我可以创建一个参考索引列,每次达到 cumsum 阈值时从 0 重置吗
Can I create a reference index column that resets from 0 every time a cumsum threshold is reached
我正在尝试添加一个累积总和列和一个新的索引列n_index。使用现有答案,我添加了一个 cumsum 列,但我拥有的参考索引列不是我需要的。
df = pd.DataFrame({'amount':[4, 3, 7, 8, 2, 1, 5, 3, 5, 8]})
ls = []
n_index = []
cumsum = 0
last_reset = 0
threshold = 16
for i, row in df.iterrows():
if cumsum + row.amount <= threshold:
cumsum = cumsum + row.amount
n_index.append(i)
else:
last_reset = cumsum
cumsum = row.amount
n_index.append(0)
ls.append(cumsum)
df['cumsum'] = ls
df['n_index'] = n_index
结果是:
df
amount cumsum n_index
0 4 4 0
1 3 7 1
2 7 14 2
3 8 8 0
4 2 10 4
5 1 11 5
6 5 16 6
7 3 3 0
8 5 8 8
9 8 16 9
我希望数据帧 n_index 每次超过阈值时都从零 (0) 开始,如下所示:
amount cumsum n_index
0 4 4 0
1 3 7 1
2 7 14 2
3 8 8 0
4 2 10 1
5 1 11 2
6 5 16 3
7 3 3 0
8 5 8 1
9 8 16 2
请帮忙,谢谢。
希望您得到了预期的结果,并消除错误。
df = pd.DataFrame({'amount':[4, 3, 7, 8, 2, 1, 5, 3, 5, 8]})
ls = []
n_index = []
cumsum = 0
last_reset = 0
threshold = 16
assign_indx=0
for i, row in df.iterrows():
if cumsum + row.amount <= threshold:
cumsum = cumsum + row.amount
n_index.append(assign_indx)
assign_indx+=1
else:
last_reset = cumsum
cumsum = row.amount
n_index.append(0)
assign_indx=1
ls.append(cumsum)
df['cumsum'] = ls
df['n_index'] = n_index
#Output:
amount cumsum n_index
0 4 4 0
1 3 7 1
2 7 14 2
3 8 8 0
4 2 10 1
5 1 11 2
6 5 16 3
7 3 3 0
8 5 8 1
9 8 16 2
我正在尝试添加一个累积总和列和一个新的索引列n_index。使用现有答案,我添加了一个 cumsum 列,但我拥有的参考索引列不是我需要的。
df = pd.DataFrame({'amount':[4, 3, 7, 8, 2, 1, 5, 3, 5, 8]})
ls = []
n_index = []
cumsum = 0
last_reset = 0
threshold = 16
for i, row in df.iterrows():
if cumsum + row.amount <= threshold:
cumsum = cumsum + row.amount
n_index.append(i)
else:
last_reset = cumsum
cumsum = row.amount
n_index.append(0)
ls.append(cumsum)
df['cumsum'] = ls
df['n_index'] = n_index
结果是:
df
amount cumsum n_index
0 4 4 0
1 3 7 1
2 7 14 2
3 8 8 0
4 2 10 4
5 1 11 5
6 5 16 6
7 3 3 0
8 5 8 8
9 8 16 9
我希望数据帧 n_index 每次超过阈值时都从零 (0) 开始,如下所示:
amount cumsum n_index
0 4 4 0
1 3 7 1
2 7 14 2
3 8 8 0
4 2 10 1
5 1 11 2
6 5 16 3
7 3 3 0
8 5 8 1
9 8 16 2
请帮忙,谢谢。
希望您得到了预期的结果,并消除错误。
df = pd.DataFrame({'amount':[4, 3, 7, 8, 2, 1, 5, 3, 5, 8]})
ls = []
n_index = []
cumsum = 0
last_reset = 0
threshold = 16
assign_indx=0
for i, row in df.iterrows():
if cumsum + row.amount <= threshold:
cumsum = cumsum + row.amount
n_index.append(assign_indx)
assign_indx+=1
else:
last_reset = cumsum
cumsum = row.amount
n_index.append(0)
assign_indx=1
ls.append(cumsum)
df['cumsum'] = ls
df['n_index'] = n_index
#Output:
amount cumsum n_index
0 4 4 0
1 3 7 1
2 7 14 2
3 8 8 0
4 2 10 1
5 1 11 2
6 5 16 3
7 3 3 0
8 5 8 1
9 8 16 2