Python - Numpy - Practice

QTM 350: Data Science Computing

Davi Moreira

Introduction

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This topic material is based on the Python Programming for Data Science book and adapted for our purposes in the course.

Exercise

Import numpy under the alias np.

# Your answer here.

Exercise

Create the following arrays:

1. Create an array of 5 zeros.
2. Create an array of 10 ones.
3. Create an array of 5 3.141s.
4. Create an array of the integers 1 to 20.
5. Create a 5 x 5 matrix of ones with a dtype int.

# Your answer here.

Exercise

Use numpy to:

1. Create an 3D matrix of 3 x 3 x 3 full of random numbers drawn from a standard normal distribution (hint: np.random.randn())
2. Reshape the above array into shape (27,)

# Your answer here.

Exercise

Create an array of 20 linearly spaced numbers between 1 and 10.

# Your answer here.

Exercise

Run the following code to create an array of shape 4 x 4 and then use indexing to produce the outputs shown below.

import numpy as np
a = np.arange(1, 26).reshape(5, -1)

20

# Your answer here.

array([[ 9, 10],
       [14, 15],
       [19, 20],
       [24, 25]])

# Your answer here.

array([ 6,  7,  8,  9, 10])

# Your answer here.

array([[11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20]])

# Your answer here.

array([[ 8,  9],
       [13, 14]])

# Your answer here.

Exercise

Calculate the sum of all the numbers in a.

# Your answer here.

Exercise

Calculate the sum of each row in a.

# Your answer here.

Exercise

Extract all values of a greater than the mean of a (hint: use a boolean mask).

# Your answer here.

Exercise

Find the location of the minimum value in the following array b:

np.random.seed(123)
b = np.random.randn(10)
b

array([-1.0856306 ,  0.99734545,  0.2829785 , -1.50629471, -0.57860025,
        1.65143654, -2.42667924, -0.42891263,  1.26593626, -0.8667404 ])

# Your answer here.

Exercise

Find the location of the maximum value in the following 2D array c (hint: there are many ways to do this question, but a quick search on stackoverflow.com will typically help you find the optimum solution for a problem, for example see post):

np.random.seed(123)
c = np.random.randn(3, 2)
c

array([[-1.0856306 ,  0.99734545],
       [ 0.2829785 , -1.50629471],
       [-0.57860025,  1.65143654]])

# Your answer here.

!jupyter nbconvert _05-py-numpy-practice.ipynb --to html --template classic --output 05-py-numpy-practice.html

[NbConvertApp] Converting notebook _05-py-numpy-practice.ipynb to html
[NbConvertApp] Writing 296182 bytes to 05-py-numpy-practice.html

Have fun!