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Python Web Scraping Cookbook

You're reading from   Python Web Scraping Cookbook Over 90 proven recipes to get you scraping with Python, microservices, Docker, and AWS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787285217
Length 364 pages
Edition 1st Edition
Languages
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Concepts
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Scraping 2. Data Acquisition and Extraction FREE CHAPTER 3. Processing Data 4. Working with Images, Audio, and other Assets 5. Scraping - Code of Conduct 6. Scraping Challenges and Solutions 7. Text Wrangling and Analysis 8. Searching, Mining and Visualizing Data 9. Creating a Simple Data API 10. Creating Scraper Microservices with Docker 11. Making the Scraper as a Service Real 12. Other Books You May Enjoy

To get the most out of this book

The primary tool required for the recipes in this book is a Python 3 interpreter. The recipes have been written using the free version of the Anaconda Python distribution, specifically version 3.6.1. Other Python version 3 distributions should work well but have not been tested.

The code in the recipes will often require the use of various Python libraries. These are all available for installation using pip and accessible using pip install. Wherever required, these installations will be elaborated in the recipes.

Several recipes require an Amazon AWS account. AWS accounts are available for the first year for free-tier access. The recipes will not require anything more than free-tier services. A new account can be created at https://portal.aws.amazon.com/billing/signup.

Several recipes will utilize Elasticsearch. There is a free, open source version available on GitHub at https://github.com/elastic/elasticsearch, with installation instructions on that page. Elastic.co also offers a fully capable version (also with Kibana and Logstash) hosted on the cloud with a 14-day free trial available at http://info.elastic.co (which we will utilize). There is a version for docker-compose with all x-pack features available at https://github.com/elastic/stack-docker, all of which can be started with a simple docker-compose up command.

Finally, several of the recipes use MySQL and PostgreSQL as database examples and several common clients for those databases. For those recipes, these will need to be installed locally. MySQL Community Server is available at https://dev.mysql.com/downloads/mysql/, and PostgreSQL can be found at https://www.postgresql.org/.

We will also look at creating and using docker containers for several of the recipes. Docker CE is free and is available at https://www.docker.com/community-edition.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packtpub.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Python-Web-Scraping-Cookbook. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "This will loop through up to 20 characters and drop them into the sw index with a document type of people"

A block of code is set as follows:

from elasticsearch import Elasticsearch
import requests
import json

if __name__ == '__main__':
es = Elasticsearch(
[

Any command-line input or output is written as follows:

$ curl https://elastic:tduhdExunhEWPjSuH73O6yLS@7dc72d3327076cc4daf5528103c46a27.us-west-2.aws.found.io:9243

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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