Book Image

Applied Data Science with Python and Jupyter

By : Alex Galea
Book Image

Applied Data Science with Python and Jupyter

By: Alex Galea

Overview of this book

Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.

Related Content you might be interested in

Current Title:

Small book image
Applied Data Science with Python and Jupyter
Alex Galea
Oct, 2018 | 192 pages
Small book image
Become a Python Data Analyst
Alvaro Fuentes
Aug, 2018 | 178 pages
Small book image
The Data Science Workshop
Robert Thas John | Anthony So | Thomas V Joseph | Andrew Worsley | Samuel Asare
Jan, 2020 | 818 pages
Small book image
Data Science for Marketing Analytics
Pranshu Bhatnagar | Tommy Blanchard | Debasish Behera
Mar, 2019 | 420 pages
Table of Contents (6 chapters)
Applied Data Science with Python and Jupyter
Applied Data Science with Python and Jupyter
Preface
Preface
Free Chapter
1
Jupyter Fundamentals
Jupyter Fundamentals
Introduction
Basic Functionality and Features
Our First Analysis - The Boston Housing Dataset
Summary
2
Data Cleaning and Advanced Machine Learning
Data Cleaning and Advanced Machine Learning
Introduction
Preparing to Train a Predictive Model
Training Classification Models
Summary
3
Web Scraping and Interactive Visualizations
Web Scraping and Interactive Visualizations
Introduction
Scraping Web Page Data
Interactive Visualizations
Summary
Appendix A
Appendix A
Chapter 1: Jupyter Fundamentals
Chapter 2: Data Cleaning and Advanced Machine
Chapter 3: Web Scraping and Interactive Visualizations

Summary

In this chapter, we scraped web page tables and then used interactive visualizations to study the data.

We started by looking at how HTTP requests work, focusing on GET requests and their response status codes. Then, we went into the Jupyter Notebook and made HTTP requests with Python using the Requests library. We saw how Jupyter can be used to render HTML in the notebook, along with actual web pages that can be interacted with. After making requests, we saw how Beautiful Soup can be used to parse text from the HTML, and used this library to scrape tabular data.

After scraping two tables of data, we stored them in pandas DataFrames. The first table contained the central bank interest rates for each country and the second table contained the populations. We combined these into a single table that was then used to create interactive visualizations.

Finally, we used Bokeh to render interactive visualizations in Jupyter. We saw how to use the Bokeh API to create various customized plots...

Previous Section
End of Chapter 3
Next Chapter