文章作者：Tyan
博客：noahsnail.com | CSDN | 简书
本文主要是关于numpy的一些基本运算的用法。
#!/usr/bin/env python
# _*_ coding: utf-8 _*_
import numpy as np
# Test 1
# 定义矩阵
arr = np.array([[1, 2, 3],
[4, 5, 6]])
print arr
# Test 1 Result
[[1 2 3]
[4 5 6]]
# Test 2
# 矩阵的维度
print 'number of dim: ', arr.ndim
# 矩阵的shape,即每一维度上的元素个数
print 'shape: ', arr.shape
# 矩阵的元素总数
print 'size: ', arr.size
# 矩阵的元素类型
print 'dtype: ', arr.dtype
# Test 2 Result
number of dim: 2
shape: (2, 3)
size: 6
dtype: int64
# Test 3
# 定义矩阵及矩阵的元素类型——int32, int64, float32, float64
a = np.array([1, 2, 3], dtype = np.int32)
print a
print a.ndim
print a.shape
print a.size
print a.dtype
# Test 3 Result
[1 2 3]
1
(3,)
3
int32
# Test 4
# 定义零矩阵
z = np.zeros((3, 4), dtype = np.int16)
print z
print z.dtype
# 定义空矩阵
n = np.empty((3, 4))
print n
# Test 4 Result
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]
int16
[[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]]
# Test 5
# 定义向量, 10-20之间, 元素间隔为2, 左闭右开
a = np.arange(10, 20, 2)
print a
# 定义向量并转为矩阵
b = np.arange(12).reshape((3, 4))
print b
# 定义向量, 类型是线性间隔
a = np.linspace(1, 10, 6).reshape((2, 3))
print a
# Test 5 Result
[10 12 14 16 18]
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
[[ 1. 2.8 4.6]
[ 6.4 8.2 10. ]]
# Test 6
# 矩阵的加、减、点乘、平方
a = np.array([10, 20, 30, 40])
b = np.arange(4)
c = a - b
d = a + b
print a, b
print c, d
e = a * b
print e
f = e ** 2
print f
# Test 6 Result
[10 20 30 40] [0 1 2 3]
[10 19 28 37] [10 21 32 43]
[ 0 20 60 120]
[ 0 400 3600 14400]
# Test 7
# 矩阵的三角运算——sin, cos, tan
sin = 10 * np.sin(a)
print sin
# 矩阵的判断
print b < 3
print b == 3
# Test 7 Result
[-5.44021111 9.12945251 -9.88031624 7.4511316 ]
[ True True True False]
[False False False True]
# Test 8
# 矩阵的点乘及乘法
a = [ [1, 1], [0, 1]]
b = np.arange(4).reshape((2, 2))
c = a * b
d = np.dot(a, b)
print c
print d
# Test 8 Result
[[0 1]
[0 3]]
[[2 4]
[2 3]]
# Test 9
# np.random返回随机的浮点数，在半开区间 [0.0, 1.0)
# 定义随机矩阵
a = np.random.random((2, 4))
print a
# Test 9 Result
[[ 0.93213483 0.58102186 0.98259187 0.27387014]
[ 0.43796835 0.98195976 0.29343791 0.94752226]]
# Test 10
# 矩阵的求和, 最小值, 最大值
print np.sum(a)
print np.min(a)
print np.max(a)
# 矩阵某一维度的求和, 最小值, 最大值, 0是列, 1是行
print np.sum(a, axis = 1)
print np.max(a, axis = 1)
print np.min(a, axis = 0)
# Test 10 Result
5.43050697485
0.273870140282
0.982591870104
[ 2.7696187 2.66088828]
[ 0.98259187 0.98195976]
[ 0.43796835 0.58102186 0.29343791 0.27387014]
