_df['avg_sal'])

这将打印如下：

department_name           employees_name 
Accountant                Sweta Rampariya     65000
Administrative Assistant  Maya Rajput         65000
CEO                       Shreya Desai       250000
Customer Service          Raj Patel           40000
Engineer                  Dhruv Panchal       40000
                          Gopal Pithadia           
                          Jatin Shah               
                          Krunal Gosai             
Finance                   Prachi Desai        90000
HR                        Jugal Sompura      130000
Production                Chaitya Panchal     46000
                          Prakshal Patel           
Quality Assurance         Hemil Soni          35000