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
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
Now Lets get started
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
import numpy as np
Create an array of 10 zeros
np.zeros(10)
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
Create an array of 10 ones
np.ones(10)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Create an array of 10 fives
np.ones(10) * 5
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])
Create an array of the integers from 10 to 50
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 50
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 8
np.arange(0,9).reshape(3,3)
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
Create a 3x3 identity matrix
np.identity(3)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
Use NumPy to generate a random number between 0 and 1
np.random.rand()
0.676711335601249
Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution
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:
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:
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 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]])
# 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 following
Get the sum of all the values in mat
mat.sum()
325
Get the standard deviation of the values in mat
mat.std()
7.211102550927978
Get the sum of all the columns in mat
mat.sum(axis=1)
array([ 15, 40, 65, 90, 115])
Leave a comment