numpy 的下载
! conda install numpy
Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done
# All requested packages already installed.
! conda install pandas
numpy 基本属性
- 创建 numpy
- 维度、形状、大小
import numpy as np
# create the numpy array
array = np.array([
[1, 2, 3],
[4, 5, 6]
])
print(array)
# 查看他的 dim, shape, size
print('number of dimentsion : ', array.ndim)
print('shape of array : ', array.shape)
print('size : ', array.size)
[[1 2 3]
[4 5 6]]
number of dimentsion : 2
shape of array : (2, 3)
size : 6
import numpy as np
X = np.arange(20).reshape(4, 5)
X.shape, X.size
((4, 5), 20)
numpy 之创建 array
- 注意类型 dtype
- 区分 numpy 和 torch 的区别
- 32 和 64 是在精度和速度之间取舍
一维 array
import numpy as np
a = np.array([2, 23, 4], dtype=np.float32)
print(a)
print(a.dtype)
[ 2. 23. 4.]
float32
import torch
a = torch.tensor([2, 23, 4], dtype=torch.float32)
print(a)
print(a.dtype)
tensor([ 2., 23., 4.])
torch.float32
多维 array
a = np.array([
[1, 2, 3],
[4, 5, 6]
])
print(a)
[[1 2 3]
[4 5 6]]
使用 np.zeros(), np.ones(), np.randn(), np.empty(), np.arange(),np.linspace()
np.random.random()
print(np.zeros((3, 3), dtype=np.int32))
print(np.ones((3, 4, 5)))
print(np.empty((3, 4)))
print(np.empty((3, 4)).dtype)
[[0 0 0]
[0 0 0]
[0 0 0]]
[[[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.]]]
[[1.22847503e-311 2.47032823e-322 0.00000000e+000 0.00000000e+000]
[1.22651357e-311 1.31370903e-076 6.81858644e-091 8.75703250e+169]
[1.52513173e-052 1.10983758e-047 3.99910963e+252 6.17134893e-062]]
float64
x = np.arange(12, dtype=np.float32)
X = x.reshape((3, 4)) # 没有 , dtype=np.int32
print(id(x) == id(X))
print(x)
print(X)
False
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.]
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]]
x = np.linspace(0, 10, 100).reshape(5, 20)
print(x)
print(x.dtype)
[[ 0. 0.1010101 0.2020202 0.3030303 0.4040404 0.50505051
0.60606061 0.70707071 0.80808081 0.90909091 1.01010101 1.11111111
1.21212121 1.31313131 1.41414141 1.51515152 1.61616162 1.71717172
1.81818182 1.91919192]
[ 2.02020202 2.12121212 2.22222222 2.32323232 2.42424242 2.52525253
2.62626263 2.72727273 2.82828283 2.92929293 3.03030303 3.13131313
3.23232323 3.33333333 3.43434343 3.53535354 3.63636364 3.73737374
3.83838384 3.93939394]
[ 4.04040404 4.14141414 4.24242424 4.34343434 4.44444444 4.54545455
4.64646465 4.74747475 4.84848485 4.94949495 5.05050505 5.15151515
5.25252525 5.35353535 5.45454545 5.55555556 5.65656566 5.75757576
5.85858586 5.95959596]
[ 6.06060606 6.16161616 6.26262626 6.36363636 6.46464646 6.56565657
6.66666667 6.76767677 6.86868687 6.96969697 7.07070707 7.17171717
7.27272727 7.37373737 7.47474747 7.57575758 7.67676768 7.77777778
7.87878788 7.97979798]
[ 8.08080808 8.18181818 8.28282828 8.38383838 8.48484848 8.58585859
8.68686869 8.78787879 8.88888889 8.98989899 9.09090909 9.19191919
9.29292929 9.39393939 9.49494949 9.5959596 9.6969697 9.7979798
9.8989899 10. ]]
float64
numpy 的基础运算形式1
- 加减乘除四则运算
- 次方，np.log(), np.exp(), np.sin(), np.tan(), np.dot() 矩阵运算
- np.dot(X, Y) = X.dot(Y)
- np.min(), np.max(), np.sum()，或者是 a.min()，具有axis相当于torch里面的dim
import numpy as np
a = np.array([10, 20, 30, 40])
b = np.arange(1, 5)
print(a)
print(b)
print(a + b)
print(a - b)
print(a * b)
print(a / b)
print(a ** b)
print(np.log(a))
print(np.sin(a))
print(np.exp(a))
[10 20 30 40]
[1 2 3 4]
[11 22 33 44]
[ 9 18 27 36]
[ 10 40 90 160]
[10. 10. 10. 10.]
