Question

我有一个类似于以下内容的数据框

df = pd.DataFrame({'id':[1,2,3,4,5,6,7,8,9,10],'value':[1,2.5,1.1,1.4,1.5,1,1.5,3,1,1.6]})
df['value_at_1'] = np.where(df['value'] == 1,1,0)
df
>>>

id   value   value_at_1
1      1          1
2     2.5         0 
3     1.1         0
4     1.4         0
5     1.5         0
6      1          1
7     1.5         0
8      3          0
9      1          1
10    1.6         0

我想创建一个枚举“value_at_1”变量的变量，每次递增直到它达到1，然后重新启动。结果如下：

df = pd.DataFrame({'id':[1,2,3,4,5,6,7,8,9,10],
               'value':[1,2.5,1.1,1.4,1.5,1,1.5,3,1,1.6],
               'value_at_1':[1,0,0,0,0,1,0,0,1,0],
               'count_since_1':[0,1,2,3,4,0,1,2,0,1]}).set_index(['value_at_1'])
>>>

id   value   value_at_1   count_since_1
1      1          1            0 
2     2.5         0            1
3     1.1         0            2
4     1.4         0            3
5     1.5         0            4
6      1          1            0
7     1.5         0            1
8      3          0            2
9      1          1            0 
10    1.6         0            1

有人能帮助我以这种方式操纵数据吗？谢谢！

Answer 1

在groupby中使用cumcount获取df.assign( count_since_1=df.value_at_1.groupby(df.value_at_1.cumsum()).cumcount()) id value value_at_1 count_since_1 0 1 1.0 1 0 1 2 2.5 0 1 2 3 1.1 0 2 3 4 1.4 0 3 4 5 1.5 0 4 5 6 1.0 1 0 6 7 1.5 0 1 7 8 3.0 0 2 8 9 1.0 1 0 9 10 1.6 0 1

ReactDOM.render

Answer 2

只想提供一种新方式

import pandas as pd 
import numpy as np 
import functools

idx=df.index[df['value_at_1'].eq(1)].values.tolist()+[len(df)]
idx=list(np.diff(idx))
df['count_since_1']=functools.reduce(lambda x,y: x+y,[list(range(y)) for y in idx])
df
Out[945]: 
   id  value  value_at_1  count_since_1
0   1    1.0           1              0
1   2    2.5           0              1
2   3    1.1           0              2
3   4    1.4           0              3
4   5    1.5           0              4
5   6    1.0           1              0
6   7    1.5           0              1
7   8    3.0           0              2
8   9    1.0           1              0
9  10    1.6           0              1

枚举一个值，直到达到pandas中的特定值

2 个答案: