R语言 cumprod
时间: 2023-07-10 17:37:18 浏览: 88
在R语言中,cumprod函数用于计算向量或矩阵的累积乘积。具体来说,它返回一个与输入相同大小的向量或矩阵,其中每个元素都是从输入的第一个元素开始到该元素的乘积。以下是一个示例:
```
x <- c(1, 2, 3, 4)
cumprod(x) # 返回结果为 [1, 2, 6, 24]
```
在上面的示例中,cumprod函数计算了1、1*2、1*2*3和1*2*3*4,分别得到了1、2、6和24。
相关问题
cumprod 源码
对于cumprod函数的源码,根据引用中提到的发现,cumproduct函数和cumprod函数是一致的。因此,我们可以通过查看cumproduct函数的源码来了解cumprod函数的实现。遗憾的是,没有提供具体的源码信息。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [【numpy】np.cumprod的使用](https://blog.csdn.net/mimiduck/article/details/122772920)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
[ .reference_list ]
pandas cumprod
pandas.cumprod() is a function in the pandas library that returns the cumulative product of the elements in a pandas Series or DataFrame. It computes the product of all the elements from the beginning of the Series or DataFrame up to the current position.
Syntax:
```
DataFrame.cumprod(axis=None, skipna=True, *args, **kwargs)
```
Parameters:
- axis: 0 or 'index' for rows and 1 or 'columns' for columns. By default, it is None and applies cumprod() on the entire DataFrame.
- skipna: If True, it skips NaN values while performing the cumprod() operation, otherwise, it returns NaN.
Returns:
- A new pandas Series or DataFrame with the cumulative product of the elements.
Example:
```
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4]})
print(df.cumprod())
```
Output:
```
A
0 1
1 2
2 6
3 24
```
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![ppt](https://img-home.csdnimg.cn/images/20210720083527.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)