[ 10 400 27000 2560000]
[2.30258509 2.99573227 3.40119738 3.68887945]
[-0.54402111 0.91294525 -0.98803162 0.74511316]
[2.20264658e+04 4.85165195e+08 1.06864746e+13 2.35385267e+17]
print(np.sin(180))
# print(np.sin(pi))
-0.8011526357338304
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_3544/373536527.py in <module>
1 print(np.sin(180))
----> 2 print(np.sin(pi))
NameError: name 'pi' is not defined
print(b < 3)
print(b == 3)
print(b.sum())
[ True True False False]
[False False True False]
10
X = np.array([
[1, 2],
[2, 5]
])
Y = np.arange(1, 5).reshape((2, 2))
print(X)
print(Y)
print(X * Y)
print(np.dot(X, Y))
print(X.dot(Y))
[[1 2]
[2 5]]
[[1 2]
[3 4]]
[[ 1 4]
[ 6 20]]
[[ 7 10]
[17 24]]
[[ 7 10]
[17 24]]
a = np.random.random((4, 6))
print(a)
print(np.sum(a), a.sum())
print(np.max(a), a.max())
print(np.min(a), a.min())
print(np.sum(a, axis=0), a.sum(axis=0))
print(np.max(a, axis=0), a.max(axis=0))
print(np.min(a, axis=0), a.min(axis=0))
print(np.sum(a, axis=1), a.sum(axis=1))
print(np.max(a, axis=1), a.max(axis=1))
print(np.min(a, axis=1), a.min(axis=1))
[[0.23205598 0.45702942 0.7222314 0.44218721 0.81785802 0.23866066]
[0.58904727 0.97667908 0.92215784 0.53217002 0.08417314 0.15867867]
[0.08002107 0.15370827 0.91751546 0.25644443 0.19251754 0.60799192]
[0.54797931 0.51718364 0.09511536 0.05141212 0.26126018 0.21689572]]
10.070973737257972 10.070973737257972
0.9766790781521906 0.9766790781521906
0.05141211685368341 0.05141211685368341
[1.44910363 2.10460041 2.65702006 1.28221378 1.35580888 1.22222698] [1.44910363 2.10460041 2.65702006 1.28221378 1.35580888 1.22222698]
[0.58904727 0.97667908 0.92215784 0.53217002 0.81785802 0.60799192] [0.58904727 0.97667908 0.92215784 0.53217002 0.81785802 0.60799192]
[0.08002107 0.15370827 0.09511536 0.05141212 0.08417314 0.15867867] [0.08002107 0.15370827 0.09511536 0.05141212 0.08417314 0.15867867]
[2.9100227 3.26290602 2.20819869 1.68984633] [2.9100227 3.26290602 2.20819869 1.68984633]
[0.81785802 0.97667908 0.91751546 0.54797931] [0.81785802 0.97667908 0.91751546 0.54797931]
[0.23205598 0.08417314 0.08002107 0.05141212] [0.23205598 0.08417314 0.08002107 0.05141212]
import numpy as np
X = np.arange(12).reshape((3, 4))
print(X.sum(axis=0))
[12 15 18 21]
numpy 的基础运算2
- np.argmax(A),A.argmax(),axis : Returns the indices of the maximum values along an axis.
