numpy学习(二)

练习篇（Part 2）

11. Create a 3x3 identity matrix (★☆☆)

 arr = np.eye(3)
 print(arr)

运行结果：[[1. 0. 0.] [0. 1. 0.] [0. 0. 1.]]

12. Create a 3x3x3 array with random values (★☆☆)

 arr = np.random.random((3,3,3))
 print(arr)

运行结果：略

13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)

 arr = np.random.random((10,10))
 print('max:'+str(arr.max()))
 print('min:'+str(arr.min()))

运行结果：

max:0.9966220981691146

min:0.0034603079973672957

14. Create a random vector of size 30 and find the mean value (★☆☆)

 arr = np.random.random(30)
 print(arr.mean())

运行结果：0.49710820465862965

15. Create a 2d array with 1 on the border and 0 inside (★☆☆)

 arr = np.ones((10,10))
 arr[1:9,1:9] = 0
 print(arr)

运行结果：

[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]

16. How to add a border (filled with 0's) around an existing array? (★☆☆)

 arr = np.ones((10,10))
 arr = np.pad(arr, pad_width=1, mode='constant', constant_values=0)
 print(arr)

运行结果：

[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]

17. What is the result of the following expression? (★☆☆)

 0 * np.nan
 np.nan == np.nan
 np.inf > np.nan
 np.nan - np.nan
 np.nan in set([np.nan])
 0.3 == 3 * 0.1

 print(0 * np.nan)
 print(np.nan == np.nan)
 print(np.inf > np.nan)
 print(np.nan - np.nan)
 print(np.nan in set([np.nan]))
 print(0.3 == 3 * 0.1)

运行结果：nan False False nan True False

18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)

 arr = np.diag(1+np.arange(4),k=-1)
 print(arr)

运行结果：

[[0 0 0 0 0]
[1 0 0 0 0]
[0 2 0 0 0]
[0 0 3 0 0]
[0 0 0 4 0]]

19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)

 arr = np.zeros((8,8))
 arr[0::2,0::2]=1
 arr[1::2,1::2]=1
 print(arr)

运行结果：

[[1. 0. 1. 0. 1. 0. 1. 0.]
[0. 1. 0. 1. 0. 1. 0. 1.]
[1. 0. 1. 0. 1. 0. 1. 0.]
[0. 1. 0. 1. 0. 1. 0. 1.]
[1. 0. 1. 0. 1. 0. 1. 0.]
[0. 1. 0. 1. 0. 1. 0. 1.]
[1. 0. 1. 0. 1. 0. 1. 0.]
[0. 1. 0. 1. 0. 1. 0. 1.]]

20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?

 print(np.unravel_index(99,(6,7,8)))

运行结果：(1, 5, 3)

21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)

 arr = np.tile(np.array([[0,1],[1,0]]),(4,4))
 print(arr)

运行结果：

[[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]]

22. Normalize a 5x5 random matrix (★☆☆)

 arr = np.random.random((5,5))
 arr = (arr - np.min(arr))/(np.max(arr) - np.min(arr))
 print(arr)

运行结果：

[[0.25182827 1. 0. 0.08239415 0.28511849]
[0.10300901 0.52930264 0.95743154 0.84571053 0.50581171]
[0.82070737 0.5720979 0.91581986 0.16283325 0.27075288]
[0.91480517 0.40637193 0.7032704 0.15695137 0.79951099]
[0.18035443 0.50388197 0.48436665 0.9828424 0.02296698]]

23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)

 color = np.dtype([
     ("r", np.ubyte, 1),
     ("g", np.ubyte, 1),
     ("b", np.ubyte, 1),
     ("a", np.ubyte, 1)])
 print(color)

运行结果：[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1')]

24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)

 arr1 = np.random.random((5,3))
 arr2 = np.random.random((3,2))
 arr3 = np.dot(arr1,arr2)
 print(arr1)
 print(arr2)
 print(arr3)

运行结果：

[[0.51143733 0.22531681 0.71917393]
[0.81667839 0.1703277 0.1519645 ]
[0.15206992 0.370845 0.62943418]
[0.33517061 0.6224698 0.46285361]
[0.09370654 0.04592576 0.52768143]]
[[0.0785262 0.25563589]
[0.04088907 0.04818778]
[0.82035168 0.40391289]]
[[0.63934976 0.43208287]
[0.19575952 0.27836044]
[0.54346236 0.3109813 ]
[0.43147462 0.30262961]
[0.44212063 0.23930515]]

25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)

 arr = np.random.randint(1,10,10)
 print(arr)
 arr[(arr >=3) & (arr <= 8)] = 0
 print(arr)

运行结果：

[8 1 9 5 5 4 3 1 1 3]

[0 1 9 0 0 0 0 1 1 0]

26. What is the output of the following script? (★☆☆)

 print(sum(range(5),-1))
 from numpy import *
 print(sum(range(5),-1))

 print(sum(range(5),-1))     #-1表示起始
 from numpy import *
 print(sum(range(5),-1))     #-1表示轴向

运行结果：9 10

27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)

 Z**Z
 2 << Z >> 2
 Z <- Z
 1j*Z
 Z/1/1
 Z<Z>Z

 z = np.random.randint(1,4,(5,5))
 print(z)
 print(z**z)
 print(2<<z>>2)
 print(z < -z)
 print(1j*z)   #1j表示复数的i
 print(z/1/1)
 print(z<z>z)

运行结果：略（只有最后一个会出错）

28. What are the result of the following expressions?

 np.array(0) / np.array(0)
 np.array(0) // np.array(0)
 np.array([np.nan]).astype(int).astype(float)

运行结果：nan 0 [-2.14748365e+09]

29. How to round away from zero a float array ? (★☆☆)

 arr = np.random.random(5)*100
 arr1 = np.trunc(arr+0.5)
 print(arr)
 print(arr1)

运行结果：

[69.37662327 99.0777984 93.6006897 45.79127547 51.93021804]
[69. 99. 94. 46. 52.]

30. How to find common values between two arrays? (★☆☆)

 arr1 = np.random.randint(1,5,10)
 arr2 = np.random.randint(3,10,10)
 print(arr1)
 print(arr2)
 print(np.intersect1d(arr1,arr2))

运行结果：

[1 2 4 3 4 4 2 4 1 1]
[7 5 4 3 9 9 8 8 8 9]
[3 4]