Numpy 中數組和矩陣的基本運算

1.建立矩陣

b = np.array([, , ])
print b


----------
c = np.array([[1, 2, 3], [1, 2, 3]])

#O:     [[1 2 3]
         [1 2 3]]

----------
b = np.arange()
print b

#O:     [                     ]


----------
b = np.arange().reshape(,)
print b

#O:     [[ 0  1  2  3]
         [ 4  5  6  7]
         [ 8  9 10 11]]


----------
b = np.eye(,)
print b
#O:     [[ 1.  0.  0.]
         [ 0.  1.  0.]
         [ 0.  0.  1.]]


----------
c = np.eye(,)
print c
#O      [[ 1.  0.  0.  0.  0.]
         [ 0.  1.  0.  0.  0.]
         [ 0.  0.  1.  0.  0.]]
           

2.矩陣次元

import numpy as np

b = np.arange().reshape(, )
print b
print b.shape


#O      [[ 0  1  2  3]
         [ 4  5  6  7]
         [ 8  9 10 11]]
        (L, L)
           

3.求和，極值

b = np.arange().reshape(, )
print b.sum()
#O      66

print b.max()
#O      11

print b.min()
#O  0

print b.mean()
#O      5.5

test1 = np.array([[, , ],
            [, , ],
            [, , ]])
#行求和
test1.sum(axis=)
# 輸出 array([30, 75, 120])

#列求和
test1.sum(axis=)
# 輸出 array([60, 75, 90])
           

4.數組乘法

a = np.array([[1, 2],
              [3, 4]])
b = np.array([[5, 6],
              [7, 8]])

#按元素相乘 elementwise
print a*b

#輸出     [[ 5 12]
         [21 32]]


#矩陣乘法
print a.dot(b)
#輸出     [[19 22]
         [43 50]]
           

5.元素運算

a = np.arange()
print a
print a**                  #square
print np.exp(a)             #power of E
print np.sqrt(a)            #root
print np.floor(np.sqrt(a))  #round

#OUT    [0 1 2 3]
        [   ]
        [                   ]
        [                       ]
        [       ]
           

6.轉置

a = np.arange().reshape(, )
b = a.T
print a
print b

#OUT     [[ 0  1  2  3]
         [ 4  5  6  7]
         [ 8  9 10 11]]

        [[ 0  4  8]
         [ 1  5  9]
         [ 2  6 10]
         [ 3  7 11]]
           

7.數組删除指定，行列

z= np.arange().reshape(, )
print z
z = np.delete(z, np.s_[:],axis = )
print z

#OUT    [[0 1]
         [2 3]
         [4 5]
         [6 7]
         [8 9]]

        [[6 7]
         [8 9]]

z = np.delete(z, np.s_[,],axis = )
print z

#OUT    [[2 3]
         [6 7]
         [8 9]]



z= np.arange().reshape(, )
print z
z = np.delete(z, np.s_[:],axis = )
print z


#OUT    [[0 1 2 3 4]
         [5 6 7 8 9]]
        [[2 3 4]
         [7 8 9]]
           

8.矩陣拼接

import numpy as np
a = [[1, 2, 3],
     [1, 2, 3]]
b = [[4,4,4],
    [4,4,4]]

c = np.row_stack((a, b))
print c
c = np.column_stack((a, b))
print c     

#OUT    [[1 2 3]
         [1 2 3]
         [4 4 4]
         [4 4 4]]

        [[1 2 3 4 4 4]
         [1 2 3 4 4 4]]