Python 기초09_vectorize

vectorize¶

In [1]:

import numpy as np

matrix= np.arange(1,10).reshape(3,3)
print(matrix)

[[1 2 3]
 [4 5 6]
 [7 8 9]]

In [2]:

add_100= lambda i:i+100

In [3]:

vectorized_add_100= np.vectorize(add_100)

vectorized_add_100(matrix) 

Out[3]:

array([[101, 102, 103],
       [104, 105, 106],
       [107, 108, 109]])

In [4]:

find_odd = lambda i:i%2*i
vectorized_find_odd= np.vectorize(find_odd)

vectorized_find_odd(matrix) 

Out[4]:

array([[1, 0, 3],
       [0, 5, 0],
       [7, 0, 9]])

In [5]:

find_odd = lambda i : i if i%2==1 else 0 
find_even= find_odd= lambda i : i if i%2==0 else 0

vectorized_find_odd= np.vectorize(find_odd)
vectorized_find_odd(matrix)

Out[5]:

array([[0, 2, 0],
       [4, 0, 6],
       [0, 8, 0]])

In [6]:

import numpy as np

matrix= np.arange(1,10).reshape(3,3)
print(matrix)

print(np.max(matrix))
print()
print(np.min(matrix))
print()
print(np.min(matrix, axis=1)) # 각 행에서 최소값 찾음
print()
print(np.min(matrix, axis=0)) # 각 열에서 최소값 찾음

[[1 2 3]
 [4 5 6]
 [7 8 9]]
9

1

[1 4 7]

[1 2 3]

In [7]:

vector_column= np.max(matrix, axis=1, keepdims=True) 
print(vector_column)
print(vector_column.shape)

[[3]
 [6]
 [9]]
(3, 1)

In [8]:

print(matrix.shape)
print(matrix-vector_column)

(3, 3)
[[-2 -1  0]
 [-2 -1  0]
 [-2 -1  0]]

평균, 분산, 표준편차¶

In [13]:

import numpy as np

matrix= np.arange(1,10).reshape(3,3)
print(matrix)

print("평균: ",np.mean(matrix))
print("분산: ",np.var(matrix))
print("표준편차: ",np.std(matrix))

[[1 2 3]
 [4 5 6]
 [7 8 9]]
평균:  5.0
분산:  6.666666666666667
표준편차:  2.581988897471611

In [14]:

print("평균 : ", np.mean(matrix))
print("평균 : ",np.mean(matrix,axis=0)) #각 열의 평균 

평균 :  5.0
평균 :  [4. 5. 6.]

In [15]:

print(np.std(matrix))
print(np.std(matrix, ddof=1))

2.581988897471611
2.7386127875258306

행렬의 전치(전치행렬, Transpose)¶

In [1]:

import numpy as np 
matrix= np.arange(1,10).reshape(3,3)
print(matrix)
print()
print(matrix.T)

[[1 2 3]
 [4 5 6]
 [7 8 9]]

[[1 4 7]
 [2 5 8]
 [3 6 9]]

In [23]:

np.array([[1,2,3,4,5,6,7]]).T

Out[23]:

array([[1],
       [2],
       [3],
       [4],
       [5],
       [6],
       [7]])

In [24]:

matrix=([[1,1,1], [1,1,10], [1,1,15]])
print(matrix)
np.linalg.matrix_rank(matrix) 

[[1, 1, 1], [1, 1, 10], [1, 1, 15]]

Out[24]:

2

inverse matrix¶

In [25]:

matrix=np.array([[1,4], [2,5]])
print(matrix)

[[1 4]
 [2 5]]

In [26]:

#역행렬 
inv_matrix= np.linalg.inv(matrix)
print(inv_matrix)

[[-1.66666667  1.33333333]
 [ 0.66666667 -0.33333333]]

In [27]:

matrix @ inv_matrix

Out[27]:

array([[1., 0.],
       [0., 1.]])

In [28]:

matrix= np.arange(1,7).reshape(2,3)
print(matrix)

[[1 2 3]
 [4 5 6]]

In [29]:

inv_matrix= np.linalg.pinv(matrix) #유사역행렬
print(inv_matrix)

[[-0.94444444  0.44444444]
 [-0.11111111  0.11111111]
 [ 0.72222222 -0.22222222]]

In [30]:

matrix@inv_matrix

Out[30]:

array([[1.00000000e+00, 2.22044605e-16],
       [0.00000000e+00, 1.00000000e+00]])

In [31]:

print(np.round(matrix @ inv_matrix,0))

[[1. 0.]
 [0. 1.]]

In [35]:

import random
a= np.arange(5)
print(a)
np.random.shuffle(a)
print(a)

[0 1 2 3 4]
[3 0 1 2 4]

In [38]:

import random
a= np.arange(5)
print(a) 
b= np.random.permutation(a)
print(b)

[0 1 2 3 4]
[2 4 1 0 3]

In [39]:

print(np.random.permutation(5))

[0 4 1 2 3]