#numpy 矩阵运算库
import numpy as np
#常用的创建函数
#array 从列表或者元祖创建数组
ar1 = np.array([1,2,3,4,5,6])
print(ar1)
#创建数组0-10之间，步长为1
ar2 = np.arange(0,10,1)
print(ar2)
#将0-10分成5个元素的数组
ar3 = np.linspace(0,10,5)
print(ar3)
#创建3行3列的阵列
ar4 = np.indices((3,3))
print(ar4)
#创建3行3列的随机数组
ar5 = np.random.rand(3,3)
print(ar5)
#创建3行3列的全为1的数组
ar6 = np.ones((3,3),float)
print(ar6)
#创建3行3列全为0的数组 np.zeros((3,3))
ar8 = np.zeros((3,3))
print(ar8)
#对角线为1，其他为0
ar7 = np.eye(3,3)
print(ar7)
[1 2 3 4 5 6]
[0 1 2 3 4 5 6 7 8 9]
[ 0. 2.5 5. 7.5 10. ]
[[[0 0 0]
[1 1 1]
[2 2 2]]
[[0 1 2]
[0 1 2]
[0 1 2]]]
[[0.13282886 0.50496395 0.7787284 ]
[0.80039057 0.40068522 0.1187407 ]
[0.51833548 0.11563461 0.72263001]]
[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
In [32]:
#数组的基础属性
#数组轴的个数，秩
print(ar6.ndim)
#每个维度的大小
print(ar6.shape)
#元素的总数
print(ar6.size)
#元素数据的类型
print(ar6.dtype)
#每个元素的字节数
print(ar6.itemsize)
#数组元素缓冲区的地址
print(ar6.data)
#元素迭代器
print(ar6.flat)
2
(3, 3)
9
float64
8
<memory at 0x00000000053A47E0>
<numpy.flatiter object at 0x00000000065992D0>
In [16]:
#数组的形态操作
ar7 = np.ones((3,3))
print(ar7)
#np.resize 修改数组
ar7.resize(2,2)
print(ar7)
#对数组进行将维度
ar8= np.ones((3,4))
print(ar8.flatten())
ar8= np.ones((3,4))
print(ar8.ravel())
#reshape
ar9= np.ones((3,4))
print(ar9)
print(ar9.reshape(2,6))
#astype 类型转换
ar10 = np.arange(10,dtype=float).reshape(2,5)
print(ar10)
print(ar10.astype(np.int32))
[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
[[1. 1.]
[1. 1.]]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[[1. 1. 1. 1.]
[1. 1. 1. 1.]
[1. 1. 1. 1.]]
[[1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1.]]
[[0. 1. 2. 3. 4.]
[5. 6. 7. 8. 9.]]
[[0 1 2 3 4]
[5 6 7 8 9]]
In [34]:
#横向相加，垂直相加
a = np.arange(5)
b = np.arange(5,10)
print(a,b)
#按行拼接
ar = np.vstack((a,b))
print(ar)
#按列拼接
ar = np.hstack((a,b))
print(ar)
ar = np.stack((a,b),1)
print(ar)
ar = np.stack((a,b),0)
print(ar)
[0 1 2 3 4] [5 6 7 8 9]
[[0 1 2 3 4]
[5 6 7 8 9]]
[0 1 2 3 4 5 6 7 8 9]
[[0 5]
[1 6]
[2 7]
[3 8]
[4 9]]
[[0 1 2 3 4]
[5 6 7 8 9]]
In [28]:
#数组的拆分
ar = np.arange(16).reshape(4,4)
print(ar)
#按列拆分
ar1 = np.hsplit(ar,2)
print(ar1)
#按行拆分
ar1 = np.vsplit(ar,2)
print(ar1)
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]]
[array([[ 0, 1],
[ 4, 5],
[ 8, 9],
[12, 13]]), array([[ 2, 3],
[ 6, 7],
[10, 11],
[14, 15]])]
[array([[0, 1, 2, 3],
[4, 5, 6, 7]]), array([[ 8, 9, 10, 11],
[12, 13, 14, 15]])]
In [44]:
#数组的简单运算
ar = np.arange(10)
print(ar)
print(ar*2)
print(ar/2)
print(ar+2)
print(ar//2)
print(ar**2)
[0 1 2 3 4 5 6 7 8 9]
[ 0 2 4 6 8 10 12 14 16 18]
[0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5]
[ 2 3 4 5 6 7 8 9 10 11]
[0 0 1 1 2 2 3 3 4 4]
[ 0 1 4 9 16 25 36 49 64 81]
import numpy as np