- np.argmin()
- np.median()
- np.average()
- np.cumsum(A) cumulation_sum 累加
- np.diff(A) 注意原本 3 X 4 --> 3 X 3
- np.nonzero(A) 非零的位置,返回元组的大小和维度一样
- np.sort(A, axis=-1) 注意参数,axis=-1表示按照最后一个维度，axis=None 表示先进行fatten在进行sort
- np.transpose(A) = A.T 转置
- np.clip(A, 5, 9) 相当于 5, 9作为上下两个阈值
import numpy as np
A = np.arange(1, 25).reshape(2, 3, 4)
print(A)
print('\n\n')
print(np.argmin(A, axis=0))
print('\n\n')
print(np.argmax(A, axis=0))
print('\n\n')
print(A.argmax(axis=0))
print('\n\n')
print(np.mean(A))
print('\n\n')
print(np.max(A))
print('\n\n')
print('\n\n')
[[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
[[13 14 15 16]
[17 18 19 20]
[21 22 23 24]]]
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]
[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]
[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]
12.5
24
print(A.argmax())
print(A.argmin())
print(A.sum())
print(np.median(A))
print(np.average(A))
print(np.cumsum(A))
print(np.diff(A))
print(np.sort(A))
23
0
300
12.5
12.5
[ 1 3 6 10 15 21 28 36 45 55 66 78 91 105 120 136 153 171
190 210 231 253 276 300]
[[[1 1 1]
[1 1 1]
[1 1 1]]
[[1 1 1]
[1 1 1]
[1 1 1]]]
[[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
[[13 14 15 16]
[17 18 19 20]
[21 22 23 24]]]
A = np.arange(0, 18).reshape(2, 3, 3)
print(A)
print(np.nonzero(A))
[[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]]
[[ 9 10 11]
[12 13 14]
[15 16 17]]]
(array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64), array([0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 1, 1, 1, 2, 2, 2], dtype=int64), array([1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2], dtype=int64))
A = np.random.random((2, 3, 4))
print(A)
print('\n\n')
print(np.sort(A))
print('\n\n')
print(np.sort(A, axis=None))
[[[0.99333036 0.19783928 0.31423449 0.699383 ]
[0.41002294 0.67969562 0.92446068 0.3547926 ]
[0.85395562 0.59284106 0.68725208 0.6029742 ]]
[[0.03314733 0.13441676 0.29763166 0.36992482]
[0.51003511 0.13022063 0.21054679 0.97082345]
[0.89280455 0.1308477 0.04494078 0.80925873]]]
[[[0.19783928 0.31423449 0.699383 0.99333036]
[0.3547926 0.41002294 0.67969562 0.92446068]
[0.59284106 0.6029742 0.68725208 0.85395562]]
[[0.03314733 0.13441676 0.29763166 0.36992482]
[0.13022063 0.21054679 0.51003511 0.97082345]
[0.04494078 0.1308477 0.80925873 0.89280455]]]
[0.03314733 0.04494078 0.13022063 0.1308477 0.13441676 0.19783928
0.21054679 0.29763166 0.31423449 0.3547926 0.36992482 0.41002294
0.51003511 0.59284106 0.6029742 0.67969562 0.68725208 0.699383
0.80925873 0.85395562 0.89280455 0.92446068 0.97082345 0.99333036]
- np.sort(A, axis=-1) 注意参数,axis=-1表示按照最后一个维度，axis=None 表示先进行fatten在进行sort
- np.transpose(A) = A.T 转置
- np.clip(A, 5, 9) 相当于 5, 9作为上下两个阈值
A = np.arange(18).reshape((2, 3, 3))
print(A)
print('\n\n')
print(A.clip(4, 12))
[[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]]
[[ 9 10 11]
[12 13 14]
[15 16 17]]]
[[[ 4 4 4]
[ 4 4 5]
[ 6 7 8]]
[[ 9 10 11]
[12 12 12]
[12 12 12]]]
numpy 的索引
- A[1,2] == A[1][2]
- :, 0:3, ::2
- 进行 for 循环
- A.flatten() 这是列表
- A.flat 这是迭代器
import numpy as np
A = np.arange(3, 15)
print(A)
print(A[3])
[ 3 4 5 6 7 8 9 10 11 12 13 14]
6
A = np.arange(3, 15).reshape(3, 4)
print(A[2])
print(A[2,:])
print(A[:,1])
print(A[0, 0])
