数据框中的累积或滚动产品 [英] Cumulative or Rolling Product in a Dataframe

问题描述

我有一个带有一列的数据框，我只是想添加另一列，该列采用原始列的滚动乘积.我已经搜索了一段时间，但这似乎是一种基本功能-不确定是否丢失了某些内容.想要将B列作为输出.

Hi I have a dataframe with a column and i'd simply like to add another column that takes the rolling product of the original column. I've been googling around for a while but this seems like such a basic functionality - not sure if I'm missing something. Id to like to get Column B as an output.

A   B
1   1
2   2
3   6
4   24
5   120
6   720
7   5040

我本质上是在寻找类似的东西(如果存在)

Im essentially looking for something like this if it existed:

data ['B'] =数据['A'].rolling(window = 1).product()

data['B'] = data['A'].rolling(window=1).product()

我从较早时发现了这篇文章，但似乎使用的是rolling_apply，它不再活动了?:

I found this post from earlier but it seems to be using rolling_apply which is no longer active?:

如何在Pandas DataFrame上计算滚动累积产品 a>

我曾尝试在此处使用类似的解决方案，但似乎无法正常工作.

i've tried using a similar solution here like this but it doesn't seem to be working.

dftest= pd.DataFrame([1,2,3,4,5,6,7],columns=['A'])
dftest['cum']=dftest['A'].rolling(1).apply(lambda x:x.prod())

输出:

   A  cumprod
0  1  1.0
1  2  2.0
2  3  3.0
3  4  4.0
4  5  5.0
5  6  6.0
6  7  7.0

推荐答案

好像您想要

Seems like you want cumprod

df = pd.DataFrame({'v':[1,2,3,4,5,6]})
df['prod'] = df.v.cumprod()

    v   prod
0   1   1
1   2   2
2   3   6
3   4   24
4   5   120
5   6   720

---

也可以

---

May also do

df.v.expanding().agg(lambda a:a.prod())

0      1.0
1      2.0
2      6.0
3     24.0
4    120.0
5    720.0

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