Python Crash Course Exercise 2
Today i will completing Numpy Exercise. If you want to solve it all by yourself, you can download notebooks file here
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Now Lets get started
NumPy ExercisesPermalink
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 npPermalink
import numpy as np
Create an array of 10 zerosPermalink
np.zeros(10)
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
Create an array of 10 onesPermalink
np.ones(10)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Create an array of 10 fivesPermalink
np.ones(10) * 5
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])
Create an array of the integers from 10 to 50Permalink
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])
Create an array of all the even integers from 10 to 50Permalink
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])
Create a 3x3 matrix with values ranging from 0 to 8Permalink
np.arange(0,9).reshape(3,3)
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
Create a 3x3 identity matrixPermalink
np.identity(3)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
Use NumPy to generate a random number between 0 and 1Permalink
np.random.rand()
0.676711335601249
Use NumPy to generate an array of 25 random numbers sampled from a standard normal distributionPermalink
np.random.standard_normal(25)
array([-1.94269319, 0.16413494, 0.12671602, -0.85521163, 0.07889969,
-0.6964513 , 0.459806 , -0.79168522, -0.85169067, -0.91152675,
-0.46378128, 1.82498542, -0.51968832, 0.40439839, -0.09762801,
-0.60365759, -0.75796157, 1.14922432, -0.47134756, -0.39654744,
1.15194413, -1.76571928, -1.14328406, -2.40506812, -0.90691368])
Create the following matrix:Permalink
np.arange(1,100) / 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])
Create an array of 20 linearly spaced points between 0 and 1:Permalink
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 SelectionPermalink
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]])
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat[2:,1:]
array([[12, 13, 14, 15],
[17, 18, 19, 20],
[22, 23, 24, 25]])
array([[12, 13, 14, 15],
[17, 18, 19, 20],
[22, 23, 24, 25]])
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat[3,4]
20
20
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat[:3, 1].reshape(3,1)
array([[ 2],
[ 7],
[12]])
array([[ 2],
[ 7],
[12]])
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat[4,:]
array([21, 22, 23, 24, 25])
array([21, 22, 23, 24, 25])
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat[3:,:]
array([[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25]])
array([[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25]])
Now do the followingPermalink
Get the sum of all the values in matPermalink
mat.sum()
325
Get the standard deviation of the values in matPermalink
mat.std()
7.211102550927978
Get the sum of all the columns in matPermalink
mat.sum(axis=1)
array([ 15, 40, 65, 90, 115])
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