Python教程
python中numpy的使用 学习过程
本文主要是介绍python中numpy的使用 学习过程，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！
import numpy as np data = np.array([1,2,3]) print(data)
[1 2 3]
data.max()
3
np.ones(3)
array([1., 1., 1.])
np.zeros(3)
array([0., 0., 0.])
np.random.random(3)
array([0.57604932, 0.97820094, 0.34434803])
data1 = np.array([1,2]) ones = np.ones(2) data1 + ones
array([2., 3.])
data1 - ones
array([0., 1.])
data1 * data1
array([1, 4])
data1 / data1
array([1., 1.])
data1 * 1.6
array([1.6, 3.2])
data[0]
1
data[1]
2
data[0:2]
array([1, 2])
data[1:]
array([2, 3])
data.max()
3
data.min()
1
data.sum()
6
np.ones((3,2))
array([[1., 1.],
[1., 1.],
[1., 1.]])
np.zeros((3,2))
array([[0., 0.],
[0., 0.],
[0., 0.]])
np.random.random((3,2))
array([[0.98142482, 0.16513566],
[0.33439541, 0.00498449],
[0.76431149, 0.50134611]])
np.ones(2) data = ([1,2],[3,4],[5,6]) data + ones
array([[2., 3.],
[4., 5.],
[6., 7.]])
data = ([1,2,3]) powers_of_ten = ([1,10],[100,1000],[10000,100000]) np.dot(data,powers_of_ten)
array([ 30201, 302010])
data = np.array([[1,2],[3,4],[5,6]]) data[0,1]
2
data[1:3]
array([[3, 4],
[5, 6]])
data[0:2,0]
array([1, 3])
data.max(axis=0)
array([5, 6])
data.max(axis = 1)
array([2, 4, 6])
data.T
array([[1, 3, 5],
[2, 4, 6]])
data.reshape(2,3)
array([[1, 2, 3],
[4, 5, 6]])
data.reshape(3,2)
array([[1, 2],
[3, 4],
[5, 6]])
data2 = np.array([[[1,2],[3,4],[5,6],[7,8]]]) data2
array([[[1, 2],
[3, 4],
[5, 6],
[7, 8]]])
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 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 = ([11,12,13],[14,15,16],[17,18,19]) b = ([21,22,23],[24,25,26],[27,28,29]) e = np.dstack((a,b)) e
array([[[11, 21],
[12, 22],
[13, 23]],
[[14, 24],
[15, 25],
[16, 26]],
[[17, 27],
[18, 28],
[19, 29]]])
np.dsplit(e,2)
[array([[[11],
[12],
[13]],
[[14],
[15],
[16]],
[[17],
[18],
[19]]]),
array([[[21],
[22],
[23]],
[[24],
[25],
[26]],
[[27],
[28],
[29]]])]
inistate = np.array([1,2,3,4]) pre_inistate = inistate[0:3] pre_inistate
array([1, 2, 3])
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([5,5]) W
array([[1, 5, 1],
[2, 5, 2]])
matrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]] matrix
[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
p1 = np.delete(matrix,1,0) p1
array([[ 1, 2, 3, 4],
[ 9, 10, 11, 12]])
p2 = np.delete(matrix,1,1) p2
array([[ 1, 3, 4],
[ 5, 7, 8],
[ 9, 11, 12]])
p3 = np.delete(matrix,1) p3
array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
p4 = np.delete(matrix,[0,1],1) p4
array([[ 3, 4],
[ 7, 8],
[11, 12]])
matrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]] matrix
[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
q1 = np.insert(matrix,1,[1,1,1,1],0) q1
array([[ 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) q2
array([[ 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) q3
array([[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12],
[ 1, 1, 1, 1]])
matrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]] m1 = np.append(matrix,[[1,1,1,1]],0) m1
array([[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12],
[ 1, 1, 1, 1]])
m2 = np.append(matrix,[[1],[1],[1]],1) m2
array([[ 1, 2, 3, 4, 1],
[ 5, 6, 7, 8, 1],
[ 9, 10, 11, 12, 1]])
m3 = np.append(matrix,[1,1,1,1]) m3
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 1, 1, 1])
a1 = np.random.choice(7,5) a1
array([0, 4, 4, 6, 1])
a2 = np.random.choice([0,1,2,3,4,5,6],5) a2
array([3, 3, 5, 6, 3])
a3 = np.random.choice(np.array([0,1,2,3,4,5,6]),5) a3
array([4, 2, 1, 3, 2])
a4 = np.random.choice([0,1,2,3,4,5,6],5,False) a4
array([5, 0, 1, 4, 2])
a5 = np.random.choice(np.array([0,1,2,3,4,5,6]),5,p=[0.1,0.1,0.1,0.1,0.2,0.1,0.3]) a5
array([1, 4, 4, 5, 6])
a = np.array([[1,1,1],[2,2,2],[0,3,6]]) a
array([[1, 1, 1],
[2, 2, 2],
[0, 3, 6]])
b1 = np.argmax(a) b1
8
b2 = np.argmax(a,0) b2
array([1, 2, 2], dtype=int64)
b3 = np.argmax(a,1) b3
array([0, 0, 2], dtype=int64)
y1 = np.linspace(-10.0,10.0) y1
