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物联191高锐，1908070103
复盘过程代码：
import numpy as np
c = np.arange(1,13).reshape(6,2)
array([[ 1, 2],
[ 3, 4],
[ 5, 6],
[ 7, 8],
[ 9, 10],
[11, 12]])
np.vsplit(c,3)
[array([[1, 2],
[3, 4]]),
array([[5, 6],
[7, 8]]),
array([[ 9, 10],
[11, 12]])]
d = c.T
array([[ 1, 3, 5, 7, 9, 11],
[ 2, 4, 6, 8, 10, 12]])
np.hsplit(d,3)
[array([[1, 3],
[2, 4]]),
array([[5, 7],
[6, 8]]),
array([[ 9, 11],
[10, 12]])]
import numpy as np
a = np.array((1,2,4,6))
b = np.array((7,8,9,20))
e = np.dstack((a,b))
ay([[[ 1, 7],
[ 2, 8],
[ 4, 9],
[ 6, 20]]])
np.dsplit(e,2)
[array([[[1],
[2],
[4],
[6]]]),
array([[[ 7],
[ 8],
[ 9],
[20]]])]
inistate =np.array([1,2,3,4])
pre_inistate = inistate[0:3]
pre_inistate
array([1, 2, 3])
import numpy as np
a = np.array([1,1,1,1])
b = np.array([[1],[1],[1],[1]])
a+b
array([[2, 2, 2, 2],
[2, 2, 2, 2],
[2, 2, 2, 2],
[2, 2, 2, 2]])
c = np.array([[1,1,1,1]])
c+b
array([[2, 2, 2, 2],
[2, 2, 2, 2],
[2, 2, 2, 2],
[2, 2, 2, 2]])
W = np.array([[1,1,1],[2,2,2]])
W[:,1]
array([1, 2])
W[1]
array([2, 2, 2])
W[:,1] = np.array([3,3])
W
array([[1, 3, 1],
[2, 3, 2]])
import numpy as np
matrix = [
[1,2,3,4],
[5,6,7,8],
[9,10,11,12]
]
p1 = np.delete(matrix, 1, 0) # 第0维度(行)第1行被删除(初始行为0行)
print('>>>>p1>>>>\n',p1)
>>>>p1>>>>
[[ 1 2 3 4]
[ 9 10 11 12]]
p2 = np.delete(matrix, 1, 1) # 第1维度(列)第1行被删除
print('>>>>p2>>>>\n',p2)
>>>>p2>>>>
[[ 1 3 4]
[ 5 7 8]
[ 9 11 12]]
p3 = np.delete(matrix, 1) # 拉平后删除第1个元素(初始为第0个)
print('>>>>p3>>>>\n',p3)
>>>>p3>>>>
[ 1 3 4 5 6 7 8 9 10 11 12]
p4 = np.delete(matrix, [0,1], 1) # 第1维度(列)第0、1行被删除
print('>>>>p4>>>>\n',p4)
>>>>p4>>>>
[[ 3 4]
[ 7 8]
[11 12]]
import numpy as np
matrix = [
[1,2,3,4], [5,6,7,8], [9,10,11,12]
]
q1 = np.insert(matrix, 1, [1,1,1,1], 0)
# 第0维度(行)第1行添加[1,1,1,1]
print('>>>>q1>>>>\n',q1)
>>>>q1>>>>
[[ 1 2 3 4]
[ 1 1 1 1]
[ 5 6 7 8]
[ 9 10 11 12]]
q2 = np.insert(matrix, 0, [1,1,1], 1) # 第1维度(列)第0列添加1,1,1
print('>>>>q2>>>>\n',q2)
>>>>q2>>>>
[[ 1 1 2 3 4]
[ 1 5 6 7 8]
[ 1 9 10 11 12]]
q3 = np.insert(matrix, 2, [1,2,3], 1) # 第1维度(列)第2行添加[1,2,3]
print('>>>>q3>>>>\n',q3)
>>>>q3>>>>
[[ 1 2 1 3 4]
[ 5 6 2 7 8]
[ 9 10 3 11 12]]
import numpy as np
c = [
[1,2,3,4],
[5,6,7,8],
[9,10,11,12]
]
m1 = np.append(c,[[2,2,2,2]],axis=0)
print('m1----\n',m1)
m1----
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]
[ 2 2 2 2]]
m2 = np.append(matrix,[[1],[1],[100]],axis=1)
print('m2-----\n',m2)
m2-----
[[ 1 2 3 4 1]
[ 5 6 7 8 1]
[ 9 10 11 12 100]]
m3 = np.append(matrix,[1,1,1,1])
print('m3-----\n',m3)
m3-----
[ 1 2 3 4 5 6 7 8 9 10 11 12 1 1 1 1]
import numpy as np
a = np.random.choice(8,4) # 从0~8中随机选择4个数组成一维数组
array([4, 5, 7, 7])
b = np.random.choice([0,1,2,23,34,45,56,89,7,55],4) # 从给定list中随机选择4个数组成一维数组
array([34, 56, 1, 7])
c = np.random.choice(np.array([0,1,2,3,4,5,6,7,8,9,10]),4) # 将list换成array数组依然可以运行，效果一致
array([0, 0, 1, 5])
