NumPy_기타

Numpy¶

기타 기능들¶

• a.T, np.transpose()
• np.dot(), np.matmul(), @
• np.meshgrid()
• np.any(), np.all()
• np.unique()
• np.ravel(), 차원증가, np.squeeze(), np.expand_dims(), np.newaxis
• np.r[], np.c[]
• np.vectorize()

In [2]:

import numpy as np
import matplotlib.pyplot as plt

In [3]:

a = np.array([[1,2],[3,4]])

In [4]:

a.T

Out[4]:

array([[1, 3],
       [2, 4]])

In [5]:

a = [1,2,1,2,3,1,2,3,4]
np.unique(a)

Out[5]:

array([1, 2, 3, 4])

In [6]:

a = ['사랑해', '너를', '사랑해', '나도']
np.unique(a)

Out[6]:

array(['나도', '너를', '사랑해'], dtype='<U3')

In [7]:

a = [[0,1], [2,3]]
np.any(a,axis=0)

Out[7]:

array([ True,  True])

In [8]:

np.c_[[1,2,3],[4,5,6]]

Out[8]:

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

In [9]:

np.r_[[1,2,3],[4,5,6]]

Out[9]:

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

In [10]:

np.c_[[[1,2,3],[4,5,6]], [7,8]]

Out[10]:

array([[1, 2, 3, 7],
       [4, 5, 6, 8]])

In [11]:

np.r_[[[1,2,3],[4,5,6]], [[7,8,9]]]

Out[11]:

array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

In [19]:

np.vstack([[[1,2,3],[4,5,6]], [7,8,9]])

Out[19]:

array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

In [6]:

import numpy as np
a = np.arange(12).reshape(4,3)
a[:,np.newaxis,:]

Out[6]:

array([[[ 0,  1,  2]],

       [[ 3,  4,  5]],

       [[ 6,  7,  8]],

       [[ 9, 10, 11]]])

In [7]:

import numpy as np
a = np.arange(12).reshape(4,3)
a[:,np.newaxis,:].shape

Out[7]:

(4, 1, 3)

In [8]:

a.reshape(2,2,3)

Out[8]:

array([[[ 0,  1,  2],
        [ 3,  4,  5]],

       [[ 6,  7,  8],
        [ 9, 10, 11]]])

In [22]:

a = np.array([[1,2],[3,4]])
a

Out[22]:

array([[1, 2],
       [3, 4]])

In [23]:

a.ravel()

Out[23]:

array([1, 2, 3, 4])

In [24]:

a = np.array([1,2,3,4])
a

Out[24]:

array([1, 2, 3, 4])

In [25]:

a.reshape(2,2)

Out[25]:

array([[1, 2],
       [3, 4]])

In [26]:

np.arange(12).reshape(4,3)

Out[26]:

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

In [27]:

a

Out[27]:

array([1, 2, 3, 4])

In [28]:

b = np.arange(12).reshape(4,3)
b

Out[28]:

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

In [29]:

b.reshape(3,4)

Out[29]:

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

In [30]:

b.reshape(2,2,3)

Out[30]:

array([[[ 0,  1,  2],
        [ 3,  4,  5]],

       [[ 6,  7,  8],
        [ 9, 10, 11]]])

In [31]:

b.reshape(1,4,3)

Out[31]:

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

In [32]:

b.reshape(4,3,1)

Out[32]:

array([[[ 0],
        [ 1],
        [ 2]],

       [[ 3],
        [ 4],
        [ 5]],

       [[ 6],
        [ 7],
        [ 8]],

       [[ 9],
        [10],
        [11]]])

In [33]:

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

Out[33]:

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

In [34]:

b = np.array([10,20,30])

In [35]:

a + b.reshape(3,1)

Out[35]:

array([[11, 12],
       [23, 24],
       [35, 36]])

In [36]:

a

Out[36]:

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

In [37]:

b = a.reshape(1,1,1,2,3)
b

Out[37]:

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

In [38]:

b.squeeze()

Out[38]:

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

In [39]:

b.shape

Out[39]:

(1, 1, 1, 2, 3)

In [40]:

a

Out[40]:

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

In [41]:

np.sin(a)

Out[41]:

array([[ 0.84147098,  0.90929743],
       [ 0.14112001, -0.7568025 ],
       [-0.95892427, -0.2794155 ]])

In [42]:

def myf(n):
    if n>0:
        return 1
    else:
        return -1
    
myf(2)

Out[42]:

1

In [43]:

np.vectorize(myf)(a)

Out[43]:

array([[1, 1],
       [1, 1],
       [1, 1]])