数据框根据其他列的值连续性在该列中添加元素 [英] Dataframe add element from a column based on values contiguity from another columns

问题描述

我有这样的df:

a=[1,2,10,11,15,16,17,18,30]
b=[5,6,7,8,9,1,2,3,4]
df=pd.DataFrame(list(zip(a,b)),columns=['s','i'])

使用a，我需要添加b的元素.

Using a I need to add elements of b.

我想要的结果:

(1-2)= 5 + 6 = 11

(1-2)=5+6=11

(10-11)= 7 + 8 = 15

(10-11)=7+8=15

(15-18)= 9 + 1 + 2 + 3 = 15

(15-18)=9+1+2+3=15

(30)= 4

我的想法是创建一个连续的值列表，取差值(+1)并用其计算对应的b个元素的总和.

My idea was to create a list of values that are continuous, take the difference(+1) and use it to calculate the sum of the corresponding b elements.

#find continuous integer 
def r (nums):
    nums= list(df['s'])
    gaps = [[s, e] for s, e in zip(nums, nums[1:]) if s+1 < e]
    edges = iter(nums[:1] + sum(gaps, []) + nums[-1:])
    return (list(zip(edges, edges)))

#difference 
a = r(df)
print (a)
for i in range (len(a)):
    diff = np.diff(a[i])+1

我正在尝试使用diff作为计数器来添加b的值，但是显然任何一次加法都从第一个值开始.有任何简单的方法可以在不更改b的情况下添加此数字?

I am trying to use diff as a counter to add the value of b but obviously any single time the addition starts from the first value. There is any simple way to add this number without changing b?

推荐答案

使用 groupby + diff

df['i'].groupby(df['s'].diff().ne(1).cumsum()).sum()

---

1    11
2    15
3    15
4     4
Name: i, dtype: int64

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