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]

结果是列表'a'和列表'wts'的移动窗口和积。

例如，结果8.2是通过代码

获得的

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

通过将8.2附加到列表'a'获得的a的新窗口获得结果。

新列表a应该是附加上述结果的结果。

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

现在计算结果列表的下一个值，即结果[1] = 7.76，它应该是

的产品

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]

'wts'列表是固定的，只有列表'a'将是移动窗口，新结果被附加到a.Any python脚本以实现这一点在这将有很大的帮助。

基于Below，我将以下函数应用于数据帧。请问关于如何将此函数应用于基于多个组的数据帧（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，我有一个包含5列的数据帧（即A列，B列，C列，权重，当前）。我使用以下代码

应用上述功能

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

我收到以下错误：TypeError：unhashable type：'list'。任何帮助都会有很大的帮助。

Answer 1

在这种情况下，您需要使用fixed-sized queue，如下所示：

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]

移动窗口和产品计算Python

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