print(A[::2,:])
print(A[::2][:])
[11 12 13 14]
[11 12 13 14]
[ 4 8 12]
3
[[ 3 4 5 6]
[11 12 13 14]]
[[ 3 4 5 6]
[11 12 13 14]]
for row in A:
print(row)
[3 4 5 6]
[ 7 8 9 10]
[11 12 13 14]
for col in A.T:
print(col)
[ 3 7 11]
[ 4 8 12]
[ 5 9 13]
[ 6 10 14]
A = np.arange(3, 15).reshape((3, 4))
print(A)
print(A.flatten())
for item in A.flatten():
print(item, end=' ')
print('\n')
for item in A.flat:
print(item, end=' ')
[[ 3 4 5 6]
[ 7 8 9 10]
[11 12 13 14]]
[ 3 4 5 6 7 8 9 10 11 12 13 14]
3 4 5 6 7 8 9 10 11 12 13 14
3 4 5 6 7 8 9 10 11 12 13 14
numpy array 的合并
- np.vstack() vertical
- np.hstack() horizontal
- 向量转置、矩阵转置的不同性,向量是没有办法进行transpose的
- A = A[:,np.newaxis]，这个维度的变化非常有意思！
- np.concatenate可以进行多个合并，并且指定维度，axis，其实就是 shape 是改的哪个地方
import numpy as np
# A B is a vector
A = np.array([1, 1, 1])
B = np.array([2, 2, 2])
C = np.vstack((A, B)) # vertical stack
D = np.hstack((A, B)) # horizontal stack
print(A.shape, B.shape)
print(C.shape)
print(C)
print(D.shape)
print(D)
(3,) (3,)
(2, 3)
[[1 1 1]
[2 2 2]]
(6,)
[1 1 1 2 2 2]
import numpy as np
# A, B is a matrix
A = np.array([1, 1, 1]).reshape(1, 3)
B = np.array([2, 2, 2]).reshape(1, 3)
C = np.vstack((A, B)) # vertical stack
D = np.hstack((A, B)) # horizontal stack
print(A.shape, B.shape)
print(C.shape)
print(C)
print(D.shape)
print(D)
(1, 3) (1, 3)
(2, 3)
[[1 1 1]
[2 2 2]]
(1, 6)
[[1 1 1 2 2 2]]
import numpy as np
A = np.array([1, 1, 1])
B = np.array([2, 2, 2])
# turn vector to matrix
A = A[:, np.newaxis]
B = B[:, np.newaxis]
C = np.concatenate((A, B), axis=0)
D = np.concatenate((A, B), axis=1)
print(A)
print(B)
print(C)
print(D)
[[1]
[1]
[1]]
[[2]
[2]
[2]]
[[1]
[1]
[1]
[2]
[2]
[2]]
[[1 2]
[1 2]
[1 2]]
import numpy as np
A = np.array([1, 1, 1])
B = np.array([2, 2, 2])
# turn vector to matrix
A = A[:, np.newaxis]
B = B[:, np.newaxis]
print(A)
print(B)
C = A[np.newaxis, :]
D = B[np.newaxis, :]
print(C)
print(D)
E = A[:, np.newaxis]
F = B[:, np.newaxis]
print(E)
print(F)
[[1]
[1]
[1]]
[[2]
[2]
[2]]
[[[1]
[1]
[1]]]
[[[2]
[2]
[2]]]
[[[1]]
[[1]]
[[1]]]
[[[2]]
[[2]]
[[2]]]
numpy array 分割
- np.split(A, num, axis) 只能进行等量的分割)
- np.array_split(A, num, axis) 可以尽心不等量的分割，前面的会多一些
- np.vsplit() 就是不需要 axis 参数了
- np.hsplit() 就是不需要 axis 参数了
import numpy as np
A = np.arange(12).reshape(3, 4)
print(np.split(A, 3, axis=0))
print(np.split(A, 2, axis=1))
# print(np.split(A, 3, axis=1))，np.split() 必须要均分
print(np.array_split(A, 3, axis=1))
A = np.arange(15).reshape(3, -1)
print(np.array_split(A, 3, axis=1))
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2, 3],
[ 6, 7],
[10, 11]])]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2],
[ 6],
[10]]), array([[ 3],
[ 7],
[11]])]
[array([[ 0, 1],
[ 5, 6],
[10, 11]]), array([[ 2, 3],
[ 7, 8],
[12, 13]]), array([[ 4],
[ 9],
[14]])]
import numpy as np
A = np.arange(12).reshape(3, 4)
print(np.vsplit(A, 3))
print(np.hsplit(A, 2))
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2, 3],
[ 6, 7],
[10, 11]])]
numpy array copy
- b = a.copy()，仅仅是获取数值，deep copy，指向的内存单元不同了
import numpy as np
a = np.arange(4)
# print(a)
b = a
c = a
d = b
a[0] = 11
print(b)
print(c)
print(id(b) == id(c))
[11 1 2 3]
[11 1 2 3]
True
d[0:3] = 2
a, b, c, d
(array([2, 2, 2, 3]),
array([2, 2, 2, 3]),
array([2, 2, 2, 3]),
array([2, 2, 2, 3]))