import numpy as np
c=np.arange(1,13).reshape(6,2)
c
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#水平拆分
d
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]])]
a=np.array([[1,2,3,4],[11,12,13,45]])
b=np.array([[9,8,7,6],21,[22,23,62]])
e=np.dstack((a.b))
e
1 aarray([[[ 1, 9],
[ 2, 8],
[ 3, 7],
[ 4, 6]],
[[11, 21],
[12, 22],
[13, 23],
[45, 62]]])
np.dsplit(e,2)
[array([[[ 1],
[ 2],
[ 3],
[ 4]],
[[11],
[12],
[13],
[45]]]),
array([[[ 9],
[ 8],
[ 7],
[ 6]],
[[21],
[22],
[23],
[62]]])]
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([[2,2,2,2,]])
b+c
array([[3, 3, 3, 3],
[3, 3, 3, 3],
[3, 3, 3, 3],
[3, 3, 3, 3]])
W=np.array([[1,1,1,],[2,2,2]])
W[:,1]
array([1, 2])
W[1]
array([2, 2, 2])
W[:,1]=np.array([5,5])
W
array([[1, 5, 1],
[2, 5, 2]])
import numpy as np#delete函数
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)
[[ 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)
[[ 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)
[ 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)
[[ 3 4]
[ 7 8]
[11 12]]
import numpy as np#insert()函数
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)
[[ 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)
[[ 1 1 2 3 4]
[ 1 5 6 7 8]
[ 1 9 10 11 12]]
q3 = np.insert(matrix, 3, [1,1,1,1], 0) # 第0维度(行)第3行添加[1,1,1,1]
print(q3)
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]
[ 1 1 1 1]]
import numpy as np#append()函数
matrix = [
[1,2,3,4],
[5,6,7,8],
[9,10,11,12]]
m1 = np.append(matrix,[[1,1,1,1]],axis=0)
print(m1)
m2 = np.append(matrix,[[1],[1],[1]],axis=1)
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]
[ 1 1 1 1]]
print(m2)
m3 = np.append(matrix,[[1],[1],[1]])
[[ 1 2 3 4 1]
[ 5 6 7 8 1]
[ 9 10 11 12 1]]
print(m3)
[ 1 2 3 4 5 6 7 8 9 10 11 12 1 1 1]
import numpy as np
a1 = np.random.choice(7,5) # 从0~7中随机选择5个数组成一维数组
a1
array([4, 2, 2, 0, 4])
a2 = np.random.choice([0,1,2,3,4,5,6],5) # 从给定list中随机选择5个数组成一维数组
a2
array([4, 1, 0, 3, 5])
a3 = np.random.choice(np.array([0,1,2,3,4,5,6]),5) # 将list换成array数组依然可以运行，效果一致
a3
array([3, 1, 1, 1, 3])
a4 = np.random.choice([0,1,2,3,4,5,6],5,replace=False) # 将replace设置为False，即可按要求没有重复的选取
a4
array([2, 1, 3, 4, 6])
a5 = np.random.choice(np.array([0,1,2,3,4,5,6]),5,p=[0.1,0.1,0.1,0.1,0.1,0.2,0.3])
a5
array([5, 6, 5, 6, 2])
import numpy as np
a = np.array([[1,1,2],[9,2,2],[0,6,6]])
a
array([[1, 1, 2],
[9, 2, 2],
[0, 6, 6]])
b1 = np.argmax(a) # 将数组a拉平，最大值索引为9(初始索引为0)
b1
3
b2 = np.argmax(a, axis=0) # 按列选取最大值的索引
b2
array([1, 2, 2], dtype=int64)
b3 = np.argmax(a, axis=1) # 按行选取最大值的索引
b3
array([2, 0, 1], dtype=int64)
import numpy as np
y1 = np.linspace(0.0,20.0) # 默认生成50个数据