d = np.random.choice([0,1,2,3,4,75,6,55,45,89],5,replace=False) # 上述均有重复，将replace设置为False，即可按要求没有重复的选取
array([89, 2, 55, 1, 6])
f = np.random.choice(np.array([0,1,2,3,4,5,6]),5,p=[0.1,0.1,0.1,0.1,0.1,0.1,0.4])
array([0, 3, 6, 2, 5])
1
import numpy as np
a = np.array([[1,1,1],[2,2,2],[5,3,6],[25,5,4]])
array([[ 1, 1, 1],
[ 2, 2, 2],
[ 5, 3, 6],
[25, 5, 4]])
b1 = np.argmax(a) # 将数组a拉平，最大值索引为12(初始索引为0)
b2 = np.argmax(a, axis=0) # 按列选取最大值的索引
array([3, 3, 2], dtype=int64)
b3 = np.argmax(a, axis=1) # 按行选取最大值的索引
array([0, 0, 2, 0], dtype=int64)
import numpy as np
y1 = np.linspace(-5.0,5.0) # 默认生成50个数据
array([-5. , -4.79591837, -4.59183673, -4.3877551 , -4.18367347,
-3.97959184, -3.7755102 , -3.57142857, -3.36734694, -3.16326531,
-2.95918367, -2.75510204, -2.55102041, -2.34693878, -2.14285714,
-1.93877551, -1.73469388, -1.53061224, -1.32653061, -1.12244898,
-0.91836735, -0.71428571, -0.51020408, -0.30612245, -0.10204082,
0.10204082, 0.30612245, 0.51020408, 0.71428571, 0.91836735,
1.12244898, 1.32653061, 1.53061224, 1.73469388, 1.93877551,
2.14285714, 2.34693878, 2.55102041, 2.75510204, 2.95918367,
3.16326531, 3.36734694, 3.57142857, 3.7755102 , 3.97959184,
4.18367347, 4.3877551 , 4.59183673, 4.79591837, 5. ])
y2 = np.linspace(1,9,7) # 生成7个数据，包括首尾
array([1. , 2.33333333, 3.66666667, 5. , 6.33333333,
7.66666667, 9. ])
y3 = np.linspace(1,10,7,endpoint=False) # 不包括尾部数据
array([1. , 2.28571429, 3.57142857, 4.85714286, 6.14285714,
7.42857143, 8.71428571])
y4= np.linspace(1, 10, 6, retstep=True) # 将步长与结果的数组放入一个list、
(array([ 1. , 2.8, 4.6, 6.4, 8.2, 10. ]), 1.8)
import numpy as np
x = np.array([[1,2,3],[4,5,6],[1,2,3]])
x.flatten()#行拉平
array([1, 2, 3, 4, 5, 6, 1, 2, 3])
x.ravel()
array([1, 2, 3, 4, 5, 6, 1, 2, 3])
x.ravel('F')#列拉平
array([1, 4, 1, 2, 5, 2, 3, 6, 3])
x.flatten('F')
array([1, 4, 1, 2, 5, 2, 3, 6, 3])
x.flatten()[1] = 20
array([[1, 2, 3],
[4, 5, 6],
[1, 2, 3]])
x.ravel()[1] = 20
array([[ 1, 20, 3],
[ 4, 5, 6],
[ 1, 2, 3]])
x.reshape(1,-1) # 注意结果仍然是二维
array([[ 1, 20, 3, 4, 5, 6, 1, 2, 3]])
x = np.array([1,2,3,6,7,8]) # 注意操作的是数组，原x是数组
x[None,:] # 转成行向量(二维矩阵)
array([[1, 2, 3, 6, 7, 8]])
x[:,None]
array([[1],
[2],
[3],
[6],
[7],
[8]])
x[np.newaxis, :] # np.newaxis与None用法一致
array([[1, 2, 3, 6, 7, 8]])
x = np.array([[1,2,3],[2,3,4]])
np.prod(x)
np.prod(x,axis=1)#行相乘
array([ 6, 24])
np.prod(x,axis=0)#列相乘
array([ 2, 6, 12])
import numpy as np
x = np.array([[1,2,3],[-3,2,4],[5,-2,9]])
array([[ 1, 2, 3],
[-3, 2, 4],
[ 5, -2, 9]])
1
y1 = np.maximum(2,x) # 把小于2的元素置2，比改变x的值
array([[2, 2, 3],
[2, 2, 4],
[5, 2, 9]])
y2 = np.minimum(3,x) # 把大于3的元素置3，不改变x的值
y2
array([[ 1, 2, 3],
[-3, 2, 3],
[ 3, -2, 3]])
x1 = x.copy()
array([[ 1, 2, 3],
[-3, 2, 4],
[ 5, -2, 9]])
x1[x1 < 2] = 0 # 把小于2的元素置0，改变x1的值
array([[0, 2, 3],
[0, 2, 4],
[5, 0, 9]])
x2 = x.copy()
x2[x2 > 3] = 2 # 把大于3的元素置2，改变x2的值
array([[ 1, 2, 3],
[-3, 2, 2],
[ 2, -2, 2]])
import numpy as np
x = np.array([[1,2,3],[-3,1,4],[1,-2,9]])
array([[ 1, 2, 3],
