Book Image

Numerical Computing with Python

By : Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou
Book Image

Numerical Computing with Python

By: Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou

Overview of this book

Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: • Statistics for Machine Learning by Pratap Dangeti • Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim • Pandas Cookbook by Theodore Petrou
Table of Contents (21 chapters)
Title Page
Contributors
About Packt
Preface
Index

Plotting our first graph


We will start with a simple line graph of a curve of squares, that is, y = x2.

Loading data for plotting

To visualize data, we should start with "having" some data. While we assume you have some nice data on hand to show, we will briefly show you how to load it in Python for plotting.

Data structures

There are several common data structures we will keep coming across.

List

List is a basic Python data type for storing a collection of values. A list is created by putting element values inside a square bracket. To reuse our list, we can give it a name and store it like this:

evens = [2,4,6,8,10]

When we want to get a series in a greater range, for instance, to get more data points for our curve of squares to make it smoother, we may use the Python range() function:

evens = range(2,102,2)

This command will give us all even numbers from 2 to 100 (both inclusive) and store it in a list named evens.

Numpy array

Very often, we deal with more complex data. If you need a matrix with multiple...