NumPy nanmean()

import numpy as np

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

# calculate the mean of the array
mean1 = np.nanmean(array1)

print(mean1)

# Output: 3.5

nanmean() 语法

numpy.nanmean() 方法的语法是：

numpy.nanmean(array, axis = None, dtype = None, out = None, keepdims = <no value>, where = <no value>)

nanmean() 参数

numpy.nanmean() 方法接受以下参数：

• array - 包含需要计算平均值的数字的数组（可以是 array_like）
• axis（可选） - 计算平均值的轴或轴（int 或 tuple of int）
• dtype（可选） - 计算平均值时使用的数据类型（datatype）
• out (可选) - 用于存放结果的输出数组（ndarray）
• keepdims (可选) - 指定是否保留原始数组的形状（bool）
• where（可选） - 要包含在平均值中的元素（array of bool）

nanmean() 返回值

numpy.nanmean() 方法返回数组的算术平均值，忽略 **NaNs**。

示例 1：计算 ndArray 的平均值

import numpy as np

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

# find the mean of entire array
mean1 = np.nanmean(array1)

# find the mean across axis 0
mean2 = np.nanmean(array1, 0)

# find the mean across axis 0 and 1
mean3 = np.nanmean(array1, (0, 1))

print('\nMean of the entire array:',mean1)
print('\nMean across axis 0:\n',mean2)
print('\nMean across axis 0 and 1:',mean3)

输出

Mean of the entire array: 3.5714285714285716

Mean across axis 0:
[[2. 3.]
 [4. 7.]]

Mean across axis 0 and 1: [3.         4.33333333]

示例 2：指定 ndArray 平均值的数据类型

dtype 参数可用于控制输出数组的数据类型。

import numpy as np

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

# by default int is converted to float
result1 = np.nanmean(array1)

# get float32 mean
result2 = np.nanmean(array1, dtype = np.float32)

print('Float64 mean:', result1, 'with type', result1.dtype)
print('Float32 mean:', result2, 'with type', result2.dtype)

输出

Float64 mean: 3.8 with type float64
Float32 mean: 3.8 with type float32

注意：使用较低精度的 dtype 可能导致精度损失。

示例 3：使用可选的 keepdims 参数

如果将 keepdims 设置为 True，则结果平均数组的维度与原始数组的维度相同。

import numpy as np

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

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

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

print('Dimensions in original array:', array1.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 5.  3. ] with dimensions 1
With keepdims: [[2.5 5.  3. ]] with dimensions 2

示例 4：使用可选的 where 参数

可选参数 where 指定要包含在平均值计算中的元素。

import numpy as np

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

# take mean of entire array
result1 = np.nanmean(array1)

# mean of only even elements
result2 = np.nanmean(array1, where = (array1 % 2 == 0))

# mean of  numbers greater than 3
result3 = np.nanmean(array1, where = (array1 > 3))

print('Mean of entire array:', result1)
print('Mean of only even elements:', result2)
print('Mean of  numbers greater than 3:', result3)

输出

Mean of entire array: 3.5
Mean of only even elements: 4.0
Mean of  numbers greater than 3: 5.0

示例 5：使用可选的 out 参数

out 参数允许指定一个输出数组，结果将存储在该数组中。

import numpy as np

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

# create an output array
output = np.zeros(4)

# compute mean and store the result in the output array
np.mean(array1, out = output, axis = 0)

print('Mean:', output)

输出

Mean: [2.5 3.5 4.5 nan]

注意：仅当所有元素都是 nan 时，nanmean() 才返回 nan 作为输出。