移动窗口和积计算的Python

Question

我有以下列表：

a= [1,2,3,4,5,6,7,8,9,10,11,12] 
wts= [0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.10,0.10,0.30] 

期望的结果是

result = [8.2,7.76,7.848,7.9504,8.179253333,8.420282667,8.628383467,8.790601973,8.894139057,8.930025594,8.891166196,8.770706404] 

结果列表“a”和列表“WTS的移动窗口和积”。

例如结果8.2由代码

sum(map(lambda xi, yi: xi * yi,x,wt)) 

结果由通过追加8.2到列表“A”而获得的一个新的窗口得到的获得。

新列表a应该是从上面的结果中追加结果的地方。

a = [1,2,3,4,5,6,7,8,9,10,11,12,8.2] 

现在计算的结果列表即结果的下一个值[1] = 7.76，它应该是

a = [2,3,4,5,6,7,8,9,10,11,12,8.2] and 
wts = [0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.10,0.10,0.30] 

的SUMPRODUCT的'WTS名单是固定的，只列表“一”将会移动窗口，新的结果会被附加到a.Anython的任何脚本来实现这一点将会很有帮助。

基于下面，我将下面的函数应用于数据框。您能否介绍一下我如何将这个函数应用到基于多个组的数据框上（基于Groupby）。

def wma(Curr,wts): 
    Curr.values.tolist() 
    wts.values.tolist() 
    len_list = len(Curr) 
    # Create a fixed sized queue 
    q = deque(maxlen=len_list) 
    # Add list a to q 
    q.extend(Curr) 
    i = 0 
    result = [] 
    while i < len(a): 
     val = sum([x*y for x, y in zip(q, wts)]) 
     q.append(val) 
     result.append(float(round(val, 2))) 
     i += 1 
    return result 

例如，我有一个数据框有5列，即（列A，列B，列C，权重，当前）。类型错误：unhashable类型：“名单”我用下面的代码

s1 = s1.groupby(['Column A', 'Column B', 'Column C']).apply(wma(df['Current'],df['Weights'])) 

我收到以下错误应用上述功能。任何帮助都会有很大的帮助。

Answer 1

在这种情况下，你需要使用fixed-sized queue，如下图所示：

尝试：

from collections import deque 

a= [1,2,3,4,5,6,7,8,9,10,11,12] 
wts= [0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.055555556,0.10,0.10,0.30] 
len_a = len(a) 

# Create a fixed sized queue 
q = deque(maxlen=len_a) 

# Add list a to q 
q.extend(a) 

# See the q 
print('q: ', q) 

i = 0 
result = [] 

while i < len(a): 
    val = sum([x*y for x, y in zip(q, wts)]) 
    q.append(val) 
    result.append(float(round(val, 2))) 
    i += 1 

print('result: ', result) 

# Output 
q: deque([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], maxlen=12) 
result: [8.2, 7.76, 7.85, 7.95, 8.18, 8.42, 8.63, 8.79, 8.89, 8.93, 8.89, 8.77]