- Python Web Scraping Tutorial
- Python Web Scraping Resources
During this lesson, you will learn code plans used by real professionals. These plans represent the most common patterns in web scraping with the BeautifulSoup library. The plans in this ebook were created after an anaylsis of 100 web scraping files from Github, as well as two interviews with people who use web scraping in their jobs. Web scraping is the process extracting data from a web site using a program. The program works like a browser and requests the HTML from the web server. However, instead of converting the HTML into something that can be displayed to the user, a web scraping program will parse the HTML and extract useful information from it. So whether you are a data analyst who wants to add web scraping to his tool set or someone else who wants to learn how to extract unstructured data from unstructured HTML web pages and then store back that data in a structured way to apply some data analysis on it then you are welcome to join this course. Learn web scraping. Now that you know the basics of web scraping, you might want to explore the topic further. To save you time, we’ve collected a few courses and tutorials suitable for all levels. We recommend these as a great way to quickly get up to speed on web scraping.
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Web scraping, also called web data mining or web harvesting, is the process of constructing an agent which can extract, parse, download and organize useful information from the web automatically.
This tutorial will teach you various concepts of web scraping and makes you comfortable with scraping various types of websites and their data.
This tutorial will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The tutorial suits the learning needs of both a beginner or an advanced learner.
The reader must have basic knowledge about HTML, CSS, and Java Script. He/she should also be aware about basic terminologies used in Web Technology along with Python programming concepts. If you do not have knowledge on these concepts, we suggest you to go through tutorials on these concepts first.
A growing number of business activities and our lives are being spent online, this has led to an increase in the amount of publicly available data. Web scraping allows you to tap into this public information with the help of web scrapers.
In the first part of this guide to basics of web scraping you will learn –
- What is web scraping?
- Web scraping use cases
- Types of web scrapers
- How does a web scraper work?
- Difference between a web scraper and web crawler
- Is web scraping legal?
What is web scraping?
Web scraping automates the process of extracting data from a website or multiple websites. Web scraping or data extraction helps convert unstructured data from the internet into a structured format allowing companies to gain valuable insights. This scraped data can be downloaded as a CSV, JSON, or XML file.
Web scraping (or Data Scraping or Data Extraction or Web Data Extraction used synonymously), helps transform this content on the Internet into structured data that can be consumed by other computers and applications. The scraped data can help users or businesses to gather insights that would otherwise be expensive and time-consuming. Sopcast for mac os.
Since the basic idea of web scraping is automating a task, it can be used to create web scraping APIs and Robotic Process Automation (RPA) solutions. Web scraping APIs allow you to stream scraped website data easily into your applications. This is especially useful in cases where a website does not have an API or has a rate/volume-limited API.
Uses of Web Scraping
People use web scrapers to automate all sorts of scenarios. Web scrapers have a variety of uses in the enterprise. We have listed a few below:
- Price Monitoring –Product data is impacting eCommerce monitoring, product development, and investing. Extracting product data such as pricing, inventory levels, reviews and more from eCommere websites can help you create a better product strategy.
- Marketing and Lead Generation –As a business, to reach out to customers and generate sales, you need qualified leads. That is getting details of companies, addresses, contacts, and other necessary information. Publicly information like this is valuable. Web scraping can enhance the productivity of your research methods and save you time.
- Location Intelligence – The transformation of geospatial data into strategic insights can solve a variety of business challenges. By interpreting rich data sets visually you can conceptualize the factors that affect businesses in various locations and optimize your business process, promotion, and valuation of assets.
- News and Social Media – Social media and news tells your viewers how they engage with, share, and perceive your content. When you collect this information through web scraping you can optimize your social content, update your SEO, monitor other competitor brands, and identify influential customers.
- Real Estate – The real estate industry has myriad opportunities. Including web scraped data into your business can help you identify real estate opportunities, find emerging markets analyze your assets.
How to get started with web scraping
There are many ways to get started with web scraper, writing code from scratch is fine for smaller data scraping needs. But beyond that, if you need to scrape a few different types of web pages and thousands of data fields, you will need a web scraping service that is able to scrape multiple websites easily on a large scale.
