[Numpy] Operation_function, Concatenate

In [2]:

import numpy as np

 

In [4]:

test_array = np.arange(1, 11)
test_array

Out[4]:

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

Sum

In [6]:

test_array.sum(dtype=np.float)

Out[6]:

55.0

 

In [9]:

test_array = np.arange(1, 13).reshape(3,4)
test_array.sum()

 

Out[9]:

78

 

In [12]:

print(test_array)
test_array.sum(axis=1) #칼럼끼리 더한다

 

[[ 1  2  3  4]
 [ 5  6  7  8]
 [ 9 10 11 12]]

Out[12]:

array([10, 26, 42])

 

In [14]:

test_array.sum(axis=0) #행끼리 더한다

Out[14]:

array([15, 18, 21, 24])

 

In [17]:

third_order_tensor = np.array([test_array, test_array, test_array])
third_order_tensor

Out[17]:

array([[[ 1,  2,  3,  4],
        [ 5,  6,  7,  8],
        [ 9, 10, 11, 12]],

       [[ 1,  2,  3,  4],
        [ 5,  6,  7,  8],
        [ 9, 10, 11, 12]],

       [[ 1,  2,  3,  4],
        [ 5,  6,  7,  8],
        [ 9, 10, 11, 12]]])

 

In [19]:

third_order_tensor.sum(axis=2) #column

Out[19]:

array([[10, 26, 42],
       [10, 26, 42],
       [10, 26, 42]])

 

In [21]:

third_order_tensor.sum(axis=1) #row

Out[21]:

array([[15, 18, 21, 24],
       [15, 18, 21, 24],
       [15, 18, 21, 24]])

 

In [23]:

third_order_tensor.sum(axis=0) #level

Out[23]:

array([[ 3,  6,  9, 12],
       [15, 18, 21, 24],
       [27, 30, 33, 36]])

 

In [25]:

test_array = np.arange(1, 13).reshape(3, 4)
test_array

Out[25]:

array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])

---

Mean

In [27]:

test_array.mean(), test_array.mean(axis=0) #평균

Out[27]:

(6.5, array([5., 6., 7., 8.]))

---

Std

In [29]:

test_array.std(), test_array.std(axis=0) #표준편차

Out[29]:

(3.452052529534663, array([3.26598632, 3.26598632, 3.26598632, 3.26598632]))

---

Exp, Sqrt

In [31]:

np.exp(test_array), np.sqrt(test_array) #지수, 제곱근

Out[31]:

(array([[2.71828183e+00, 7.38905610e+00, 2.00855369e+01, 5.45981500e+01],
        [1.48413159e+02, 4.03428793e+02, 1.09663316e+03, 2.98095799e+03],
        [8.10308393e+03, 2.20264658e+04, 5.98741417e+04, 1.62754791e+05]]),
 array([[1.        , 1.41421356, 1.73205081, 2.        ],
        [2.23606798, 2.44948974, 2.64575131, 2.82842712],
        [3.        , 3.16227766, 3.31662479, 3.46410162]]))

---

vstack, hstack

In [33]:

a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
np.vstack((a, b)) #vertical 방향으로 합침

Out[33]:

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

 

In [37]:

a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
np.hstack((a,b)) #horizontal 방향으로 합침

Out[37]:

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

---

concatenate

In [39]:

a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
np.concatenate((a, b), axis=0) #1차원 array이므로 row끼리 합쳐짐

Out[39]:

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

In [43]:

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
np.concatenate((a, b.T), axis=1) #열끼리 합쳐짐

Out[43]:

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

---

tolist()

In [44]:

a.tolist()

Out[44]:

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