numpy基本操作（二）
此博客只有代码以及标题，适合使用时查找，不适合用来学习 代码参考 NumPy中文文档https://www.numpy.org.cn/index.html
import numpy as np
创建一个数组
a = np.array([0,1,2,3,4])
b = np.array([0,1,2,3,4])
c = np.arange(0,5,1)
d = np.linspace(0,2*np.pi,5)
print(a)
print(b)
print(c)
print(d)
print(a[3])
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0. 1.57079633 3.14159265 4.71238898 6.28318531]
3
e = np.array([[11,12,13,14,15],
[16,17,18,19,20],
[21,22,23,24,25],
[26,27,28,29,30],
[31,32,33,34,35]])
print(e);
print(e[1][2]);
[[11 12 13 14 15]
[16 17 18 19 20]
[21 22 23 24 25]
[26 27 28 29 30]
[31 32 33 34 35]]
18
数组切片
print(e[0,1:4])
print(e[1:4,0])
print(e[::2,::2])
print(e[:,1])
[12 13 14]
[16 21 26]
[[11 13 15]
[21 23 25]
[31 33 35]]
[12 17 22 27 32]
数组性质
print(type(e))
print(e.dtype)
print(e.size)
print(e.shape)
print(e.itemsize)#每项占字节数
print(e.ndim)#数组维度
print(e.nbytes)#总占空间字节数
int32
25
(5, 5)
4
2
100
数组的使用
a = np.arange(25)
a = a.reshape((5,5))
b = np.array([12,32,123,14,12,4,35,32,2,5,23,53,1,3,124,325,15,34,1,23,21,23,34,64,34])
b = b.reshape((5,5))
print(a + b)
print(a - b)
print(a * b)
print(a / b)
print(a ** 2)
print(a < b)
print(a > b)
print(a.dot(b))
[[ 12 33 125 17 16]
[ 9 41 39 10 14]
[ 33 64 13 16 138]
[340 31 51 19 42]
[ 41 44 56 87 58]]
[[ -12 -31 -121 -11 -8]
[ 1 -29 -25 6 4]
[ -13 -42 11 10 -110]
[-310 1 -17 17 -4]
[ -1 -2 -12 -41 -10]]
[[ 0 32 246 42 48]
[ 20 210 224 16 45]
[ 230 583 12 39 1736]
[4875 240 578 18 437]
[ 420 483 748 1472 816]]
[[0.00000000e+00 3.12500000e-02 1.62601626e-02 2.14285714e-01
3.33333333e-01]
[1.25000000e+00 1.71428571e-01 2.18750000e-01 4.00000000e+00
1.80000000e+00]
[4.34782609e-01 2.07547170e-01 1.20000000e+01 4.33333333e+00
1.12903226e-01]
[4.61538462e-02 1.06666667e+00 5.00000000e-01 1.80000000e+01
8.26086957e-01]
[9.52380952e-01 9.13043478e-01 6.47058824e-01 3.59375000e-01
7.05882353e-01]]
[[ 0 1 4 9 16]
[ 25 36 49 64 81]
[100 121 144 169 196]
[225 256 289 324 361]
[400 441 484 529 576]]
[[ True True True True True]
[False True True False False]
[ True True False False True]
[ True False True False True]
[ True True True True True]]
[[False False False False False]
[ True False False True True]
[False False True True False]
[False True False True False]
[False False False False False]]
[[1109 278 272 267 458]
[3034 1068 1392 687 1448]
[4959 1858 2512 1107 2438]
[6884 2648 3632 1527 3428]
[8809 3438 4752 1947 4418]]
特殊运算符
a = np.arange(10)
print(a)
print(a.sum())
print(a.min())
print(a.max())
print(a.cumsum())
[0 1 2 3 4 5 6 7 8 9]
45
0
9
[ 0 1 3 6 10 15 21 28 36 45]
花式索引
a = np.arange(0,100,10)
indices = [1,5,6,-2]
b = a[indices]
print(a)
print(b)
[ 0 10 20 30 40 50 60 70 80 90]
[10 50 60 80]
布尔屏蔽
import matplotlib.pyplot as plt
a = np.linspace(0,2 * np.pi,50)
b = np.sin(a)
c = plt.plot(a,b)
mask = b >=0
plt.plot(a[mask],b[mask],'bo')
mask = (b >= 0)&(a <= np.pi/2)
plt.plot(a[mask],b[mask],'go')
plt.show(c)
缺省索引
a = np.arange(50,150,10)
b = a[:5]
c = a[a >=50]
print(b)
print(c)
[50 60 70 80 90]
[ 50 60 70 80 90 100 110 120 130 140]
Where函数
a = np.arange(0,100,10)
b = np.where(a < 50)
c = np.where(a >= 50)[0]
print(b)
print(c)
(array([0, 1, 2, 3, 4], dtype=int64),)
[5 6 7 8 9]