numpy.nanvar(arr, axis = None)：计算指定数据(数组元素)沿指定轴(如果有)的方差，而忽略NaN值。
例：
x = 1 1 1 1 1
Standard Deviation = 0 . Variance = 0
y = 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Step 1 : Mean of distribution 4 = 7
Step 2 : Summation of (x - x.mean())**2 = 178
Step 3 : Finding Mean = 178 /20 = 8.9
This Result is Variance.
参数：
arr :[数组]输入数组。
axis :我们要沿其计算方差的[int或int元组]轴。否则，它将考虑arr 展平(在所有轴上工作)。 axis = 0表示沿列的方差，而axis = 1表示沿行的方差。
out :[ndarray，可选]我们要在其中放置结果的不同数组。数组必须具有与预期输出相同的尺寸。
dtype :[数据类型，可选]计算方差时需要的类型。
Results :数组的方差(如果没有轴则为标量值)或具有沿指定轴的方差值的数组；而忽略NaN值。
代码1：
# Python Program illustrating
# numpy.nanvar() method
import numpy as np
# 1D array
arr = [20, 2, np.nan, 1, 34]
print("arr : ", arr)
print("\nnanvar of arr : ", np.nanvar(arr))
print("var of arr : ", np.var(arr))
print("\nnanvar of arr : ", np.nanvar(arr, dtype = np.float32))
print("var of arr : ", np.var(arr, dtype = np.float32))
输出：
arr : [20, 2, nan, 1, 34] nanvar of arr : 187.1875 var of arr : nan nanvar of arr : 187.1875 var of arr : nan
代码2：
# Python Program illustrating
# numpy.nanvar() method
import numpy as np
# 2D array
arr = [[2, 2, 2, 2, 2],
[15, 6, np.nan, 8, 2],
[23, 2, 54, 1, 2, ],
[np.nan, 44, 34, 7, 2]]
# nanvar of the flattened array
print("\nnanvar of arr, axis = None : ", np.nanvar(arr))
print("\nvar of arr, axis = None : ", np.var(arr))
# nanvar along the axis = 0
print("\nnanvar of arr, axis = 0 : \n", np.nanvar(arr, axis = 0))
print("\nvar of arr, axis = 0 : ", np.var(arr, axis = 0))
# nanvar along the axis = 1
print("\nnanvar of arr, axis = 1 : ", np.nanvar(arr, axis = 1)) 输出：
nanvar of arr, axis = None : 249.88888888888889 var of arr, axis = None : nan nanvar of arr, axis = 0 : [ 74.88888889 312.75 458.66666667 9.25 0. ] var of arr, axis = 0 : [ nan 312.75 nan 9.25 0. ] nanvar of arr, axis = 1 : [ 0. 22.1875 421.84 313.1875]
相关用法
注：本文由纯净天空筛选整理自Mohit Gupta_OMG 大神的英文原创作品 numpy.nanvar() in Python。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。