数据框中的累积或滚动产品

本文介绍了数据框中的累积或滚动产品的处理方法，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小编来一起学习吧！ 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I found this post from earlier but it seems to be using rolling_apply which is no longer active?: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 cumprod

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