In [8]:
import numpy as np
array = np.array([[1,2,3],
[2,3,5]])
print(array)
[[1 2 3] [2 3 5]]
In [9]:
array.ndim
Out[9]:
2
In [10]:
array.shape
Out[10]:
(2, 3)
In [11]:
array.size
Out[11]:
6
In [17]:
a=np.array([1,2,3],dtype=np.int) print(a) print(a.dtype) b=np.array([1,2,3],dtype=np.float) print(b) print(b.dtype)
[1 2 3] int32 [ 1. 2. 3.] float64
In [19]:
array0 = np.zeros((3,4)) print(array0)
[[ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.]]
In [21]:
array1 = np.ones((3,4)) print(array1)
[[ 1. 1. 1. 1.] [ 1. 1. 1. 1.] [ 1. 1. 1. 1.]]
In [23]:
array2 = np.empty((3,4)) print(array2)
[[ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.]]
In [24]:
array3 = np.arange(10,20,2) print(array3)
[10 12 14 16 18]
In [26]:
array4 = np.arange(12).reshape((3,4)) print(array4)
[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]]
In [27]:
array5 = np.linspace(1,10,25) print(array5)
[ 1. 1.375 1.75 2.125 2.5 2.875 3.25 3.625 4. 4.375 4.75 5.125 5.5 5.875 6.25 6.625 7. 7.375 7.75 8.125 8.5 8.875 9.25 9.625 10. ]
In [30]:
array6 = np.linspace(1,10,12).reshape((3,4)) print(array6)
[[ 1. 1.81818182 2.63636364 3.45454545] [ 4.27272727 5.09090909 5.90909091 6.72727273] [ 7.54545455 8.36363636 9.18181818 10. ]]
In [38]:
a=np.array([10,20,30,40]) b=np.arange(4) c=a-b print(c)
[10 19 28 37]
In [34]:
c=b**3 print(c)
[ 0 1 8 27]
In [35]:
c=10*np.sin(a) print(c)
[-5.44021111 9.12945251 -9.88031624 7.4511316 ]
In [36]:
print(b) print(b<3) print(b==3)
[0 1 2 3] [ True True True False] [False False False True]
In [44]:
a=np.array([[1,2,3],[3,4,5]]) b=np.arange(6).reshape((3,2)) print(a) print(b) #c=a*b c_dot=np.dot(a,b) print(c_dot) c_dot2=a.dot(b) print(c_dot2)
[[1 2 3] [3 4 5]] [[0 1] [2 3] [4 5]] [[16 22] [28 40]] [[16 22] [28 40]]
In [50]:
a=np.random.random((2,4)) print(a) print(np.sum(a)) print(np.max(a)) print(np.min(a))
[[ 0.4601967 0.93594758 0.5499286 0.41483107] [ 0.5729537 0.04874679 0.26190708 0.5702891 ]] 3.81480060629 0.935947580711 0.0487467894088
In [52]:
a=np.random.random((2,4)) print(a) print(np.sum(a,axis=1)) #axis=1为行 print(np.max(a,axis=0)) #axis=0为列 print(np.min(a,axis=1)) #行
[[ 0.47054195 0.44146948 0.71298909 0.8230615 ] [ 0.155426 0.06085024 0.36118835 0.45072419]] [ 2.44806202 1.02818877] [ 0.47054195 0.44146948 0.71298909 0.8230615 ] [ 0.44146948 0.06085024]
In [67]:
A=np.arange(2,14).reshape((3,4)) print(A) print(np.argmin(A)) print(np.argmax(A))
[[ 2 3 4 5] [ 6 7 8 9] [10 11 12 13]] 0 11
In [63]:
print(np.mean(A)) print(A.mean()) #平均值 print(np.average(A)) #平均值 print(np.median(A)) #中位数
7.5 7.5 7.5 7.5
In [68]:
print(np.cumsum(A)) #累加
[ 2 5 9 14 20 27 35 44 54 65 77 90]
In [69]:
print(np.diff(A)) #累差
[[1 1 1] [1 1 1] [1 1 1]]
In [72]:
print(np.nonzero(A)) #
(array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int64), array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], dtype=int64))
In [74]:
A=np.arange(14,2,-1).reshape((3,4)) print(A) print(np.sort(A)) #按行排序
[[14 13 12 11] [10 9 8 7] [ 6 5 4 3]] [[11 12 13 14] [ 7 8 9 10] [ 3 4 5 6]]
In [78]:
print(A) print(A.T) #行列数交换。矩阵反向 也可以表示成transpose(A) print(A.T.dot(A)) #求矩阵相乘
[[14 13 12 11] [10 9 8 7] [ 6 5 4 3]] [[14 10 6] [13 9 5] [12 8 4] [11 7 3]] [[332 302 272 242] [302 275 248 221] [272 248 224 200] [242 221 200 179]]
In [79]:
print(A) print(np.clip(A,5,9)) #小于5大于9的都替换成5或9，其他数保留不变。
[[14 13 12 11] [10 9 8 7] [ 6 5 4 3]] [[9 9 9 9] [9 9 8 7] [6 5 5 5]]
In [81]:
print(A) print(np.mean(A,axis=1)) #行平均值 print(np.mean(A,axis=0)) #列平均值
[[14 13 12 11] [10 9 8 7] [ 6 5 4 3]] [ 12.5 8.5 4.5] [ 10. 9. 8. 7.]