y1
array([ 0. , 0.40816327, 0.81632653, 1.2244898 , 1.63265306,
2.04081633, 2.44897959, 2.85714286, 3.26530612, 3.67346939,
4.08163265, 4.48979592, 4.89795918, 5.30612245, 5.71428571,
6.12244898, 6.53061224, 6.93877551, 7.34693878, 7.75510204,
8.16326531, 8.57142857, 8.97959184, 9.3877551 , 9.79591837,
10.20408163, 10.6122449 , 11.02040816, 11.42857143, 11.83673469,
12.24489796, 12.65306122, 13.06122449, 13.46938776, 13.87755102,
14.28571429, 14.69387755, 15.10204082, 15.51020408, 15.91836735,
16.32653061, 16.73469388, 17.14285714, 17.55102041, 17.95918367,
18.36734694, 18.7755102 , 19.18367347, 19.59183673, 20. ])
y2 = np.linspace(1,10,5) # 生成5个数据，包括首尾
y2
array([ 1. , 3.25, 5.5 , 7.75, 10. ])
y3 = np.linspace(1,10,10,endpoint=False) # 不包括尾部数据
y3
array([1. , 1.9, 2.8, 3.7, 4.6, 5.5, 6.4, 7.3, 8.2, 9.1])
y4= np.linspace(1, 10, 6, retstep=True) # 将步长与结果的数组放入一个list、
y4
(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()
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')
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
x
array([[1, 2, 3],
[4, 5, 6],
[1, 2, 3]])
x.ravel()[1] = 20
x
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, 2, 3, 4, 5, 6, 1, 2, 3]]) # 这里有两个方括号
x = np.array([1,2,3,6,7,8])
x[None,:] # 转成行向量(二维矩阵)# 注意操作的是数组，即原x是数组
array([[1, 2, 3, 6, 7, 8]])
x[:,None] # 转成列向量(二维矩阵)***
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)
144
np.prod(x,axis=1)
array([ 6, 24])
np.prod(x,axis=0)
array([ 2, 6, 12])
import numpy as np
x = np.array([[1,2,3],[-3,2,4],[5,-2,9]])
x
array([[ 1, 2, 3],
[-3, 2, 4],
[ 5, -2, 9]])
y1 = np.maximum(0,x) # 把小于0的元素置0，比改变x的值
y1
array([[1, 2, 3],
[0, 2, 4],
[5, 0, 9]])
y2 = np.minimum(0,x) # 把大于0的元素置0，不改变x的值
y2
array([[ 0, 0, 0],
[-3, 0, 0],
[ 0, -2, 0]])
x1 = x.copy()
x1
array([[ 1, 2, 3],
[-3, 2, 4],
[ 5, -2, 9]])
x1[x1 < 0] = 0 # 把小于0的元素置0，改变x1的值
x1
array([[1, 2, 3],
[0, 2, 4],
[5, 0, 9]])
x2 = x.copy()
x2[x2 > 0] = 0 # 把大于0的元素置0，改变x2的值
x2
array([[ 0, 0, 0],
[-3, 0, 0],
[ 0, -2, 0]])
import numpy as np
x = np.array([[1,2,3],[-3,2,4],[5,-2,9]])
x
array([[ 1, 2, 3],
[-3, 2, 4],
[ 5, -2, 9]])
x1 = x.copy() # copy()，开辟新地址
x1[x1 > 0] = 0
x1
array([[ 0, 0, 0],
[-3, 0, 0],
[ 0, -2, 0]])
x # x不变
array([[ 1, 2, 3],
[-3, 2, 4],
[ 5, -2, 9]])
x2 = x # 直接等于，未开辟新地址，x2与x相关联
x2
array([[ 1, 2, 3],
[-3, 2, 4],
[ 5, -2, 9]])
x2[x2>0] = 0
x2
array([[ 0, 0, 0],
[-3, 0, 0],
[ 0, -2, 0]])
x # x也改变
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
x3
array([ 5, -2, 9])
x3[2] = 100 # 将x3第3个元素置100
x # x中对应的元素置也被置成100了
array([[ 1, 2, 3],
[ -3, 2, 4],
[ 5, -2, 100]])
import numpy as np
x = np.array([[1,2,3],[4,5,6]])
