NumPy average()

import numpy as np

# create an array
array1 = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# calculate the average of the array
avg = np.average(array1)

print(avg)

# Output: 3.5

average() 语法

numpy.average() 方法的语法是

numpy.average(array, axis = None, weights = None, returned = False, keepdims = <no value>)

average() 参数

numpy.average() 方法接受以下参数

• array - 包含所需平均值的数字的数组（可以是 array_like）
• axis (可选) - 计算平均值的轴（可以是 int 或 tuple of int）
• weights (可选) - 与 array 中每个值相关联的权重（array_like）
• returned (可选) - 如果为 True，则返回元组 (average, sum_of_weights)，否则仅返回平均值。
• keepdims (可选) - 指定是否保留原始数组的形状（bool）

average() 返回值

numpy.average() 方法返回数组的加权平均值。

示例 1：查找 ndArray 的平均值

import numpy as np

# create an array
array1 = np.array([[[0, 1], 
                    [2, 3]], 
                    [[4, 5], 
                    [6, 7]]])

# find the average of entire array
average1 = np.average(array1)

# find the average across axis 0
average2 = np.average(array1, 0)

# find the average across axis 0 and 1
average3 = np.average(array1, (0, 1))

print('\naverage of the entire array:', average1)
print('\naverage across axis 0:\n', average2)
print('\naverage across axis 0 and 1:', average3)

输出

average of the entire array: 3.5

average across axis 0:
[[2. 3.]
 [4. 5.]]

average across axis 0 and 1: [3. 4.]

示例 2：为 ndArray 的值指定权重

weights 参数可用于控制输入数组中每个值的重要性。

import numpy as np

array1= np.array([[1, 2, 3],
                [4, 5, 6]])

# by default all values have the same weight(1)
result1 = np.average(array1)

# assign variable weights
result2 = np.average(array1, weights = np.arange(0,6,1).reshape(2, 3))

# assign 0 weight to first column
# to get average of 2nd and 3rd column
result3 = np.average(array1, weights = [0, 1, 1], axis = 1)

print('No weights given:', result1)
print('Variable weights:', result2)
print('Average of 2nd and 3rd columns:', result3)

输出

No weights given: 3.5
Variable weights: 4.666666666666667
Average of 2nd and 3rd columns: [2.5 5.5]

当 weights 未分配时，numpy.average() 的作用与 numpy.mean() 相同。

例如，在 result1 中，

average = (1 + 2 + 3 + 4 + 5 + 6) / 6 = 21 / 6 = 3.5

当传递 weights 时，将计算加权平均值。

例如，在 result2 中，

weighted average
= sum(values * weights) / sum(weights)
= (1  * 0 + 2 * 1 + 3  * 2+ 4 * 3 + 5 * 4 + 6 * 5) / (15)
= 4.666666666667

示例 3：使用可选的 keepdims 参数

如果 keepdims 设置为 True，则结果平均数组的维度数与原始数组相同。

import numpy as np

array1= np.array([[1, 2, 3],
                [4, 5, 6]])

# keepdims defaults to False
result1 = np.average(array1, axis = 0)

# pass keepdims as True
result2 = np.average(array1, axis = 0, keepdims = True)

print('Dimensions in original array:',arr.ndim)
print('Without keepdims:', result1, 'with dimensions', result1.ndim)
print('With keepdims:', result2, 'with dimensions', result2.ndim)

输出

Dimensions in original array: 2
Without keepdims: [2.5 3.5 4.5] with dimensions 1
With keepdims: [[2.5 3.5 4.5]] with dimensions 2

示例 4：使用可选的 returned 参数

returned 参数允许指定返回值是仅包含计算出的平均值，还是包含一个元组 (average, sum_of_weights)。

import numpy as np

array1= np.array([[1, 2, 3],
                [4, 5, 6]])

# by default, returned = False
# only average is returned
avg = np.average(array1)

# return the average and sum of weights
avg2,sumWeights = np.average(array1, returned = True)

print('Average:', avg)
print('Average:', avg2, 'with sum of weights:', sumWeights )

输出

Average: 3.5
Average: 3.5 with sum of weights: 6.0

import numpy as np

array1= np.array([[1, 2, 3],
                [4, 5, 6]])
weights = np.zeros(6).reshape(2, 3)

# compute average, by default returned = False, only average returned
avg = np.average(array1, weights = weights)

print('Average:', avg)

ZeroDivisionError: Weights sum to zero, can't be normalized

import numpy as np

array1= np.array([1, 2, 3, 4, 5, 6])
weights = np.ones(5)

# the length of array is 6 whereas the length of weights is 5
avg = np.average(array1, weights = weights)

print('Average:', avg)

TypeError: Axis must be specified when shapes of a and weights differ.

import numpy as np

array1= np.array([1, 2, 3, 4, 5, 6])
weights = np.ones(5)

# the length of array is 6 whereas the length of weights is 5
avg = np.average(array1, weights = weights, axis = 0)

print('Average:', avg)

ValueError: Length of weights not compatible with specified axis.