Web Scraping: Python Data Playbook

Web Scraping: Python Data Playbook
Web Scraping: Python Data Playbook

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 17m | 188 MB
eLearning | Skill level: All Levels

Learn how to tell a compelling graphical data story in a Jupyter Notebook with Seaborn having scraped information from a static web page with BeautifulSoup4 when no API is available.

Scrape data from a static web page with BeautifulSoup4 and turn it into a compelling graphical data story in a Jupyter Notebook. In this course, Web Scraping: The Python Data Playbook, you will gain the ability to scrape data and present it graphically. First, you will learn to scrape using the requests module and BeautifulSoup4. Next, you will discover how to write a trustworthy scraping module backed by a unit test. Finally, you will explore how to turn the columns of data in a graphical story that will change the opinions of your colleagues. When you’re finished with this course, you will have the skills and knowledge of web scraping needed to create a graphically compelling Jupyter Notebook without the use of an API.

+ Table of Contents

Course Overview
1 Course Overview

Setting Up BeautifulSoup
2 General Strategies for Scraping Web Pages
3 Reviewing Our Target Auto-MPG Web Page
4 The Complicated Difference between Dynamic and Static Web Pages

Understanding Your Scraped Data
5 A Primer on HTML and CSS
6 Understanding the HTML, CSS and Structure of Our Target Page
7 Coming up with a Strategy for a More Complicated Web Page
8 Using BeautifulSoup4 to Navigate Our Scraped Data
9 Extracting Information from a Scraped Division
10 Using Selectors as an Alternative to the Find Method
11 Advice and Strategy for Scraping
12 Building the Scraper Module Using PyCharm
13 Dealing with Missing Data during the Scrape
14 Refactoring Our Code and Caching Our Scraped Data
15 Adding a Test to Verify Our Regular Expression Processing

Making Scraped Data Usable
16 Exporting Scraped Data to a CSV File
17 Getting a Data Overview with Pandas
18 Exploratory Data Analysis Strategy
19 Reviewing Our Hypothesis
20 Investigating Relationships between MPG and Weight
21 Understanding How Cylinders and Displacement Are Related
22 Looking at MPG over the Years
23 Understanding Brands and Territories with Text Processing
24 Telling a Data Story to Explain Our Discoveries