# -*- coding: utf-8 -*-

"""

Created on Mon Mar 12 15:37:14 2018

@author: Administrator

"""

import numpy as np

from numpy.matlib import randn

arr = np.empty((8, 4)) #二维数组

#range(8)即 [0, 1, 2, 3, 4, 5, 6, 7]

for i in range(8):

arr[i] = i #第i行的数字都为i;

print(arr)

print(arr[[4, 3, 0, 6]]) #[[ 4. 4. 4. 4.] [ 3. 3. 3. 3.] [ 0. 0. 0. 0.] [ 6. 6. 6. 6.]]

arr = np.arange(32).reshape((8, 4))

print("np.arange(32).reshape((8, 4)):")

print(arr)

print(arr[[1, 5, 7, 2], [0, 3, 1, 2]])#[ 4 23 29 10]

print(" 输出矩阵转置: ")

arr = np.arange(15).reshape((3, 5))

print(arr.T)

print("输出np.arange(10)：")

arr = np.arange(10)

print(arr)

print("输出np.sqrt(arr)：")

print(np.sqrt(arr)) #仍是一维数组，求数组中的每个元素求平方根

print("输出np.exp(arr))：")

print(np.exp(arr)) #仍是一维数组，求以e为底，数组中的每个元素作为指数的值

print("randn(8):")

x = randn(8) #生成8个随机数

print(x)

print("randn(8):")

y = randn(8)

print(y)

print(np.maximum(x, y)) #仍是一维数组，每个元素为数组x和y对应元素最大的那个；

print("-------------------------------------")

xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5])

yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5])

cond = np.array([True, False, True, True, False])

result = np.where(cond, xarr, yarr)

print(result)#输出：[1.1 2.2 1.3 1.4 2.5]

print("-------------------------------------")

arr = randn(4, 4)

print(arr)

print(np.where(arr > 0, 2, -2))

print("-------------------------------------")

arr = np.random.randn(5, 4) # normally-distributed data

print(arr)

print(arr.mean())

print(np.mean(arr))

print(arr.sum())

print(arr.mean(axis=1))