numpy 属性
array = np.array([[1,2,3],
[2,3,4]])
print(array)
print("number of dim:"array.ndim)
print("shape",array.shape)
print("size",array.size) # 有多少个元素
[[1 2 3]
[2 3 4]]
number of dim: 2
shape (2, 3)
size 6
创建 array
a = np.array([2,23,4])
print(a)
[ 2 23 4]
a = np.array([2,23,4],dtype=np.int64) #int float16 32 64
print(a.dtype)
int64
a = np.arange(10,20,2) #range类似
print(a)
[10 12 14 16 18]
a = np.arange(12).reshape((3,4))
print(a)
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
# 定义矩阵
a = np.array([[3,4,6],
[7,1,8]])
print(a)
[[3 4 6]
[7 1 8]]
a = np.zeros((3,4)) #ones empty
print(a)
[[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]
#生成线段
a = np.linspace(1,10,5) #5段
print(a)
[ 1. 3.25 5.5 7.75 10. ]
a = np.linspace(1,10,6).reshape((2,3))
print(a)
[[ 1. 2.8 4.6]
[ 6.4 8.2 10. ]]
numpy 基础运算
a = np.array([10,20,30,40])
b = np.arange(4)
print(a,b)
c = a - b # + 逐个相加减法
print(c)
[10 20 30 40] [0 1 2 3]
[10 19 28 37]
a = np.array([10,20,30,40])
b = np.arange(4)
print(a,b) # / 逐个相乘除法
c = a * b
print(c)
[10 20 30 40] [0 1 2 3]
[ 0 20 60 120]
b = np.arange(4)
print(b) # / 逐个相乘除法
c = b ** 2 # 平方是 **
print(c)
[0 1 2 3]
[0 1 4 9]
a = np.array([10,20,30,40])
c = 10*np.sin(a) # 每个值sin以后再*10 cos tan
print(c)
[-5.44021111 9.12945251 -9.88031624 7.4511316 ]
0.49999999999999994
b = np.arange(4)
print(b)http://localhost:8888/notebooks/LearnArray.ipynb#%E7%9F%A9%E9%98%B5%E8%BF%90%E7%AE%97
print(b<3) # == >=
[0 1 2 3]
[ True True True False]
矩阵运算
a = np.array([[1,1],
[0,1]])
b = np.arange(4).reshape((2,2))
print(a)
print(b)
print("逐个相乘")
print(a*b) #逐个相乘
print("矩阵相乘")
print(np.dot(a,b)) #矩阵相乘
print(a.dot(b))
[[1 1]
[0 1]]
[[0 1]
[2 3]]
逐个相乘
[[0 1]
[0 3]]
矩阵相乘
[[2 4]
[2 3]]
[[2 4]
[2 3]]
a = np.random.random((2,4))
print(a)
print(np.sum(a))
print(np.min(a))
print(np.max(a))
[[0.75043705 0.64915184 0.90982739 0.95481296]
[0.10239917 0.05245467 0.05292892 0.15421724]]
3.6262292489095636
0.05245467473374732
0.9548129643015092
a = np.random.random((2,4))
print(a,end="\n\n")
print(np.sum(a,axis=1)) #axis=1为行
[[0.24422292 0.75909185 0.77952272 0.45213038]
[0.14313807 0.24731068 0.14017471 0.99728501]]
[2.23496787 1.52790848]
print(np.min(a,axis=0)) #axis=0为列
[0.14313807 0.24731068 0.14017471 0.45213038]
矩阵运算2
A = np.arange(2,14).reshape((3,4))
print(A,end="\n\n")
print(np.argmin(A)) #最小值的索引
print(np.argmax(A)) #最大值的索引
[[ 2 3 4 5]
[ 6 7 8 9]
[10 11 12 13]]
0
11
print(A.mean()) #np.mean(A)
print("平均数",np.average(A)) #不能 A.average(A)
print("中位数",np.median(A))
7.5
平均数 7.5
中位数 7.5
print("逐步累加 cumsum",np.cumsum(A))
print("相邻差 diff",np.diff(A))
逐步累加 cumsum [ 2 5 9 14 20 27 35 44 54 65 77 90]
