本文介绍了如何根据根据条件重置的累积总和进行分组的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着跟版网的小编来一起学习吧!
问题描述
我有一个 pandas df,其字数与文章相对应.我希望能够添加另一列 MERGED
,该列基于具有最小累积总和min_words"的文章组.
I have a pandas df with word counts corresponding to articles. I want to be able to be able to add another column MERGED
that is based on groups of articles that have a minimum cumulative sum of 'min_words'.
df = pd.DataFrame([[ 0, 6],
[ 1, 10],
[ 3, 5],
[ 4, 7],
[ 5, 26],
[ 6, 7],
[ 9, 4],
[ 10, 133],
[ 11, 42],
[ 12, 1]], columns=['ARTICLE', 'WORD_COUNT'])
df
Out[15]:
ARTICLE WORD_COUNT
0 0 6
1 1 10
2 3 5
3 4 7
4 5 26
5 6 7
6 9 4
7 10 133
8 11 42
9 12 1
那么如果 min_words = 20
这是所需的输出:
So then if min_words = 20
this is the desired output:
df
Out[17]:
ARTICLE WORD_COUNT MERGED
0 0 6 0
1 1 10 0
2 3 5 0
3 4 7 1
4 5 26 1
5 6 7 2
6 9 4 2
7 10 133 2
8 11 42 3
9 12 1 4
如上所示,最终文章可能不满足 min_words 条件,这没关系.
As seen above, it is possible that the final article(s) won't satisfy the min_words condition, and that's ok.
推荐答案
只能做self def功能
We can only do self def function
def dymcumsum(v, limit):
idx = []
sums = 0
for i in range(len(v)):
sums += v[i]
if sums >= limit:
idx.append(i)
sums = 0
return(idx)
df['New']=np.nan
df.loc[dymcumsum(df.WORD_COUNT,20),'New']=1
df.New=df.New.iloc[::-1].eq(1).cumsum()[::-1].factorize()[0]+1
df
ARTICLE WORD_COUNT New
0 0 6 1
1 1 10 1
2 3 5 1
3 4 7 2
4 5 26 2
5 6 7 3
6 9 4 3
7 10 133 3
8 11 42 4
9 12 1 5
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