numpy exercises
This article is only for my study notes and is deleted
The title comes from https://github.com/nndl/nndl.github.io
array operation of numpy
1. Import numpy Library
import numpy as np
2. Create a one-dimensional array A and initialize it to [4,5,6], (1) output the type of a, (2) output the size of each dimension of a, (3) output the first element of a (the value is 4)
a = np.array([4, 5, 6]) print(a.dtype) print(a.shape) print(a[0])
int64 (3,) 4
3. Create a two-dimensional array b and initialize it as [[4, 5, 6], [1, 2, 3]] (1) output the size of each dimension (shape) (2) output the three elements of b(0,0), b (0,1) and b (1,1) (the corresponding values are 4,5,2 respectively)
b = np.array( [[4, 5, 6],
[1, 2, 3]])
print(b.shape)
print(b[0][0], b[0][1], b[1][1])
(2, 3) 4 5 2
4. (1) establish a full 0 matrix A with the size of 3x3; The type is integer (hint: dtype = int) (2) establish a full 1 matrix b with a size of 4x5; (3) Establish an identity matrix c with a size of 4x4; (4) Generate a random number matrix d with a size of 3x2
a = np.zeros((3, 3), dtype = int) b = np.ones((4, 5)) c = np.identity((4)) d = np.random.randint(1, 10, size=(3, 2))
5. Create an array a, (the values are [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), (1) print a; (2) The output subscripts are the values of the two array elements (2,3), (0,0)
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) print(a) print(a[2][3], a[0][0])
[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] 12 1
6. Put 0 to 1 rows and 2 to 3 columns of the a array in the previous question into b, (there is no need to create a new one here, just call it directly) (1) and output b;(2) Output the value of the (0,0) element of b
b = a[0:2, 2:4] b
array([[3, 4],
[7, 8]])
7. Put all the elements in the last two lines of array a in question 5 into c, (prompt: a[1:2,:]) (1) output c; (2) Output the last element of the first line in c (hint, use - 1 for the last element)
c = a[1:,] print(c) print(c[0][-1])
[[ 5 6 7 8] [ 9 10 11 12]] 8
8. Create array a, initialize a as [[1,2], [3,4], [5,6]], and output three elements (0,0) (1,1) (2,0) (prompt: use print (a [[0,1,2], [0,1,0]])
a = np.array([[1, 2], [3, 4], [5, 6]]) print(a[[0, 1, 2], [0, 1, 0]]) a
[1 4 5]
array([[1, 2],
[3, 4],
[5, 6]])
9. Establish matrix A, initialize to [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], output (0,0), (1,2), (2,0), (3,1) (prompt to use B = NP. Array ([0, 2, 0, 1]) print (a [NP. Range (4, b]))
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]) print(a) b = np.array([0, 2, 0, 1]) print(a[np.arange(4), b])
[[ 1 2 3] [ 4 5 6] [ 7 8 9] [10 11 12]] [ 1 6 7 11]
10. Add 10 to each of the four elements output in 9, and then re output matrix A. (prompt: a [NP. Orange (4), b] + = 10)
a[np.arange(4), b] += 10 a
array([[11, 2, 3],
[ 4, 5, 16],
[17, 8, 9],
[10, 21, 12]])
Mathematical operation of array
11. Execute x = NP Array ([1, 2]), and then output the data type of X
x = np.array([1, 2]) x.dtype
dtype('int64')
12. Execute x = NP Array ([1.0, 2.0]), and then output the data class type of X
x = np.array([1.0, 2.0]) x.dtype
dtype('float64')
13. Execute x = NP array([[1, 2], [3, 4]], dtype=np.float64) ，y = np.array([[5, 6], [7, 8]], dtype=np.float64), and then output x+y, and NP add(x,y)
x = np.array([[1, 2], [3, 4]], dtype=np.float64) y = np.array([[5, 6], [7, 8]], dtype=np.float64) print(x+y) print(np.add(x,y))
[[ 6. 8.] [10. 12.]] [[ 6. 8.] [10. 12.]]
