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Data Science  with Python

You're reading from   Data Science with Python Combine Python with machine learning principles to discover hidden patterns in raw data

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781838552862
Length 426 pages
Edition 1st Edition
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Authors (3):
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Rohan Chopra Rohan Chopra
Author Profile Icon Rohan Chopra
Rohan Chopra
Mohamed Noordeen Alaudeen Mohamed Noordeen Alaudeen
Author Profile Icon Mohamed Noordeen Alaudeen
Mohamed Noordeen Alaudeen
Aaron England Aaron England
Author Profile Icon Aaron England
Aaron England
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Toc

Table of Contents (10) Chapters Close

About the Book 1. Introduction to Data Science and Data Pre-Processing FREE CHAPTER 2. Data Visualization 3. Introduction to Machine Learning via Scikit-Learn 4. Dimensionality Reduction and Unsupervised Learning 5. Mastering Structured Data 6. Decoding Images 7. Processing Human Language 8. Tips and Tricks of the Trade 1. Appendix

Functional Approach

The functional approach to plotting in Matplotlib is a way of quickly generating a single-axis plot. Often, this is the approach taught to beginners. The functional approach allows the user to customize and save plots as image files in a chosen directory. In the following exercises and activities, you will learn how to build line plots, bar plots, histograms, box-and-whisker plots, and scatterplots using the functional approach.

Exercise 13: Functional Approach – Line Plot

To get started with Matplotlib, we will begin by creating a line plot and go on to customize it:

  1. Generate an array of numbers for the horizontal axis ranging from 0 to 10 in 20 evenly spaced values using the following code:

    import numpy as np

    x = np.linspace(0, 10, 20)

  2. Create an array and save it as object y. The snippet of the following code cubes the values of x and saves it to the array, y:

    y = x**3

  3. Create the plot as follows:

    import matplotlib.pyplot as plt

    plt.plot(x, y)

    plt.show()

    See...

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