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03-NumPy-Exercises

Updated
17 Sept 2026
2 min read
5 sections
Page 10 of 117
Python & Machine Learning

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!

1. Import NumPy as np

2. Create an array of 10 zeros

python
# CODE HERE
np.zeros((10))
Out · result
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
python
# DON'T WRITE HERE
Out · result
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

3. Create an array of 10 ones

python
np.ones((10))
Out · result
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
python
# DON'T WRITE HERE
Out · result
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

4. Create an array of 10 fives

python
np.ones((10)) * 5
Out · result
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])
python
# DON'T WRITE HERE
Out · result
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])

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

python
np.arange(10,51)
Out · 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])
python
# DON'T WRITE HERE
Out · 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

python
np.arange(10, 51, 2)
Out · result
array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,
       44, 46, 48, 50])
python
# DON'T WRITE HERE
Out · 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

python
np.arange(9).reshape(3,3)
Out · result
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
python
# DON'T WRITE HERE
Out · result
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

8. Create a 3x3 identity matrix

python
np.identity(3)
Out · result
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])
python
# DON'T WRITE HERE
Out · 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.

python
np.random.rand(1)
Out · result
array([0.96934451])
python
# DON'T WRITE HERE
Out · 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.

python
np.random.rand(1, 25)
Out · 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]])
python
# DON'T WRITE HERE
Out · 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:

python
np.arange(1, 101).reshape(10,10) / 100
Out · result
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 ],
5 more lines
       [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.  ]])
python
# DON'T WRITE HERE
Out · result
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 ],
5 more lines
       [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:

python
np.linspace(0,1,20)
Out · 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.        ])
python
# DON'T WRITE HERE
Out · 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:

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

python
# CODE HERE
mat[2:,1:]
Out · result
array([[12, 13, 14, 15],
       [17, 18, 19, 20],
       [22, 23, 24, 25]])
python
# DON'T WRITE HERE

14. Write code that reproduces the output shown below.

python
mat[3,4]
Out · result
np.int64(20)
python
# DON'T WRITE HERE

15. Write code that reproduces the output shown below.

python
mat[0:3,1:2]
Out · result
array([[ 2],
       [ 7],
       [12]])
python
# DON'T WRITE HERE

16. Write code that reproduces the output shown below.

python
mat[4]
Out · result
array([21, 22, 23, 24, 25])
python
# DON'T WRITE HERE
Out · result
array([21, 22, 23, 24, 25])

17. Write code that reproduces the output shown below.

python
mat[3:]
Out · result
array([[16, 17, 18, 19, 20],
       [21, 22, 23, 24, 25]])
python
# DON'T WRITE HERE
Out · result
array([[16, 17, 18, 19, 20],
       [21, 22, 23, 24, 25]])

NumPy Operations

18. Get the sum of all the values in mat

python
mat.sum()
Out · result
np.int64(325)
python
# DON'T WRITE HERE
Out · result
325

19. Get the standard deviation of the values in mat

python
mat.std()
Out · result
np.float64(7.211102550927978)
python
# DON'T WRITE HERE
Out · result
7.211102550927978

20. Get the sum of all the columns in mat

python
mat.sum(axis=0)
Out · result
array([55, 60, 65, 70, 75])
python
# DON'T WRITE HERE
Out · 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

Great Job!