14. Use X and Y in topic 13 to output x-y and NP subtract(x,y)
print(x-y) np.subtract(x,y)
[[-4. -4.]
[-4. -4.]]
array([[-4., -4.],
[-4., -4.]])
15. Use X and Y in topic 13 to output x*y and NP Multiply (x, y) and NP Dot (x, y), compare the differences. Then try another one that is not square array.
print(x*y) print(np.multiply(x,y)) print(np.dot(x,y))
[[ 5. 12.] [21. 32.]] [[ 5. 12.] [21. 32.]] [[19. 22.] [43. 50.]]
16. Output x / y by using X and Y in 13 topics (tip: use the function np.divide())
print(x/y) print(np.divide(x,y))
[[0.2 0.33333333] [0.42857143 0.5 ]] [[0.2 0.33333333] [0.42857143 0.5 ]]
17. Use x in topic 13 to output the formula of x. (tip: use the function np.sqrt())
print(x**0.5) print(np.sqrt(x))
[[1. 1.41421356] [1.73205081 2. ]] [[1. 1.41421356] [1.73205081 2. ]]
18. Execute print(x.dot(y)) and print(np.dot(x,y)) using x,y in topic 13
print(x.dot(y)) print(np.dot(x,y))
[[19. 22.] [43. 50.]] [[19. 22.] [43. 50.]]
19. Use X in TITLE 13 to sum. Prompt: output three kinds of summation: (1)print(np.sum(x)): (2)print(np.sum(x, axis = 0)); (3)print(np.sum(x,axis = 1))
print(x) print(np.sum(x)) print(np.sum(x,axis =0 )) print(np.sum(x,axis = 1))
[[1. 2.] [3. 4.]] 10.0 [4. 6.] [3. 7.]
20. Use X in 13 questions to calculate the average (prompt: output three kinds of averages (1)print(np.mean(x)) (2)print(np.mean(x,axis = 0))(3) print(np.mean(x,axis =1)))
print(np.mean(x)) print(np.mean(x,axis=0)) print(np.mean(x,axis=1))
2.5 [2. 3.] [1.5 3.5]
21. Use X in topic 13 to transpose the matrix of X, and then output the transposed result. (prompt: x.T indicates the transposition of x)
print(x) print(x.T)
[[1. 2.] [3. 4.]] [[1. 3.] [2. 4.]]
22. Use x in topic 13 to find the exponent of e (hint: function np.exp())
print(np.exp(x))
[[ 2.71828183 7.3890561 ] [20.08553692 54.59815003]]
23. Use X in 13 questions to evaluate the largest subscript (prompt (1) print (np.argmax (x)), (2) print (np.argmax (x, axis = 0)) (3) print (np.argmax (x), axis = 1))
print(x) print(np.argmax(x)) print(np.argmax(x,axis=0)) print(np.argmax(x,axis=1))
[[1. 2.] [3. 4.]] 3 [1 1] [1 1]
24. Draw, y=x*x, where x = NP Orange (0, 100, 0.1) (hint: matplotlib.pyplot library is used here)
import matplotlib.pyplot as plt x = np.arange(0, 100, 0.1) y = x*x plt.plot(x,y) plt.show()
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25. Draw. Draw sine function and cosine function, x = NP Orange (0, 3 * NP. PI, 0.1) (hint: NP. Sin () NP. Pi is used here Cos() function and Matplotlib Pyplot Library)
x = np.arange(0, 3 * np.pi, 0.1) y = np.sin(x) plt.plot(x, y) plt.show() y = np.cos(x) plt.plot(x, y) plt.show()
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25. Draw. Draw sine function and cosine function, x = NP Orange (0, 3 * NP. PI, 0.1) (hint: NP. Sin () NP. Pi is used here Cos() function and Matplotlib Pyplot Library)
x = np.arange(0, 3 * np.pi, 0.1) y = np.sin(x) plt.plot(x, y) plt.show() y = np.cos(x) plt.plot(x, y) plt.show()
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