CODEWITHSUNDEEP
pythonpandasloopscumsum
anonymous13
anonymous13

Reputation: 621

Python: Sum until 0 is reached and then restart

Looking for a solution in Python as discussed here R: Sum until 0 is reached and then restart

I have a similar dataframe and I want cummulative sum of values in the APpliance column when the values in Run column is 0 and reset the count to zero when the value changes from 0 to 1 in Run column

        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

I have tried below code. but it just adds up previous row value and does not do a cummulative sum

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

My desired output is as follows in the value column

        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

could somebody help me with this

Upvotes: 1

Views: 602

Answers (2)

Mad Physicist
Mad Physicist

Reputation: 114578

You can generate a label to group on like this:

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

Now you can group and sum each group:

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

Upvotes: 1

BENY
BENY

Reputation: 323386

We can doing the filter for Appliance , and calculated the groupby key with cumsum , notice when the Appliance is 0 the cumsum will not increased and it had been split by the number 1

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

Upvotes: 1

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