array([-10. , -9.59183673, -9.18367347, -8.7755102 ,
-8.36734694, -7.95918367, -7.55102041, -7.14285714,
-6.73469388, -6.32653061, -5.91836735, -5.51020408,
-5.10204082, -4.69387755, -4.28571429, -3.87755102,
-3.46938776, -3.06122449, -2.65306122, -2.24489796,
-1.83673469, -1.42857143, -1.02040816, -0.6122449 ,
-0.20408163, 0.20408163, 0.6122449 , 1.02040816,
1.42857143, 1.83673469, 2.24489796, 2.65306122,
3.06122449, 3.46938776, 3.87755102, 4.28571429,
4.69387755, 5.10204082, 5.51020408, 5.91836735,
6.32653061, 6.73469388, 7.14285714, 7.55102041,
7.95918367, 8.36734694, 8.7755102 , 9.18367347,
9.59183673, 10. ])
y2 = np.linspace(1,10,10) y2
array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
y3 = np.linspace(1,10,10,False) 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,True) y4
array([ 1. , 2.8, 4.6, 6.4, 8.2, 10. ])
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()[1] = 20 x
array([[1, 2, 3],
[4, 5, 6],
[1, 2, 3]])
x.ravel()[2] = 20 x
array([[ 1, 2, 20],
[ 4, 5, 6],
[ 1, 2, 3]])
x.reshape(1,-1)
array([[ 1, 2, 20, 4, 5, 6, 1, 2, 3]])
x = np.array([1,2,3,4,5,6,7,8]) x[None,:]
array([[1, 2, 3, 4, 5, 6, 7, 8]])
x[:,None]
array([[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8]])
x[np.newaxis,:]
array([[1, 2, 3, 4, 5, 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])
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) y1
array([[1, 2, 3],
[0, 2, 4],
[5, 0, 9]])
y2 = np.minimum(0,x) 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 x1
array([[1, 2, 3],
[0, 2, 4],
[5, 0, 9]])
x2 = x.copy() x2[x2 > 0] = 0 x2
array([[ 0, 0, 0],
[-3, 0, 0],
[ 0, -2, 0]])
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() x1[x1>0] = 0 x1
array([[ 0, 0, 0],
[-3, 0, 0],
[ 0, -2, 0]])
x
array([[ 1, 2, 3],
[-3, 2, 4],
[ 5, -2, 9]])
x2 = x 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
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] x3
array([ 5, -2, 9])
x3[2]=100 x
array([[ 1, 2, 3],
[ -3, 2, 4],
[ 5, -2, 100]])
x = np.array([[1,2,3],[4,5,6]]) np.zeros_like(x)
array([[0, 0, 0],
[0, 0, 0]])
np.random.random()
0.24108840798663544
np.random.uniform()
0.28920921137490374
np.random.rand(3,4)
array([[0.76535944, 0.0267839 , 0.53517298, 0.6172231 ],
[0.71641417, 0.21781393, 0.6337269 , 0.81380947],
[0.95943125, 0.84188697, 0.73926115, 0.98160599]])
np.random.randn(2,3)
array([[ 0.3331236 , 0.59506845, 0.76552428],
[-1.73011677, 0.85722459, 2.09085071]])
y = np.multiply(0.1,np.random.randn(2,3))+0.5 y
array([[0.550347 , 0.21607968, 0.68245393],
[0.6324042 , 0.3286062 , 0.61420423]])
np.random.randint(2,9,(2,3))
array([[6, 5, 4],
[4, 6, 6]])
np.random.randint(9,size = (2,3))
array([[3, 4, 7],
[3, 6, 5]])
x = 'You are right' type(x)
str
assert type(x) == str,'x is not str' x = [1,2,3] type(x)
list
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]]])
x = np.empty([3,2],dtype = int) x
array([[0, 0],
[1, 1],
[1, 1]])
a = np.array([[1,2,3],[1,2,3]]) b = np.array([[1,2,3],[1,2,3]]) c = np.array([[1,4,3],[1,2,3]]) (a==b).all()
True
(a==c).all()
False
(a==c).any()
True
c = np.array([[1,2],[3,4]]) c
array([[1, 2],
[3, 4]])
c.astype(np.float32)
array([[1., 2.],
[3., 4.]], dtype=float32)
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]])
a = np.array([0.125,0.568,5.688]) np.round(a)
array([0., 1., 6.])
np.round(a,decimals=2)
array([0.12, 0.57, 5.69])
np.floor(a)
array([0., 0., 5.])
np.ceil(a)
array([1., 1., 6.])
c = np.array([1,2,5,4]) c[:,np.newaxis]
array([[1],
[2],
[5],
[4]])
c[np.newaxis,:]
array([[1, 2, 5, 4]])
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([[[3, 4, 5, 6]],
[[1, 2, 3, 4]],
[[4, 5, 5, 5]]])
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.transpose(1,0,2)
array([[[1, 2, 5],
[4, 5, 6]],
[[3, 4, 6],
[7, 8, 9]]])
a = np.array([2,2,3,4,5,5,6,7]) a[0:7:2]
array([2, 3, 5, 6])
a = np.array([2,2,3,4,5,5,6,7]) a[0::2]
array([2, 3, 5, 6])
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