Python 中的 numpy.std()
numpy.std(arr，axis = None) : 计算给定数据(数组元素)沿指定轴(如果有)的标准偏差..
标准偏差(SD) 作为给定数据集中数据分布的扩展来测量。
举例:
x = 1 1 1 1 1
Standard Deviation = 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.
Step 4 : Standard Deviation = sqrt(Variance) = sqrt(8.9) = 2.983..
参数: arr:【array _ like】输入数组。 轴:【int 或 int 的元组】轴，我们要沿着该轴计算标准差。否则，它将考虑将 arr 展平(在所有轴上工作)。轴= 0 表示沿列的标清，轴= 1 表示沿行的标清。 出:【n 数组，可选】我们想要放置结果的不同数组。数组必须具有与预期输出相同的维度。 数据类型:【数据类型，可选】计算 SD 时我们想要的类型。
结果:数组(如果轴为无，则为标量值)或具有沿指定轴的标准偏差值的数组的标准偏差。
代码#1:
# Python Program illustrating
# numpy.std() method
import numpy as np
# 1D array
arr = [20, 2, 7, 1, 34]
print("arr : ", arr)
print("std of arr : ", np.std(arr))
print ("\nMore precision with float32")
print("std of arr : ", np.std(arr, dtype = np.float32))
print ("\nMore accuracy with float64")
print("std of arr : ", np.std(arr, dtype = np.float64))
输出:
arr : [20, 2, 7, 1, 34]
std of arr : 12.576167937809991
More precision with float32
std of arr : 12.576168
More accuracy with float64
std of arr : 12.576167937809991
代码#2:
# Python Program illustrating
# numpy.std() method
import numpy as np
# 2D array
arr = [[2, 2, 2, 2, 2],
[15, 6, 27, 8, 2],
[23, 2, 54, 1, 2, ],
[11, 44, 34, 7, 2]]
# std of the flattened array
print("\nstd of arr, axis = None : ", np.std(arr))
# std along the axis = 0
print("\nstd of arr, axis = 0 : ", np.std(arr, axis = 0))
# std along the axis = 1
print("\nstd of arr, axis = 1 : ", np.std(arr, axis = 1))
输出:
std of arr, axis = None : 15.3668474320532
std of arr, axis = 0 : [ 7.56224173 17.68473918 18.59267329 3.04138127 0\. ]
std of arr, axis = 1 : [ 0\. 8.7772433 20.53874388 16.40243884]