相邻差 diff [[1 1 1]
[1 1 1]
[1 1 1]]
print("非0的数",np.nonzero(A)) #先输出行数，在输出列数
非0的数 (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))
A = np.arange(14,2,-1).reshape((3,4))
print(A,end="\n\n")
print(np.sort(A)) #逐行排序
[[14 13 12 11]
[10 9 8 7]
[ 6 5 4 3]]
[[11 12 13 14]
[ 7 8 9 10]
[ 3 4 5 6]]
A = np.arange(14,2,-1).reshape((3,4))
print(A,end="\n\n")
print(np.transpose(A))
print(A.T)
print((A.T).dot(A)) #实对称
[[14 13 12 11]
[10 9 8 7]
[ 6 5 4 3]]
[[14 10 6]
[13 9 5]
[12 8 4]
[11 7 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]]
A = np.arange(14,2,-1).reshape((3,4))
print(A,end="\n\n")
print(np.clip(A,5,11)) #所有小于 5 的数变成 5，所有大于 11 的数变成 11
[[14 13 12 11]
[10 9 8 7]
[ 6 5 4 3]]
[[11 11 11 11]
[10 9 8 7]
[ 6 5 5 5]]
A = np.arange(14,2,-1).reshape((3,4))
print(A,end="\n\n")
print("计算每列平均数",np.mean(A,axis=0)) # 计算每列平均数
print("计算每行平均数",np.mean(A,axis=1)) # 计算每行平均数
[[14 13 12 11]
[10 9 8 7]
[ 6 5 4 3]]
计算每列平均数 [10. 9. 8. 7.]
计算每行平均数 [12.5 8.5 4.5]
numpy索引
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)
print(A[1][1])
print(A[2,1])
[[ 3 4 5 6]
[ 7 8 9 10]
[11 12 13 14]]
8
12
print(A[2,:])
print(A[:,1])
print(A[0,1:4])
[11 12 13 14]
[ 4 8 12]
[4 5 6
# 输出列，用T，妙啊
for col in A.T:
print(col)
[ 3 7 11]
[ 4 8 12]
[ 5 9 13]
[ 6 10 14]
# 输出矩阵中的每一个值
print(A.flatten())
for item in A.flat:
print(item)
[ 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 合并
A = np.array([1,1,1])
B = np.array([2,2,2])
C = np.vstack((A,B))
print(C)
print(A.shape,C.shape)
[[1 1 1]
[2 2 2]]
(3,) (2, 3)
D = np.hstack((A,B))
print(D)
print(A.shape,D.shape)
[1 1 1 2 2 2]
(3,) (6,)
# 变成 [[1],[1],[1]]
E = A[np.newaxis,:] # 在行上加了一个维度
print(E)
print(E.shape,end="\n\n")
F = A[:,np.newaxis]# 在列上加了一个维度
print(F)
print(F.shape)
[[1 1 1]]
(1, 3)
[[1]
[1]
[1]]
(3, 1)
A = np.array([1,1,1])[:,np.newaxis]
print(A,end="\n\n")
B = np.array([2,2,2])[:,np.newaxis]
print(B,end="\n\n")
G = np.hstack((A,B))
print(G)
[[1]
[1]
[1]]
[[2]
[2]
[2]]
[[1 2]
[1 2]
[1 2]]
C = np.concatenate((A,B,B,A),axis=0) #上下合并
print(C,end="\n\n")
C = np.concatenate((A,B,B,A),axis=1) #左右合并
print(C)
[[1]
[1]
[1]
[2]
[2]
[2]
[2]
[2]
[2]
[1]
[1]
[1]]
[[1 2 2 1]
[1 2 2 1]
[1 2 2 1]]
numpy array 分割
A = np.arange(12).reshape((3,4))
print(A)
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
print(np.split(A,2,axis=1)) #等分
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2, 3],
[ 6, 7],
[10, 11]])]
print(np.split(A,3,axis=0))
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]
print(np.array_split(A,3,axis=1)) #不等项分割
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2],
[ 6],
[10]]), array([[ 3],
[ 7],
[11]])]
