1 import numpy as np 2 3 # 創建 4 # 創建一維數組 5 a = np.array([1, 2, 3]) 6 print(a) 7 ''' 8 [1 2 3] 9 ''' 10 # 創建多維數組 11 b = np.array([(1, 2, 3), (4, 5, 6)]) 12 print(b) 13 ''' 14 [[1 2 3] 15 [4 5 6]] 16 ''' 17 # 創建等差一維數組 18 c = np.arange(1, 5, 0.5) 19 print(c) 20 ''' 21 [1. 1.5 2. 2.5 3. 3.5 4. 4.5] 22 ''' 23 # 創建隨機數數組 24 d = np.random.random((2, 2)) 25 print(d) 26 ''' 27 [[0.65746941 0.09766114] 28 [0.15024283 0.9212932 ]] 29 ''' 30 # 創建一個確定起始點和終止點和個數的等差一維數組 31 ##包含終止點 32 e = np.linspace(1, 2, 10) 33 print(e) 34 ''' 35 [1. 1.11111111 1.22222222 1.33333333 1.44444444 1.55555556 1.66666667 1.77777778 1.88888889 2. ] 36 ''' 37 ##不包含終止點 38 f = np.linspace(1, 2, 10, endpoint=False) 39 print(f) 40 ''' 41 [1. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9] 42 ''' 43 # 創建一個全為‘1’的 數組 44 g = np.ones([2, 3]) 45 print(g) 46 ''' 47 [[1. 1. 1.] 48 [1. 1. 1.]] 49 ''' 50 # 創建一個全為‘0’的數組 51 h = np.zeros([2, 3]) 52 print(h) 53 ''' 54 [[0. 0. 0.] 55 [0. 0. 0.]] 56 ''' 57 # 創建一個全為'自定義的值'的數組 58 i = np.full((2, 3), fill_value=21) 59 print(i) 60 ''' 61 [[21 21 21] 62 [21 21 21]] 63 ''' 64 # 創建一個對角線為‘1’，其他的位置為‘0’ 65 j = np.eye(4) 66 print(j) 67 ''' 68 [[1. 0. 0. 0.] 69 [0. 1. 0. 0.] 70 [0. 0. 1. 0.] 71 [0. 0. 0. 1.]] 72 ''' 73 # 創建一個標准的正態分布 74 h = np.random.randn(50) 75 print(h) 76 ''' 77 [ 0.01250963 -0.7387912 0.34890184 0.45922031 0.69632711 1.45936167 78 -0.01958069 -0.42200162 -1.59439929 -0.38340785 -0.09423212 0.46495457 79 -1.07383807 1.26489024 1.50519718 1.21760287 -1.43837182 0.11904866 80 0.29399612 -1.66294523 1.42131044 0.13073129 0.02832415 1.57078671 81 -0.96096118 0.1636397 0.25686109 0.92687274 -0.14074038 -0.2355995 82 0.06471922 0.00188039 0.56639013 -0.12014897 -0.5348929 -0.91173276 83 1.04026246 -1.39317966 -0.42333174 -0.28924722 1.09360504 0.16879087 84 -0.4505147 0.38581222 -0.42106339 0.29927751 -0.9056031 -0.86102655 85 -0.61423026 -0.94604185] 86 ''' 87 # 創建一個自定義的正態分布 88 h = np.random.normal(loc=175, scale=0.3, size=50) 89 print(h) 90 # loc為位置參數 91 # scale為尺度參數，值越大離散程度越大 92 # size為總數據個數 93 ''' 94 [175.01002617 175.49445311 175.15833447 174.42510606 174.78144183 95 174.84035925 174.76628391 174.84687069 174.93967239 175.29902946 96 175.08438032 175.1476928 174.992446 174.87066715 175.02578143 97 175.03768609 175.20249608 174.96956083 174.62277043 175.59116051 98 175.59419255 174.74925345 175.44279974 175.07262176 174.91848554 99 174.90220037 175.19871001 175.04802743 174.71962518 175.07843723 100 174.87821195 174.88255464 175.56090823 174.44660242 175.11230508 101 174.89422801 174.63803226 175.03060753 174.84452539 174.99050179 102 174.9037525 174.90163791 175.42865325 174.76396595 174.99927621 103 175.15771656 174.72123296 175.22466598 174.72349497 174.95927315] 104 ''' 105 # 通過函數創建數組 106 k = np.fromfunction(lambda i, j: (i + 1) * (j + 1), (9, 9)) 107 print(k) 108 ''' 109 [[ 1. 2. 3. 4. 5. 6. 7. 8. 9.] 110 [ 2. 4. 6. 8. 10. 12. 14. 16. 18.] 111 [ 3. 6. 9. 12. 15. 18. 21. 24. 27.] 112 [ 4. 8. 12. 16. 20. 24. 28. 32. 36.] 113 [ 5. 10. 15. 20. 25. 30. 35. 40. 45.] 114 [ 6. 12. 18. 24. 30. 36. 42. 48. 54.] 115 [ 7. 14. 21. 28. 35. 42. 49. 56. 63.] 116 [ 8. 16. 24. 32. 40. 48. 56. 64. 72.] 117 [ 9. 18. 27. 36. 45. 54. 63. 72. 81.]] 118 '''