================ by Jawad Haider

04 - NumPy Exercises - Solutions¶

Copyright Qalmaqihir For more information, visit us at www.github.com/qalmaqihir/

1 NumPy Exercises - Solutions
1.1 Numpy Indexing and Selection
1.2 NumPy Operations
1.3 Bonus Question
2 Great Job! Thats the end of this part.

NumPy Exercises - Solutions¶

Now that we’ve learned about NumPy let’s test your knowledge. We’ll start off with a few simple tasks and then you’ll be asked some more complicated questions.

IMPORTANT NOTE! Make sure you don’t run the cells directly above the example output shown,
otherwise you will end up writing over the example output!

1. Import NumPy as np¶

import numpy as np

2. Create an array of 10 zeros¶

# CODE HERE

# DON'T WRITE HERE
np.zeros(10)

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

3. Create an array of 10 ones¶

# DON'T WRITE HERE
np.ones(10)

array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

4. Create an array of 10 fives¶

# DON'T WRITE HERE
np.ones(10) * 5

array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])

5. Create an array of the integers from 10 to 50¶

# DON'T WRITE HERE
np.arange(10,51)

array([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])

6. Create an array of all the even integers from 10 to 50¶

# DON'T WRITE HERE
np.arange(10,51,2)

array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,
       44, 46, 48, 50])

7. Create a 3x3 matrix with values ranging from 0 to 8¶

# DON'T WRITE HERE
np.arange(9).reshape(3,3)

array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

8. Create a 3x3 identity matrix¶

# DON'T WRITE HERE
np.eye(3)

array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])

9. Use NumPy to generate a random number between 0 and 1

NOTE: Your result’s value should be different from the one shown below.¶

# DON'T WRITE HERE
np.random.rand(1)

array([0.65248055])

10. Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution

NOTE: Your result’s values should be different from the ones shown below.¶

# DON'T WRITE HERE
np.random.randn(25)

array([ 1.80076712, -1.12375847, -0.98524305,  0.11673573,  1.96346762,
        1.81378592, -0.33790771,  0.85012656,  0.0100703 , -0.91005957,
        0.29064366,  0.69906357,  0.1774377 , -0.61958694, -0.45498611,
       -2.0804685 , -0.06778549,  1.06403819,  0.4311884 , -1.09853837,
        1.11980469, -0.48751963,  1.32517611, -0.61775122, -0.00622865])

11. Create the following matrix:¶

# DON'T WRITE HERE
np.arange(1,101).reshape(10,10) / 100

array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],
       [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],
       [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],
       [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],
       [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],
       [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],
       [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],
       [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],
       [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],
       [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  ]])

12. Create an array of 20 linearly spaced points between 0 and 1:¶

# DON'T WRITE HERE
np.linspace(0,1,20)

array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,
       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,
       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,
       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])

Numpy Indexing and Selection¶

Now you will be given a starting matrix (be sure to run the cell below!), and be asked to replicate the resulting matrix outputs:

# RUN THIS CELL - THIS IS OUR STARTING MATRIX
mat = np.arange(1,26).reshape(5,5)
mat

array([[ 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]])

13. Write code that reproduces the output shown below.

Be careful not to run the cell immediately above the output, otherwise you won’t be able to see the output any more.¶

# CODE HERE

# DON'T WRITE HERE
mat[2:,1:]

array([[12, 13, 14, 15],
       [17, 18, 19, 20],
       [22, 23, 24, 25]])

14. Write code that reproduces the output shown below.¶

# DON'T WRITE HERE
mat[3,4]

15. Write code that reproduces the output shown below.¶

# DON'T WRITE HERE
mat[:3,1:2]

array([[ 2],
       [ 7],
       [12]])

16. Write code that reproduces the output shown below.¶

# DON'T WRITE HERE
mat[4,:]

array([21, 22, 23, 24, 25])

17. Write code that reproduces the output shown below.¶

# DON'T WRITE HERE
mat[3:5,:]

array([[16, 17, 18, 19, 20],
       [21, 22, 23, 24, 25]])

NumPy Operations¶

18. Get the sum of all the values in mat¶

# DON'T WRITE HERE
mat.sum()

19. Get the standard deviation of the values in mat¶

# DON'T WRITE HERE
mat.std()

7.211102550927978

20. Get the sum of all the columns in mat¶

# DON'T WRITE HERE
mat.sum(axis=0)

array([55, 60, 65, 70, 75])

Bonus Question¶

We worked a lot with random data with numpy, but is there a way we can insure that we always get the same random numbers? Click Here for a Hint

np.random.seed(101)

Great Job! Thats the end of this part.¶

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