Review & Group Exercises
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.
Import NumPy as np
Create an array of 10 zeros
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
Create an array of 10 ones
array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Create an array of 10 fives
array([ 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])
Create an array of the integers from 10 to 50
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])
Create an array of all the even integers from 10 to 50
array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,
44, 46, 48, 50])
Create a 3x3 matrix with values ranging from 0 to 8
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
Create a 3x3 identity matrix
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
Use NumPy to generate a random number between 0 and 1
array([ 0.42829726])
Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution
array([ 1.32031013, 1.6798602 , -0.42985892, -1.53116655, 0.85753232,
0.87339938, 0.35668636, -1.47491157, 0.15349697, 0.99530727,
-0.94865451, -1.69174783, 1.57525349, -0.70615234, 0.10991879,
-0.49478947, 1.08279872, 0.76488333, -2.3039931 , 0.35401124,
-0.45454399, -0.64754649, -0.29391671, 0.02339861, 0.38272124])
Create the following matrix:
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. ]])
Create an array of 20 linearly spaced points between 0 and 1:
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 few matrices, and be asked to replicate the resulting matrix outputs:
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]])
array([[12, 13, 14, 15],
[17, 18, 19, 20],
[22, 23, 24, 25]])
array([[ 2],
[ 7],
[12]])
array([21, 22, 23, 24, 25])
array([[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25]])
Now do the following
Get the sum of all the values in mat
325
Get the standard deviation of the values in mat
7.2111025509279782
Get the sum of all the columns in mat
array([55, 60, 65, 70, 75])
Good Job Kids!
