++++Data Science
May 2026×Notebook lesson
Notebook converted from Jupyter for blog publishing.
04-NumPy-Exercises-Solutions
Driptanil DattaSoftware Developer
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!
otherwise you will end up writing over the example output!
1. Import NumPy as np
import numpy as np2. Create an array of 10 zeros
# CODE HERE# DON'T WRITE HERE
np.zeros(10)RESULT
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)RESULT
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) * 5RESULT
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)RESULT
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)RESULT
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)RESULT
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])8. Create a 3x3 identity matrix
# DON'T WRITE HERE
np.eye(3)RESULT
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)RESULT
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)RESULT
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) / 100RESULT
MORE
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 ],12. Create an array of 20 linearly spaced points between 0 and 1:
# DON'T WRITE HERE
np.linspace(0,1,20)RESULT
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)
matRESULT
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:]RESULT
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]RESULT
2015. Write code that reproduces the output shown below.
# DON'T WRITE HERE
mat[:3,1:2]RESULT
array([[ 2],
[ 7],
[12]])16. Write code that reproduces the output shown below.
# DON'T WRITE HERE
mat[4,:]RESULT
array([21, 22, 23, 24, 25])17. Write code that reproduces the output shown below.
# DON'T WRITE HERE
mat[3:5,:]RESULT
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()RESULT
32519. Get the standard deviation of the values in mat
# DON'T WRITE HERE
mat.std()RESULT
7.21110255092797820. Get the sum of all the columns in mat
# DON'T WRITE HERE
mat.sum(axis=0)RESULT
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 (opens in a new tab)
np.random.seed(101)