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]])
import numpy as np
n = np.random.rand(3,4)
n
array([[0.2475704 , 0.88462247, 0.13423477, 0.1450988 ],
[0.22450036, 0.20503036, 0.11874109, 0.84063727],
[0.2036903 , 0.356566 , 0.21515832, 0.52965046]])
import numpy as np
x = np.random.randn(2,3)
x
array([[ 0.0663273 , 1.00451172, -1.34876747],
[ 0.51042144, 1.06405736, 1.00671665]])
y = np.multiply(0.1,np.random.randn(2,3))+0.5 # 一般正太分布
y
array([[0.38764744, 0.47289113, 0.55155268],
[0.55454147, 0.69613112, 0.51384308]])
import numpy as np
z = np.random.randint(2,9,(2,3))
z
array([[2, 6, 8],
[3, 4, 5]])
m = np.random.randint(9,size = (2,3))
m
array([[0, 8, 1],
[7, 3, 5]])
x = ‘You are right’
type(x)
str
assert type(x)==str, ‘x is not str’
x = [1,2,3]
type(x)
list
assert type(x)==str, ‘x is not str’
x = [1,2,3]
type(x)
list
assert type(x)==str, ‘x is not str’
Traceback (most recent call last):
File “”, line 1, in
File "", line 1
assert type(x)==str, ‘x is not str’
A = np.arange(95,99).reshape(2,2)
A
array([[95, 96], [97, 98]])
np.pad(A,((3,2),(2,3)),'constant',constant_values = (0,0))
array([[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 95, 96, 0, 0, 0],
[ 0, 0, 97, 98, 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]]])
b
array([[[1, 2],
[3, 4]],
[[3, 4],
[7, 8]],
[[4, 5],
[1, 2]]])
np.pad(b, ((0,0),(1,1),(1,1)), 'constant', constant_values = 0)
array([[[0, 0, 0, 0],
[0, 1, 2, 0],
[0, 3, 4, 0],
[0, 0, 0, 0]],
[[0, 0, 0, 0],
[0, 3, 4, 0],
[0, 7, 8, 0],
[0, 0, 0, 0]],
[[0, 0, 0, 0],
[0, 4, 5, 0],
[0, 1, 2, 0],
[0, 0, 0, 0]]])
import numpy as np
c = np.array([[1,2],[3,4]])
c
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)
XX
array([[1, 3, 5],
[1, 3, 5]])
YY
array([[4, 4, 4],
[6, 6, 6]])
import numpy as np
x = np.array([[3,4,5],[1,3,4]])
y = np.array([[1,1,1],[2,2,2]])
np.hstack((x,y)) # 水平堆叠
array([[3, 4, 5, 1, 1, 1],
[1, 3, 4, 2, 2, 2]])
np.vstack((x,y)) # 竖直堆叠
array([[3, 4, 5],
[1, 3, 4],
[1, 1, 1],
[2, 2, 2]])
import numpy as np
a = np.array([0.125,0.568,5.688])
np.round(a) # 四舍五入取整， np.around 和 round 用法一致
array([0., 1., 6.])
np.round(a,decimals = 2) # 四舍五入保留2位小数
array([0.12, 0.57, 5.69])
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]])
b
array([[3, 4, 5, 6],
[1, 2, 3, 4],
[4, 5, 5, 5]])
b1 = b.reshape((1,3,4)).transpose((1,0,2))
b1
array([[[1, 2, 3, 4]],
[[3, 4, 7, 8]],
[[4, 5, 1, 2]]])
a1
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]]])
c
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])
import numpy as np
a = np.array([2,2,3,4,5,5,6,7])
a[0::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])
