++++Data Science
May 2026×Notebook lesson
Notebook converted from Jupyter for blog publishing.
03-NumPy-Exercises
Driptanil DattaSoftware Developer
NumPy Exercises
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
np.zeros((10))RESULT
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])# DON'T WRITE HERERESULT
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])3. Create an array of 10 ones
np.ones((10))RESULT
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])# DON'T WRITE HERERESULT
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])4. Create an array of 10 fives
np.ones((10)) * 5RESULT
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])# DON'T WRITE HERERESULT
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])5. Create an array of the integers from 10 to 50
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])# DON'T WRITE HERERESULT
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
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])# DON'T WRITE HERERESULT
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
np.arange(9).reshape(3,3)RESULT
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])# DON'T WRITE HERERESULT
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])8. Create a 3x3 identity matrix
np.identity(3)RESULT
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])# DON'T WRITE HERERESULT
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.
np.random.rand(1)RESULT
array([0.96934451])# DON'T WRITE HERERESULT
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.
np.random.rand(1, 25)RESULT
array([[0.90567178, 0.7850947 , 0.68553382, 0.93534235, 0.88121079,
0.22514085, 0.78426661, 0.813014 , 0.36364837, 0.90605616,
0.62467637, 0.46156559, 0.03637912, 0.84683038, 0.93085044,
0.55751785, 0.80180866, 0.13463081, 0.0829896 , 0.12628335,
0.9022479 , 0.91327593, 0.81035739, 0.68158791, 0.37698927]])# DON'T WRITE HERERESULT
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:
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 ],# DON'T WRITE HERERESULT
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:
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. ])# DON'T WRITE HERERESULT
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
mat[2:,1:]RESULT
array([[12, 13, 14, 15],
[17, 18, 19, 20],
[22, 23, 24, 25]])# DON'T WRITE HERE14. Write code that reproduces the output shown below.
mat[3,4]RESULT
np.int64(20)# DON'T WRITE HERE15. Write code that reproduces the output shown below.
mat[0:3,1:2]RESULT
array([[ 2],
[ 7],
[12]])# DON'T WRITE HERE16. Write code that reproduces the output shown below.
mat[4]RESULT
array([21, 22, 23, 24, 25])# DON'T WRITE HERERESULT
array([21, 22, 23, 24, 25])17. Write code that reproduces the output shown below.
mat[3:]RESULT
array([[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25]])# DON'T WRITE HERERESULT
array([[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25]])NumPy Operations
18. Get the sum of all the values in mat
mat.sum()RESULT
np.int64(325)# DON'T WRITE HERERESULT
32519. Get the standard deviation of the values in mat
mat.std()RESULT
np.float64(7.211102550927978)# DON'T WRITE HERERESULT
7.21110255092797820. Get the sum of all the columns in mat
mat.sum(axis=0)RESULT
array([55, 60, 65, 70, 75])# DON'T WRITE HERERESULT
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)