基本运算
import numpy as np
a = np.array([20,30,40,50])
b = np.arange(4)
c = a - b #减法
d = b**2 #平方
e = 10 *np.sin(a)
print(a<35)
A = np.array([[1,1],[0,1]])
B = np.array([[2,0],[3,4]])
C = A*B #元素相乘
E = np.dot(A,B) #矩阵相乘
print(C)
print(E)
import numpy as np
a = np.ones((2,3),dtype = int)
b = np.random.random((2,3))
print(a)
print(b)
b += a
print(b)
#a += b #error
# Cannot cast ufunc add output from dtype('float64') to dtype('int32') with casting rule 'same_kind'
print(a)
print(a.sum())
print(a.min())
print(a.max())
c = np.arange(12).reshape(3,4)
print(c)
d = c.sum(axis=0) #sum of each column
print(d)
e = c.min(axis=1) # min of each row
print(e)
f = c.cumsum(axis=1) #cumulative sum along each row
print(f)
输出
[[1 1 1]
[1 1 1]]
[[ 0.33554238 0.2963108 0.91907985]
[ 0.86329755 0.21385802 0.98444173]]
[[ 1.33554238 1.2963108 1.91907985]
[ 1.86329755 1.21385802 1.98444173]]
[[1 1 1]
[1 1 1]]
6
1
1
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
[12 15 18 21]
[0 4 8]
[[ 0 1 3 6]
[ 4 9 15 22]
[ 8 17 27 38]]
通用函数ufunc
import numpy as np
B = np.arange(3)
print(B)
print(np.exp(B))
print(np.sqrt(B))
C = np.array([2,1,3])
print(np.add(B,C))
索引、切片和迭代
a = np.arange(10)**3
print(a)
print(a[2])
print(a[2:5])
a[:6:2] = -1000
print(a)
print(a[: :-1])
def f(x,y):
return 10*x+y
b = np.fromfunction(f,(5,4),dtype= int)
print(b)
print(b[2,3])
print(b[0:5,1])
print(b[:,1])
print(b[1:3,:])
print(b[-1])
形状操作
import numpy as np
#floor 计算各元素的floor值，即小于等于该值的最大正数
a = np.floor(10*np.random.random((3,4)))
print(a)
print(a.shape)
# ravel flatten实现功能是一致的，将多维数组将为一维
#两者的区别在于ravel返回视图，会影响原始矩阵
#flatten 返回一份拷贝，对拷贝修改不影响原始矩阵
print(a.ravel())
print(a.flatten())
print(a.transpose())
#reshape 函数改变参数形状并返回它
#resize 函数改变数组自身
b = np.array([[7,5],[9,3],[7,2],[7,8],[6,8],[3,2]])
print(b)
print(b.reshape(3,4))
b.resize((3,2))
print(b)
stack组合不同的数组
import numpy as np
a = np.array([1,2,3])
print(a)
b = np.array([4,5,6])
print(b)
c = np.vstack((a,b))
print(c)
d = np.hstack((a,b))
print(d)
e = np.column_stack((a,b))
print(e)
输出
[1 2 3]
[4 5 6]
[[1 2 3]
[4 5 6]]
[1 2 3 4 5 6]
[[1 4]
[2 5]
[3 6]]
将数组分割split成几个小数组
f = np.array([1,2,3,4,5,6,7,8,9,10,11,12]).reshape(2,6)
print(f)
print(np.hsplit(f,3)) #沿水平方向
print(np.vsplit(f,2)) #沿垂直方向
