编辑：

我之前提供的方法是一种简单有效的方法来计算NumPy数组列表的加权平均值。但是，如果您喜欢更简洁的方式来实现相同的结果，则可以使用NumPy的average函数和weights参数。下面是如何做到这一点：

import numpy as np

l1 = [1, 3]
l2 = [np.array([3, 3]), np.array([1, 1])]

# Convert the list of weights to a NumPy array
weights = np.array(l1)

# Convert the list of arrays to a NumPy array
arrays = np.array(l2)

# Calculate the weighted average
weighted_avg = np.average(arrays, axis=0, weights=weights)

print(weighted_avg)

字符串
输出量：

array([1.5, 1.5])

型
带有weights参数的np.average函数允许您直接计算加权平均值，而无需分别显式计算加权和和总权重。它提供了一种简洁有效的方法来实现与前面的方法相同的结果。
要计算NumPy数组列表的加权平均值，您需要数组及其相应的权重。以下是您的操作方法：

import numpy as np

def weighted_average(arr_list, weights):
    # Check if the input arrays and weights have the same length
    if len(arr_list) != len(weights):
        raise ValueError("The length of arrays and weights must be the same.")

    # Convert the list of arrays into a NumPy array
    arr_list = np.array(arr_list)

    # Calculate the weighted sum
    weighted_sum = np.sum(arr_list * weights[:, np.newaxis], axis=0)

    # Calculate the total weight
    total_weight = np.sum(weights)

    # Calculate the weighted average
    weighted_avg = weighted_sum / total_weight

    return weighted_avg

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使用示例：

# List of NumPy arrays
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
array3 = np.array([7, 8, 9])

# Corresponding weights
weights = np.array([0.3, 0.5, 0.2])

# Calculate the weighted average
result = weighted_average([array1, array2, array3], weights)
print(result)  # Output: [3.7 4.7 5.3]

型
在该示例中，具有权重[0.3，0.5，0.2]的阵列[1，2，3]、[4，5，6]和[7，8，9]的加权平均值是[3.7，4.7，5.3]。