Python：求和到0再重新开始

Question

在 Python 中寻找解决方案，如此处所讨论

我有一个类似的数据框，当运行列中的值为 0 时，我想要 APPliance 列中的值的累积总和，并在 [=] 中的值从 0 变为 1 时将计数重置为零26=]列

        Home   Date     Time   Appliance Run   
          2  1/21/2017  1:30:00      100    1 
          2  1/21/2017  1:45:00      207    1 
          2  1/21/2017  2:00:00      310    1       
          2  1/21/2017  2:15:00      450    1       
          2  1/21/2017  2:30:00      804    0      
          2  1/21/2017  2:45:00      556    0     
          2  1/21/2017  3:00:00      844    0    
          2  1/21/2017  3:15:00      396    0    
          2  1/21/2017  3:30:00      392    0    
          2  1/21/2017  3:45:00      1220   0    
          2  1/21/2017  4:00:00      455    1      
          2  1/21/2017  4:15:00      550    1       
          2  1/21/2017  4:30:00      600    1       
          2  1/21/2017  4:45:00      809    1       
          2  1/22/2017  3:30:00      609    1       
          2  1/22/2017  3:45:00      244    0    
          2  1/22/2017  4:00:00     1068    0    
          2  1/22/2017  4:15:00      44     0    
          2  1/22/2017  4:30:00     1240    0    
          2  1/22/2017  4:45:00      40     0    
          2  1/22/2017  5:00:00     1608    0    
          2  1/22/2017  5:15:00       0     1       
          2  1/22/2017  5:30:00       0     1

我试过下面的代码。但它只是将前一行的值加起来，并没有做累计和

newcum = []
lastvalue = 0

for i, row in df.iterrows():
    if df['Run'][i+1] == 0 :
        lastvalue += x['Appliance'][i] 
    else:
        lastvalue = 0
    newcum.append(lastvalue)
df['value'] = newcum

我想要的输出值列如下

        Home   Date     Time   Appliance Run   value
          2  1/21/2017  1:30:00      100    1       0
          2  1/21/2017  1:45:00      207    1       0
          2  1/21/2017  2:00:00      310    1       0
          2  1/21/2017  2:15:00      450    1       0
          2  1/21/2017  2:30:00      804    0     804
          2  1/21/2017  2:45:00      556    0    1360
          2  1/21/2017  3:00:00      844    0    2204
          2  1/21/2017  3:15:00      396    0    2600
          2  1/21/2017  3:30:00      392    0    2992
          2  1/21/2017  3:45:00      1220   0    4212
          2  1/21/2017  4:00:00      455    1       0
          2  1/21/2017  4:15:00      550    1       0
          2  1/21/2017  4:30:00      600    1       0
          2  1/21/2017  4:45:00      809    1       0
          2  1/22/2017  3:30:00      609    1       0
          2  1/22/2017  3:45:00      244    0    244
          2  1/22/2017  4:00:00     1068    0    1312
          2  1/22/2017  4:15:00      44     0    1356
          2  1/22/2017  4:30:00     1240    0    2596
          2  1/22/2017  4:45:00      40     0    2636
          2  1/22/2017  5:00:00     1608    0    4244
          2  1/22/2017  5:15:00       0     1       0
          2  1/22/2017  5:30:00       0     1       0

有人可以帮我解决这个问题吗

Answer 1

我们可以对 Appliance 进行过滤，用 cumsum 计算出 groupby key，注意当 Appliance 为 0 时，cumsum 不会增加，它已经被数字 1 拆分了

df['new'] = df.loc[df['Run'].eq(0)].groupby(df['Run'].cumsum())['Appliance'].cumsum()
df['new'].fillna(0,inplace=True)
df
Out[78]: 
    Home       Date     Time  Appliance  Run     new
0      2  1/21/2017  1:30:00        100    1     0.0
1      2  1/21/2017  1:45:00        207    1     0.0
2      2  1/21/2017  2:00:00        310    1     0.0
3      2  1/21/2017  2:15:00        450    1     0.0
4      2  1/21/2017  2:30:00        804    0   804.0
5      2  1/21/2017  2:45:00        556    0  1360.0
6      2  1/21/2017  3:00:00        844    0  2204.0
7      2  1/21/2017  3:15:00        396    0  2600.0
8      2  1/21/2017  3:30:00        392    0  2992.0
9      2  1/21/2017  3:45:00       1220    0  4212.0
10     2  1/21/2017  4:00:00        455    1     0.0
11     2  1/21/2017  4:15:00        550    1     0.0
12     2  1/21/2017  4:30:00        600    1     0.0
13     2  1/21/2017  4:45:00        809    1     0.0
14     2  1/22/2017  3:30:00        609    1     0.0
15     2  1/22/2017  3:45:00        244    0   244.0
16     2  1/22/2017  4:00:00       1068    0  1312.0
17     2  1/22/2017  4:15:00         44    0  1356.0
18     2  1/22/2017  4:30:00       1240    0  2596.0
19     2  1/22/2017  4:45:00         40    0  2636.0
20     2  1/22/2017  5:00:00       1608    0  4244.0
21     2  1/22/2017  5:15:00          0    1     0.0
22     2  1/22/2017  5:30:00          0    1     0.0

Answer 2

您可以像这样生成要分组的标签：

label = (df.Run.diff().fillna(df.Run.iloc[0]) == 1).cumsum()

现在您可以对每组进行分组和求和：

df.value = (df.Appliance * (1 - df.Run)).groupby(label).cumsum()

Python：求和到0再重新开始

Python: Sum until 0 is reached and then restart

python

loops

pandas

cumsum