Python Web Scraping Cookbook

Michael Heydt

• Editorial: Packt Publishing
• Año: 2018
• ISBN:
  9781787286634
• Edición: 1
• Categoría: Computer Science
• Categoría: Programming in Python

Untangle your web scraping complexities and access web data with ease using Python scriptsAbout This Book• Hands-on recipes for advancing your web scraping skills to expert level.• One-Stop Solution Guide to address complex and challenging web scraping tasks using Python.• Understand the web page structure and collect meaningful data from the website with ease Who This Book Is ForThis book is ideal for Python programmers, web administrators, security professionals or someone who wants to perform web analytics would find this book relevant and useful. Familiarity with Python and basic understanding of web scraping would be useful to take full advantage of this book.What You Will Learn• Use a wide variety of tools to scrape any website and data—including BeautifulSoup, Scrapy, Selenium, and many more• Master expression languages such as XPath, CSS, and regular expressions to extract web data• Deal with scraping traps such as hidden form fields, throttling, pagination, and different status codes• Build robust scraping pipelines with SQS and RabbitMQ• Scrape assets such as images media and know what to do when Scraper fails to run• Explore ETL techniques of build a customized crawler, parser, and convert structured and unstructured data from websites• Deploy and run your scraper-as-aservice in AWS Elastic Container ServiceIn DetailPython Web Scraping Cookbook is a solution-focused book that will teach you techniques to develop high-performance scrapers and deal with crawlers, sitemaps, forms automation, Ajax-based sites, caches, and more.You'll explore a number of real-world scenarios where every part of the development/product life cycle will be fully covered. You will not only develop the skills to design and develop reliable, performance data flows, but also deploy your codebase to an AWS. If you are involved in software engineering, product development, or data mining (or are interested in building data-driven products), you will find this book useful as each recipe has a clear purpose and objective.Right from extracting data from the websites to writing a sophisticated web crawler, the book's independent recipes will be a godsend on the job. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with AJAX websites, paginated items, and more. You will also learn to tackle problems such as 403 errors, working with proxy, scraping images, LXML, and more.By the end of this book, you will be able to scrape websites more efficiently and to be able to deploy and operate your scraper in the cloud.Style and approachThis book is a rich collection of recipes that will come in handy when you are scraping a website using Python.Addressing your common and not-so-common pain points while scraping website, this is a book that you must have on the shelf.

Making the Scraper as a Service Real

In this chapter, we will cover:

• Creating and configuring an Elastic Cloud trial account
• Accessing the Elastic Cloud cluster with curl
• Connecting to the Elastic Cloud cluster with Python
• Performing an Elasticsearch query with the Python API
• Using Elasticsearch to query for jobs with specific skills
• Modifying the API to search for jobs by skill
• Storing configuration in the environment
  Creating an AWS IAM user and a key pair for ECS
• Configuring Docker to authenticate with ECR
• Pushing containers into ECR
• Creating an ECS cluster
• Creating a task to run our containers
• Starting and accessing the containers in AWS

Introduction

In this chapter, we will first add a feature to search job listings using Elasticsearch and extend the API for this capability. Then will move Elasticsearch functions to Elastic Cloud, a first step in cloud-enabling our cloud based scraper. Then, we will move our Docker containers to Amazon Elastic Container Repository (ECR), and finally run our containers (and scraper) in Amazon Elastic Container Service (ECS).

Creating and configuring an Elastic Cloud trial account

In this recipe we will create and configure an Elastic Cloud trial account so that we can use Elasticsearch as a hosted service. Elastic Cloud is a cloud service offered by the creators of Elasticsearch, and provides a completely managed implementation of Elasticsearch.

While we have examined putting Elasticsearch in a Docker container, actually running a container with Elasticsearch within AWS is very difficult due to a number of memory requirements and other system configurations that are complicated to get working within ECS. Therefore, for a cloud solution, we will use Elastic Cloud.

How to do it

We'll proceed with the recipe as follows:

1. Open your browser and navigate to https://www.elastic.co/cloud/as-a-service/signup. You will see a page similar to the following:

The Elastic Cloud signup page

2. Enter your email and press the Start Free Trial button. When the email arrives, verify yourself. You will be taken to a page to create your cluster:

Cluster creation page

3. I'll be using AWS (not Google) in the Oregon (us-west-2) region in other examples, so I'll pick both of those for this cluster. You can pick a cloud and region that works for you. You can leave the other options as it is, and just press create. You will then be presented with your username and password. Jot those down. The following screenshot gives an idea of how it displays the username and password:

The credentials info for the Elastic Cloud account

We won't use the Cloud ID in any recipes.

4. Next, you will be presented with your endpoints. The Elasticsearch URL is what's important to us:

5. And that's it - you are ready to go (at least for 14 days)!

Accessing the Elastic Cloud cluster with curl

Elasticsearch is fundamentally accessed via a REST API. Elastic Cloud is no different and is actually an identical API. We just need to be able to know how to construct the URL properly to connect. Let's look at that.

How to do it

We proceed with the recipe as follows:

1. When you signed up for Elastic Cloud, you were given various endpoints and variables, such as username and password. The URL was similar to the following:

https://<account-id>.us-west-2.aws.found.io:9243

Depending on the cloud and region, the rest of the domain name, as well as the port, may differ.

2. We'll use a slight variant of the following URL to communicate and authenticate with Elastic Cloud:

https://<username>:<password>@<account-id>.us-west-2.aws.found.io:9243

3. Currently, mine is (it will be disabled by the time you read this):

https://elastic:[e-mail]:9243

4. Basic authentication and connectivity can be checked with curl:

$ curl https://elastic:[e-mail]:9243
{
 "name": "instance-0000000001",
 "cluster_name": "7dc72d3327076cc4daf5528103c46a27",
 "cluster_uuid": "g9UMPEo-QRaZdIlgmOA7hg",
 "version": {
 "number": "6.1.1",
 "build_hash": "bd92e7f",
 "build_date": "2017-12-17T20:23:25.338Z",
 "build_snapshot": false,
 "lucene_version": "7.1.0",
 "minimum_wire_compatibility_version": "5.6.0",
 "minimum_index_compatibility_version": "5.0.0"
 },
 "tagline": "You Know, for Search"
}
Michaels-iMac-2:pems michaelheydt$

And we are up and talking!

Connecting to the Elastic Cloud cluster with Python

Now let's look at how to connect to Elastic Cloud using the Elasticsearch Python library.

Getting ready

The code for this recipe is in the 11/01/elasticcloud_starwars.py script. This script will scrape Star Wars character data from the swapi.co API/website and put it into the Elastic Cloud.

How to do it

We proceed with the recipe as follows:

1. Execute the file as a Python script:

$ python elasticcloud_starwars.py

2. This will loop through up to 20 characters and drop them into the sw index with a document type of people. The code is straightforward (replace the URL with yours):

from elasticsearch import Elasticsearch
import requests
import json

if __name__ == '__main__':
 es = Elasticsearch(
 [
 "https://elastic:tdu...