[-3, 1, 4],
[ 1, -2, 9]])
x1 = x.copy() # copy()，开辟新地址
x1[x1 > 2] = 555#把大于2的置555，改变x1的值
array([[ 1, 2, 555],
[ -3, 1, 555],
[ 1, -2, 555]])
array([[ 1, 2, 3],
[-3, 1, 4],
[ 1, -2, 9]])
x2 = x # 直接等于，未开辟新地址，x2与x相关联
array([[ 1, 2, 3],
[-3, 1, 4],
[ 1, -2, 9]])
x2[x2>0] = 0#大于零的变为0，x2改变值
array([[ 0, 0, 0],
[-3, 0, 0],
[ 0, -2, 0]])
array([[ 0, 0, 0],
[-3, 0, 0],
[ 0, -2, 0]])
x = np.array([[1,2,3],[-3,2,4],[5,-2,9]])
x3 = x[2] # 取x的第3行
array([ 5, -2, 9])
x3[1] = 666 # 将x3第2个元素置666
array([[ 1, 2, 3],
[ -3, 2, 4],
[ 5, 666, 9]])
port numpy as np
x = np.array([[1,2,3],[4,5,6],[1,1,1]])
np.zeros_like(x) # 生成一个和x大小相同的全零矩阵
array([[0, 0, 0],
[0, 0, 0],
[0, 0, 0]])
import numpy as np
n = np.random.rand(3,3)#生成一个3行3列的随机矩阵，并且服从【0，1】
array([[0.41149638, 0.61946996, 0.76353632],
[0.15538046, 0.9615752 , 0.11827965],
[0.62096548, 0.11298423, 0.28300111]])
y = np.multiply(0.1,np.random.randn(2,3))+0.5 # 一般正太分布
array([[0.48038695, 0.40829938, 0.43245035],
[0.36174277, 0.51192861, 0.5244684 ]])
import numpy as np
z = np.random.randint(2,9,(2,3))
array([[8, 2, 4],
[7, 8, 4]])
m = np.random.randint(9,size = (2,3))
array([[7, 1, 2],
[1, 3, 3]])
x = 'Who the hell are U?'
type(x)
assert type(x)==str, 'x is not str'
x = [1,2,3,67,89]
type(x)
import numpy as np
A = np.arange(95,104).reshape(3,3)
array([[ 95, 96, 97],
[ 98, 99, 100],
[101, 102, 103]])
np.pad(A,((1,2),(1,3)),'constant',constant_values = (0,0))
array([[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 95, 96, 97, 0, 0, 0],
[ 0, 98, 99, 100, 0, 0, 0],
[ 0, 101, 102, 103, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0]])
b = np.array([[[1,2],[3,4]],[[3,4],[7,8]],[[4,5],[1,2]]])
array([[[1, 2],
[3, 4]],
[[3, 4],
[7, 8]],
[[4, 5],
[1, 2]]])
np.pad(b, ((2,2),(1,1),(3,3)), 'constant', constant_values = 0)
array([[[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 2, 0, 0, 0],
[0, 0, 0, 3, 4, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 3, 4, 0, 0, 0],
[0, 0, 0, 7, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 4, 5, 0, 0, 0],
[0, 0, 0, 1, 2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]]])
import numpy as np
x = np.empty([3,3], dtype = str)
print (x)
[['' '' '']
['' '' '']
['' '' '']]
import numpy as np
c = np.array([[1,2],[3,4]])
array([[1, 2],
[3, 4]])
c.astype(np.float32)
array([[1., 2.],
[3., 4.]], dtype=float32)
import numpy as np
x = np.array([1,3,5])
y = np.array([4,6])
XX,YY = np.meshgrid(x,y)
array([[1, 3, 5],
[1, 3, 5]])
array([[4, 4, 4],
[6, 6, 6]])
import numpy as np
x = np.array([[3,4,5],[1,3,4],[1,1,1]])
y = np.array([[1,1,1],[2,2,2],[2,2,2]])
np.hstack((x,y)) # 水平堆叠
array([[3, 4, 5, 1, 1, 1],
[1, 3, 4, 2, 2, 2],
[1, 1, 1, 2, 2, 2]])
np.vstack((x,y)) # 竖直堆叠
array([[3, 4, 5],
[1, 3, 4],
[1, 1, 1],
[1, 1, 1],
[2, 2, 2],
[2, 2, 2]])
import numpy as np
a = np.array([0.11111,0.89568,5.00688])
np.round(a) # 四舍五入取整， np.around 和 round 用法一致
array([0., 1., 5.])