In [12]:
ar = np.arange(20)
#一维数组切片
ar = np.arange(20)
print(ar[10])
print(ar[0:10])
print(ar[0:10:2])
10
[0 1 2 3 4 5 6 7 8 9]
[0 2 4 6 8]
In [35]:
#二维数组的切片
ar = np.arange(16).reshape(4,4)
print(ar)
print(ar[0],ar[0,1])
print(ar[0:2])
print(ar[2:3])
print(ar[:4,2:])
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]]
[0 1 2 3] 1
[[0 1 2 3]
[4 5 6 7]]
[[ 8 9 10 11]]
[[ 2 3]
[ 6 7]
[10 11]
[14 15]]
In [38]:
#复制
#复制
br = ar.copy()
cr = ar
print(id(cr),id(br),id(ar))
92916432 92916352 92916432
In [41]:
_new = ar[ar > 5]
print(ar_new)
#小作业
ar = np.arange(10).reshape(2,5)
ar_new = ar[ar > 5]
print(ar_new)
[6 7 8 9]
In [48]:
#随机数 正态分布
samples = np.random.normal(size=(100))
print(samples)
import matplotlib.pyplot as plt
% matplotlib inline
plt.hist(samples)
[-0.61227469 -0.36103937 0.28734323 0.63352977 1.13658948 1.15243507
0.70852784 0.40504143 0.2712728 -0.27399682 -0.68746028 0.15983535
-0.02442818 -1.71875477 -1.90132349 -1.52038551 2.04971197 -0.97968765
-0.7398707 2.58470461 -0.06499621 -0.52215928 1.11283527 1.77455139
-0.29510978 -0.3529555 0.25065179 -3.32293627 0.39312473 0.64401673
-0.24950078 1.29350219 -0.10785972 0.82839765 -0.14743582 1.28908868
1.38144382 1.1830453 1.55924739 1.00124522 -0.31871122 0.66595038
0.37629854 -0.20744411 -0.44468078 -0.57489172 0.16270152 1.96241238
1.69363656 -1.89301945 -0.80169326 -0.20132487 -0.4355692 -0.69024891
0.58913554 -1.62332398 -0.46826792 0.12339841 0.12177624 0.60421575
-0.00350087 1.28861972 0.10536274 1.36020219 -0.4496176 0.65117662
1.03193623 0.82908065 0.18313433 -0.02936144 -2.60726463 -1.58424282
-0.09221244 0.39805004 -0.46286613 -0.13994535 0.46165815 1.7918481
1.34881523 0.49391726 0.10413002 1.03006967 -1.00628197 0.556884
-0.86332625 -1.02390836 -0.46605733 -0.32249655 0.61331111 0.39275608
-0.70372193 0.37659617 -0.39106357 0.22215355 -1.40161871 1.04150908
-0.12478619 2.40395458 1.26045816 -0.45474683]
Out[48]:
(array([ 1., 1., 5., 5., 17., 25., 21., 17., 5., 3.]),
array([-3.32293627, -2.73217218, -2.1414081 , -1.55064401, -0.95987992,
-0.36911583, 0.22164826, 0.81241234, 1.40317643, 1.99394052,
2.58470461]),
<a list of 10 Patch objects>)
#生成100个随机浮点数 均匀分布
a = np.random.rand(1000)
% matplotlib inline
plt.hist(a)
(array([110., 93., 89., 106., 95., 105., 115., 90., 97., 100.]),
array([5.47661376e-05, 1.00025836e-01, 1.99996906e-01, 2.99967976e-01,
3.99939045e-01, 4.99910115e-01, 5.99881185e-01, 6.99852255e-01,
7.99823325e-01, 8.99794394e-01, 9.99765464e-01]),
<a list of 10 Patch objects>)
#生成一个浮点数或者浮点数组 正态分布
samples1 = np.random.randn(1000)
samples2 = np.random.randn(1000)
plt.scatter(samples1,samples2)
<matplotlib.collections.PathCollection at 0x112d2710>
