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...@@ -78,4 +78,556 @@ Window의 시작 버튼을 누르고, 명령 프롬프트(CMD)를 관리자 권 ...@@ -78,4 +78,556 @@ Window의 시작 버튼을 누르고, 명령 프롬프트(CMD)를 관리자 권
# Numpy 배워보기 # Numpy 배워보기
```python
import numpy as np
import pandas as pd
print (np.__version__)
print (pd.__version__)
```
1.19.3
1.1.5
```python
#np array 만들기
#여기서부터는 array = 배열로 취급
```
```python
one = [1,2,3,4,5]
print (one)
ten = [10,20,30,40,50]
print (ten)
type (one)
```
[1, 2, 3, 4, 5]
[10, 20, 30, 40, 50]
list
```python
onearr = np.array(one)
tenarr = np.array(ten)
print (onearr)
print (tenarr)
type (onearr)
```
[1 2 3 4 5]
[10 20 30 40 50]
numpy.ndarray
```python
onearr.dtype
```
dtype('int32')
```python
onearr.shape
```
(5,)
```python
arr2nd = np.array([[1,0,0],[0,1,0],[0,0,1],[1,1,1]])
arr2nd.shape
```
(4, 3)
```python
one.dtype
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-81347d222ef3> in <module>
----> 1 one.dtype
AttributeError: 'list' object has no attribute 'dtype'
```python
sumarr = onearr + tenarr
sumlist = one + ten
print (sumarr)
print (sumlist)
```
[11 22 33 44 55]
[1, 2, 3, 4, 5, 10, 20, 30, 40, 50]
```python
#기본 배열 만드는 함수 -> 빠르게 배열 만들기
```
```python
arr1 = np.zeros(3)
arr2 = np.zeros((5, 3))
print (arr1)
print (arr2)
```
[0. 0. 0.]
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
```python
arr1 = np.ones(3)
arr2 = np.ones((5, 3))
print (arr1)
print (arr2)
```
[1. 1. 1.]
[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
```python
arr1 = np.identity(1)
arr2 = np.identity(2)
print (arr1)
print (arr2)
```
[[1.]]
[[1. 0.]
[0. 1.]]
```python
arr1 = np.arange(100)
arr2 = np.arange(96,100)
print (arr1)
print (arr2)
```
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
96 97 98 99]
[96 97 98 99]
```python
#기본적인 배열의 연산
```
```python
print (onearr)
print (tenarr)
```
[1 2 3 4 5]
[10 20 30 40 50]
```python
plus = onearr + tenarr
minus = onearr - tenarr
mul = onearr * tenarr
div = onearr / tenarr
print (plus)
print (minus)
print (mul)
print (div)
```
[11 22 33 44 55]
[ -9 -18 -27 -36 -45]
[ 10 40 90 160 250]
[0.1 0.1 0.1 0.1 0.1]
```python
arr1 = np.array([[10, 20, 30], [100, 200, 300]])
arr2 = np.array(([5, 6, 7], [5, 6, 7]))
print (arr1 + arr2)
print (arr1 - arr2)
```
[[ 15 26 37]
[105 206 307]]
[[ 5 14 23]
[ 95 194 293]]
```python
arr1 = np.array([[10, 20], [30, 40]])
arr2 = np.identity(2)
print (arr1 * arr2)
```
[[10. 0.]
[ 0. 40.]]
```python
#numpy에서의 브로드캐스트
#numpy는 브로드 캐스트를 지원합니다. 몰론 두 array가 행이나 열의 길이가 같아야 합니다.
```
```python
arr1 = np.array([[10, 20],[30, 40],[50,60]])
print (arr1)
print (arr1.shape)
```
[[10 20]
[30 40]
[50 60]]
(3, 2)
```python
arr2 = np.array([100,200])
arr3 = np.array([[100], [200], [300]])
print (arr2.shape)
print (arr3.shape)
```
(2,)
(3, 1)
```python
horizontal = arr1 + arr2
print (horizontal)
vertical = arr1 + arr3
print (vertical)
```
[[110 220]
[130 240]
[150 260]]
[[110 120]
[230 240]
[350 360]]
```python
difarr = np.array([100,200,300,400])
sumarr2 = onearr + difarr
print (sumarr2)
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-3603de6b54e0> in <module>
1 difarr = np.array([100,200,300,400])
----> 2 sumarr2 = onearr + difarr
3 print (sumarr2)
ValueError: operands could not be broadcast together with shapes (5,) (4,)
```python
#배열의 값 찾기
#파이썬, C언어에서 하는 배열 값 찾기와 비슷하다.
#1차원에서는 기본적으로 1개의 값만 입력하면 되지만, 2차원은 2개의 값을 입력한다.
