python numpy基本操作

import numpy as np
#     
a= np.array([1,2,3,4])
b= np.arange(4)
c1 = a-b
c2 = a*b
print(a)
print(a.ndim)
print(a.shape)
print(a.size)
print(b)
print(c1)
print(c2)
#   
c3 = a.dot(b)
print(c3)
c4 = a**2
print(c4)

import numpy as np
#  array arange      
a = np.array([[1,2,3],[0,3,-1]])
print(a)
b = np.arange(6).reshape((3,2))
print(b)
c = a.dot(b)  #   
print(c)

#    
a = np.random.random((2,3))
print(a)
print(np.sum(a))
print(np.min(a))
print(np.mean(a))
print(np.sum(a, axis=0))

#       
a= np.arange(2,14).reshape((3,4))
print(a)
print(np.argmin(a)) #        :0
print(np.argmax(a)) #        :0
print(np.mean(a)) #        
print(a.mean()) #      
print(np.average(a)) #    
print(np.median(a))  #    
print(np.cumsum(a))  #   ，      =        ，   scala  fold?
print(np.diff(a))  #     ？   

import numpy as np
a= np.arange(14,2,-1).reshape((3,4))
print(a)
print(np.sort(a))  #       ？
print(np.transpose(a))  #   
print(a.T)  #   
print(np.clip(a,5,9)) #      5-9  ，  5    5，  9    9

#      
import numpy as np

a=np.arange(3,15).reshape((3,4))
print(a)
print(a[1]) #  3 
print(a[1][2]) #  2  3   
print(a[1,2:3]) #  2 ， 2-3   ，     
print(a[1][2:3]) #                 ，             ，              
print(a[0:2][1:2]) #   a[0:2]           ，     [1:2]          
print(a[0:2,1:3]) #           。     2 ，1-2 ，      
print('-------------')
for row in a:
    print(row)
b = np.arange(12).reshape((3,4))
print(b)
print(b.flatten())