From 47a6e4a03600ea82fcb29fd90e66fae29978cb47 Mon Sep 17 00:00:00 2001
From: Young Woo Kim <skysaver00@ajou.ac.kr>
Date: Sun, 20 Dec 2020 22:01:22 +0900
Subject: [PATCH] Update README.md
---
README.md | 552 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 552 insertions(+)
diff --git a/README.md b/README.md
index f0cf9fe..613b214 100644
--- a/README.md
+++ b/README.md
@@ -78,4 +78,556 @@ Window의 시작 버튼을 누르고, 명령 프롬프트(CMD)를 관리자 권
# 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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