```
```python
arr1 = np.arange(10)
print (arr1)
print (arr1[6])
print (arr1[0])
print (arr1[3:7])
print (arr1[:4])
print (arr1[6:])
print (arr1[:])
```
[0 1 2 3 4 5 6 7 8 9]
6
0
[3 4 5 6]
[0 1 2 3]
[6 7 8 9]
[0 1 2 3 4 5 6 7 8 9]
```python
arr2 = np.array([[1,2,3],[4,5,6],[7,8,9]])
print (arr2)
print (arr2[2,2])
print (arr2[0,0])
print (arr2[0,:])
print (arr2[0:,1:])
print (arr2[:3,:1])
```
[[1 2 3]
[4 5 6]
[7 8 9]]
9
1
[1 2 3]
[[2 3]
[5 6]
[8 9]]
[[1]
[4]
[7]]
```python
#numpy의 함수들
#진짜 많다 여기 있는게 다가 아니다
```
```python
arr1 = np.random.random(10)
arr2 = np.random.randn(10)
arr3 = np.random.rand(10)
arr4 = np.random.randint(10)
print (arr1)
print (arr2)
print (arr3)
print (arr4)
```
[0.9446711 0.83497248 0.83201387 0.13935773 0.17415421 0.49383566
0.62534832 0.95301777 0.775304 0.42802872]
[ 0.49760198 0.42642281 -0.17887658 0.97600137 -1.09890443 1.14944642
-1.61367583 0.68313963 -0.69266217 -0.20675362]
[0.45892907 0.79511622 0.23510859 0.83475971 0.61042409 0.98928205
0.04701599 0.47026134 0.10542542 0.6393744 ]
3
```python
np.random.rand(3, 10)
```
array([[0.69049139, 0.33961382, 0.4720473 , 0.05657901, 0.44823818,
0.62798771, 0.18857805, 0.58646854, 0.78296022, 0.23064811],
[0.89095585, 0.56933846, 0.66368549, 0.36591346, 0.78302718,
0.04110933, 0.23313533, 0.3403511 , 0.68102568, 0.99330884],
[0.18974105, 0.01787721, 0.24513353, 0.61273492, 0.82943479,
0.88117367, 0.39032357, 0.85654216, 0.57124692, 0.9269831 ]])
```python
arr1 = arr1 * 10
print (arr1)
print (np.ceil(arr1))
print (np.floor(arr1))
```
[9.44671104 8.34972483 8.32013866 1.39357733 1.74154213 4.9383566
6.2534832 9.53017769 7.75304001 4.28028724]
[10. 9. 9. 2. 2. 5. 7. 10. 8. 5.]
[9. 8. 8. 1. 1. 4. 6. 9. 7. 4.]
```python
arr1 = np.array([[1,2,3],[4,5,6],[7,8,9]])
print (np.sqrt(arr1))
```
[[1. 1.41421356 1.73205081]
[2. 2.23606798 2.44948974]
[2.64575131 2.82842712 3. ]]
```python
arr2 = np.array([[10, -100],[10, -1000]])
print (arr2)
print (np.abs(arr2))
```
[[ 10 -100]
[ 10 -1000]]
[[ 10 100]
[ 10 1000]]
```python
print (np.exp(arr1))
print (np.log10(arr1))
```
[[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]]
[[0. 0.30103 0.47712125]
[0.60205999 0.69897 0.77815125]
[0.84509804 0.90308999 0.95424251]]
```python
print (np.cos(arr1))
```
[[ 0.54030231 -0.41614684 -0.9899925 ]
[-0.65364362 0.28366219 0.96017029]
[ 0.75390225 -0.14550003 -0.91113026]]
```python
arr3 = np.array([[9,8,7],[6,5,4],[3,2,1]])
print (np.maximum(arr1, arr3))
print (np.minimum(arr1, arr3))
```
[[9 8 7]
[6 5 6]
[7 8 9]]
[[1 2 3]
[4 5 4]
[3 2 1]]
```python
#행렬의 정렬
```
```python
arr1 = np.random.rand(30)
arr1 = arr1 * 100
arr1 = np.floor(arr1)
print (arr1)
arr1 = arr1.astype('int32')
print (arr1)
print (arr1.dtype)
```
[31. 75. 13. 88. 97. 83. 97. 52. 42. 40. 27. 39. 95. 78. 86. 9. 72. 85.
55. 75. 8. 17. 77. 78. 55. 1. 81. 89. 62. 68.]
[31 75 13 88 97 83 97 52 42 40 27 39 95 78 86 9 72 85 55 75 8 17 77 78
55 1 81 89 62 68]
int32
```python
print (np.sort(arr1))
print (np.sort(arr1)[::-1])
```
[ 1 8 9 13 17 27 31 39 40 42 52 55 55 62 68 72 75 75 77 78 78 81 83 85
86 88 89 95 97 97]
[97 97 95 89 88 86 85 83 81 78 78 77 75 75 72 68 62 55 55 52 42 40 39 31
27 17 13 9 8 1]
```python
```
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