当列的值相同时，如何获得行总和？

Question

我有一个像这样的数据集：

 time(secs) setup
     40     setup1
     30     setup1       
     20     setup1
     10     setup2
     20     setup2 
     10     setup1
     30     setup1
     30     setup2
     40     setup2
     10     setup3
     20     setup3

我想根据类似的pandas dataframe值来获取setup中的行总和：

  time(secs)  setup
    90        setup1
    30        setup2
    40        setup1
    70        setup2
    30        setup3

但是通过使用groupby()函数：

  df.groupby(['setup']).sum()

我得到的结果是：

  setup      time 

  setup1      130 
  setup2      100
  setup3       30

请帮助解决此问题...

谢谢！！！

Answer 1

分组并通过助手sum和cumsum与shift和Series.ne first比较Series和助手(!=)与助手df1 = (df.groupby(df['setup'].ne(df['setup'].shift()).cumsum(), as_index=False) .agg({'time(secs)':'sum', 'setup':'first'})) print (df1) time(secs) setup 0 90 setup1 1 30 setup2 2 40 setup1 3 70 setup2 4 30 setup3

print (df['setup'].ne(df['setup'].shift()).cumsum())
0     1
1     1
2     1
3     2
4     2
5     3
6     3
7     4
8     4
9     5
10    5
Name: setup, dtype: int32

详细信息：

df['groups'] = df['setup'].ne(df['setup'].shift()).cumsum()
print (df)
    time(secs)   setup  groups
0           40  setup1       1
1           30  setup1       1
2           20  setup1       1
3           10  setup2       2
4           20  setup2       2
5           10  setup1       3
6           30  setup1       3
7           30  setup2       4
8           40  setup2       4
9           10  setup3       5
10          20  setup3       5

df1 = (df.groupby('groups')
         .agg({'time(secs)':'sum', 'setup':'first'})
         .reset_index(drop=True))

---

与新列相似的解决方案：

df1 = (df.groupby(['groups', 'setup'])['time(secs)'].sum()
         .reset_index(level=0, drop=True)
         .reset_index())

print (df1)
   time(secs)   setup
0          90  setup1
1          30  setup2
2          40  setup1
3          70  setup2
4          30  setup3

---

let group = [
    {
        id: 1,
        name: 'Test 1',
        geo: 'Japan',
        car: 'Toyota'
    },
    {
        id: 2,
        name: 'Test 2',
        geo: 'USA',
        car: 'Tesla'
    },
    {
        id: 3,
        name: 'Test 3',
        geo: 'Germany',
        car: 'Audi'
    }
];