In [86]:
A=np.arange(3,15).reshape((3,4)) print(A) print(A[2]) #同 A[2,：] 第3行的所有数 print(A[2][1]) #同A[2,1] print(A[:,1]) #第一列的所有数 print(A[1,1:3])
[[ 3 4 5 6] [ 7 8 9 10] [11 12 13 14]] [11 12 13 14] 12 [ 4 8 12] [8 9]
In [87]:
A=np.arange(3,15).reshape((3,4))
print(A)
for row in A:
print(row)
[[ 3 4 5 6] [ 7 8 9 10] [11 12 13 14]] [3 4 5 6] [ 7 8 9 10] [11 12 13 14]
In [89]:
A=np.arange(3,15).reshape((3,4))
print(A)
for column in A.T:
print(column)
[[ 3 4 5 6] [ 7 8 9 10] [11 12 13 14]] [ 3 7 11] [ 4 8 12] [ 5 9 13] [ 6 10 14]
In [91]:
A=np.arange(3,15).reshape((3,4))
print(A)
print(A.flatten())
for i in A.flat:
print(i)
[[ 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
In [96]:
A=np.array([1,2,3]) B=np.array([4,5,6]) c=np.vstack((A,B)) #上下合并 print(A.shape) print(c) print(c.shape)
(3,) [[1 2 3] [4 5 6]] (2, 3)
In [97]:
d=np.hstack((A,B)) #左右合并 print(d) print(d.shape)
[1 2 3 4 5 6] (6,)
In [99]:
print(A) print(A.T)
[1 2 3] [1 2 3]
In [105]:
print(A) print(A[:,np.newaxis],A[:,np.newaxis].shape)
[1 2 3] [[1] [2] [3]] (3, 1)
In [104]:
print(A,A.shape) print(A[np.newaxis:],A[np.newaxis:].shape)
[1 2 3] (3,) [1 2 3] (3,)
In [107]:
A=np.array([1,2,3])[:,np.newaxis] #以列作为维度 B=np.array([4,5,6])[:,np.newaxis] c=np.vstack((A,B)) #上下合并 d=np.hstack((A,B)) #左右合并 print(A) print(B) print(c) print(d)
[[1] [2] [3]] [[4] [5] [6]] [[1] [2] [3] [4] [5] [6]] [[1 4] [2 5] [3 6]]
In [112]:
A=np.array([1,2,3])[:,np.newaxis] #以列作为维度 B=np.array([4,5,6])[:,np.newaxis] e=np.concatenate((A,B,B,A),axis=0) #按列合并。等同vstack((A,B)) 上下合并 print(e) f=np.concatenate((A,B,B,A),axis=1) #按行合并。等同hstack((A,B)) 左右合并 print(f)
[[1] [2] [3] [4] [5] [6] [4] [5] [6] [1] [2] [3]] [[1 4 4 1] [2 5 5 2] [3 6 6 3]]
In [120]:
A=np.arange(12).reshape((3,4)) print(A) b=np.split(A,2,axis=1) #axis=1 按列来分割 print(b) c=np.split(A,3,axis=0) #axis=0 按行来分割 print(c)
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2, 3],
[ 6, 7],
[10, 11]])]
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]
In [122]:
print(A) d=np.array_split(A,3,axis=1) #不等分割 print(d)
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2],
[ 6],
[10]]), array([[ 3],
[ 7],
[11]])]
In [124]:
print(A) b=np.vsplit(A,3) #上下分割 按行分割 同 split(A,3,axis=0) print(b)
[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]
In [125]:
print(A) b=np.hsplit(A,2) #左右分割 按列分割 同 split(A,2,axis=1) print(b)
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2, 3],
[ 6, 7],
[10, 11]])]
In [130]:
a=np.array([1,2,3,4]) b=a c=a d=b print(a,b,c,d) print(b is a) a[0]=11 print(a,b,c,d) b[1:3]=[22,33] print(a,b,c,d)
[1 2 3 4] [1 2 3 4] [1 2 3 4] [1 2 3 4] True [11 2 3 4] [11 2 3 4] [11 2 3 4] [11 2 3 4] [11 22 33 4] [11 22 33 4] [11 22 33 4] [11 22 33 4]
In [137]:
a=np.array([1,2,3,4]) e=a.copy() #deep copy print(e,a) print(e is a) a[0]=55 print(e,a)
[1 2 3 4] [1 2 3 4] False [1 2 3 4] [55 2 3 4]
时间： 2024-10-29 05:53:31