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]