使用rolling_apply对pandas的Python自定义函数 [英] Python custom function using rolling_apply for pandas

问题描述

我想使用pandas.rolling_apply函数在滚动窗口的基础上应用自己的自定义函数.

I would like to use the pandas.rolling_apply function to apply my own custom function on a rolling window basis.

但是我的函数需要两个参数，并且还具有两个输出.这可能吗?

but my function requires two arguments, and also has two outputs. Is this possible?

下面是一个最小的可重现示例...

Below is a minimum reproducible example...

import pandas as pd
import numpy as np
import random
tmp  = pd.DataFrame(np.random.randn(2000,2)/10000, 
                    index=pd.date_range('2001-01-01',periods=2000),
                    columns=['A','B'])

def gm(df,p):
    v =(((df+1).cumprod())-1)*p
    return v.iloc[-1]

# an example output when subsetting for just 2001
gm(tmp['2001'],5)


# the aim is to do it on a rolling basis over a 50 day window
# whilst also getting both outputs and also allows me to add in the parameter p=5
# or any other number I want p to be... 
pd.rolling_apply(tmp,50,gm)

这会导致错误...因为gm需要两个参数...

which leads to an error...since gm takes two arguments...

任何帮助将不胜感激...

any help would be greatly appreciated...

编辑

按照Jeff的说明，我已经取得了进步，但仍在为两个或多个列输出苦苦挣扎，因此，如果我改为创建一个新函数(如下)，该函数仅返回两个随机数(与上一个计算无关)而不是最后一个v行，出现错误TypeError: only length-1 arrays can be converted to Python scalars.如果

Following Jeff's comment I have progressed, but am still struggling with two or more column outputs, so if instead i make a new function (below) which just returns two random numbers (unconnected to the previous calculation) instead rather than the last rows of v, I get an error of TypeError: only length-1 arrays can be converted to Python scalars. This function works if

def gm2(df,p):
    df = pd.DataFrame(df)
    v =(((df+1).cumprod())-1)*p
    return np.random.rand(2)

pd.rolling_apply(tmp,50,lambda x: gm2(x,5)).tail(20)

如果将2更改为1 ...，此功能将起作用.

This function works if 2 is changed to 1...

推荐答案

rolling_apply将numpy数组传递给应用的函数(当前)，到0.14时它应该传递一帧.问题是此处

rolling_apply passes numpy arrays to the applied function (at-the-moment), by 0.14 it should pass a frame. The issue is here

因此，重新定义您的函数以在numpy数组上工作. (您当然可以在这里构造一个DataFrame，但是您的索引/列名称将不同).

So redefine your function to work on a numpy array. (You can of course construct a DataFrame inside here, but your index/column names won't be the same).

In [9]: def gm(df,p):
   ...:     v = ((np.cumprod(df+1))-1)*p
   ...:     return v[-1]
   ...: 

如果您想在自定义函数中使用更多的熊猫函数，请执行此操作(请注意，调用帧的标记是未通过ATM传递的.)

If you wanted to use more of pandas functions in your custom function, do this (note that the indicies of the calling frame are not passed ATM).

def gm(arr,p):
    df = DataFrame(arr)
    v =(((df+1).cumprod())-1)*p
    return v.iloc[-1]

通过lambda传递

In [11]: pd.rolling_apply(tmp,50,lambda x: gm(x,5)).tail(20)
Out[11]: 
                   A         B
2006-06-04  0.004207 -0.002112
2006-06-05  0.003880 -0.001598
2006-06-06  0.003809 -0.002228
2006-06-07  0.002840 -0.003938
2006-06-08  0.002855 -0.004921
2006-06-09  0.002450 -0.004614
2006-06-10  0.001809 -0.004409
2006-06-11  0.001445 -0.005959
2006-06-12  0.001297 -0.006831
2006-06-13  0.000869 -0.007878
2006-06-14  0.000359 -0.008102
2006-06-15 -0.000885 -0.007996
2006-06-16 -0.001838 -0.008230
2006-06-17 -0.003036 -0.008658
2006-06-18 -0.002280 -0.008552
2006-06-19 -0.001398 -0.007831
2006-06-20 -0.000648 -0.007828
2006-06-21 -0.000799 -0.007616
2006-06-22 -0.001096 -0.006740
2006-06-23 -0.001160 -0.006004

[20 rows x 2 columns]

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