np.round(a,decimals = 2) # 四舍五入保留2位小数
array([0.11, 0.9 , 5.01])
np.floor(a) # 向下取整
array([0., 0., 5.])
np.ceil(a) # 向上取整
array([1., 1., 6.])
import numpy as np
c = np.array([1,2,5,4])
c[:,np.newaxis]
array([[1],
[2],
[5],
[4]])
c[np.newaxis,:]
array([[1, 2, 5, 4]])
import numpy as np
a = np.array([[1,2,3],[4,5,6]])
a = np.array([[1,2,3,6],[4,5,6,6]])
a1 = a.reshape((1,2,4))
a1
array([[[1, 2, 3, 6],
[4, 5, 6, 6]]])
b = np.array([[3,4,5,6],[1,2,3,4],[4,5,5,5]])
array([[3, 4, 5, 6],
[1, 2, 3, 4],
[4, 5, 5, 5]])
b1 = b.reshape((1,3,4)).transpose((1,0,2))
array([[[3, 4, 5, 6]],
[[1, 2, 3, 4]],
[[4, 5, 5, 5]]])
array([[[1, 2, 3, 6],
[4, 5, 6, 6]]])
a1+b1
array([[[ 4, 6, 8, 12],
[ 7, 9, 11, 12]],
[[ 2, 4, 6, 10],
[ 5, 7, 9, 10]],
[[ 5, 7, 8, 11],
[ 8, 10, 11, 11]]])
c = np.array([[[1,2,5],[3,4,6]],[[4,5,6],[7,8,9]]])
array([[[1, 2, 5],
[3, 4, 6]],
[[4, 5, 6],
[7, 8, 9]]])
c.transpose(1,0,2) # 将c的维度按照 第1维度，第0维度，第2维度的排序排成 第0,1,2维度
array([[[1, 2, 5],
[4, 5, 6]],
[[3, 4, 6],
[7, 8, 9]]])
c.transpose(1,2,0) # 将c的维度按照 第1维度，第2维度，第0维度的排序排成 第0,1,2维度
array([[[1, 4],
[2, 5],
[5, 6]],
[[3, 7],
[4, 8],
[6, 9]]])
import numpy as np
a = np.array([2,2,3,4,5,5,6,7])
a[0:7:2]
array([2, 3, 5, 6])
a[::-1]
array([7, 6, 5, 5, 4, 3, 2, 2])
import numpy as np
a = np.array([2,2,3,4,5,5,6,7])
s = slice(0,7,2)
a[s]
array([2, 3, 5, 6])
大概内容（引用自老师）@Dr.PhyiscsLogical
1. numpy基本加减和取行操作
2. 矩阵删除、插入、尾部添加操作(delete,insert,append)
3. delete()函数
4. insert()函数
5. append()函数
6. np.random.choice(a, size, replace, p)
7. np.argmax(a, axis=None, out=None)
8. 星号(*)的作用
9. ndarray.ndim代表的就是shape元组的长度。
10. numpy.linspace用法
11.拉平操作 ravel()和faltten()及reshape(1,-1)的区别联系(补充[None,:]操作)
12.np.prod() 计算元素乘积
13.把矩阵大于或小于N的元素置M的技巧
14. numpy中的矩阵copy问题
15. np.zeros_like()构造全零矩阵，无需指定大小
random.rand和random.rand和random.randint区别
np.random.random() 生成一个随机数
np.random.uniform()
np.random.rand() 生成均匀分布矩阵
np.random.randn() 生成正太分布矩阵
np.randm.randint() 生成离散均匀分布的整数值组成的矩阵
python 断言 assert
星号( * ) 和 .dot 和 np.multiply 的区别联系
np.pad()函数进行padding操作
numpy.empty() 创建指定形状和数据类型且未初始化的数组
判断两个矩阵元素完全相等&&存在相等
关于 np.mat() 的使用
np.full 用于形成元素全为某元素的矩阵
astype 转换数据类型
np.meshgrid() 快速生成网格
np.hstack() 和 np.vstack() 用于堆叠矩阵
numpy 取整
np.newaxis 在特定位置增加一个维度
python 广播机制
numpy.transpose()转置
numpy 中双冒号的用法
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