Custom Web Scraping Services
Many companies build their own web scraping departments but other companies use Web Scraping services. While it may make sense to start an in house web scraping solution, the time and cost involved far outweigh the benefits. Hiring a custom web scraping service ensures that you can concentrate on your projects.
Web scraping companies such as ScrapeHero, have the technology and scalability to handle web scraping tasks that are complex and massive in scale – think millions of pages. You need not worry about setting up and running scrapers, avoiding and bypassing CAPTCHAs, rotating proxies, and other tactics websites use to block web scraping.
Web Scraping Tools and Software
Point and click web scraping tools have a visual interface, where you can annotate the data you need, and it automatically builds a web scraper with those instructions. Web Scraping tools (free or paid) and self-service applications can be a good choice if the data requirement is small, and the source websites aren’t complicated.
ScrapeHero Cloud has pre-built scrapers that in addition to scraping search engine data, can Scrape Job data, Scrape Real Estate Data, Scrape Social Media and more. These scrapers are easy to use and cloud-based, where you need not worry about selecting the fields to be scraped nor download any software. The scraper and the data can be accessed from any browser at any time and can deliver the data directly to Dropbox.
Scraping Data Yourself
You can build web scrapers in almost any programming language. It is easier with Scripting languages such as Javascript (Node.js), PHP, Perl, Ruby, or Python. If you are a developer, open-source web scraping tools can also help you with your projects. If you are just new to web scraping these tutorials and guides can help you get started with web scraping.
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How does a web scraper work
A web scraper is a software program or script that is used to download the contents (usually text-based and formatted as HTML) of multiple web pages and then extract data from it.
Web scrapers are more complicated than this simplistic representation. They have multiple modules that perform different functions.
What are the components of a web scraper
Web scraping is like any other Extract-Transform-Load (ETL) Process. Web Scrapers crawl websites, extracts data from it, transforms it into a usable structured format, and loads it into a file or database for subsequent use.
A typical web scraper has the following components:
1. Crawl
First, we start at the data source and decide which data fields we need to extract. For that, we have web crawlers, that crawl the website and visit the links that we want to extract data from. (e.g the crawler will start at https://scrapehero.com and crawl the site by following links on the home page.)
The goal of a web crawler is to learn what is on the web page, so that the information when it is needed, can be retrieved. The web crawling can be based on what it finds or it can search the whole web (just like the Google search engine does).
2. Parse and Extract
Extracting data is the process of taking the raw scraped data that is in HTML format and extracting and parsing the meaningful data elements. In some cases extracting data may be simple such as getting the product details from a web page or it can get more difficult such as retrieving the right information from complex documents.
You can use data extractors and parsers to extract the information you need. There are different kinds of parsing techniques: Regular Expression, HTML Parsing, DOM Parsing (using a headless browser), or Automatic Extraction using AI.
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3. Format
Now the data extracted needs to be formatted into a human-readable form. These can be in simple data formats such as CSV, JSON, XML, etc. You can store the data depending on the specification of your data project.
The data extracted using a parser won’t always be in the format that is suitable for immediate use. Most of the extracted datasets need some form of “cleaning” or “transformation.” Regular expressions, string manipulation, and search methods are used to perform this cleaning and transformation.
4. Store and Serialize Data
After the data has been scraped, extracted, and formatted you can finally store and export the data. Once you get the cleaned data, it needs to be serialized according to the data models that you require. Choosing an export method largely depends on how large your data files are and what data exports are preferred within your company.
This is the final module that will output data in a standard format that can be stored in Databases using ETL tools (Check out our guide on ETL Tools), JSON/CSV files, or data delivery methods such as Amazon S3, Azure Storage, and Dropbox.
Web Crawling vs. Web Scraping
People often use Web Scraping and Web Crawling interchangeably. Although the underlying concept is to extract data from the web, they are different.
Web Crawling mostly refers to downloading and storing the contents of a large number of websites, by following links in web pages. A web crawler is a standalone bot, that scans the internet, searching, and indexing for content. In general, a ‘crawler’ means the ability to navigate pages on its own. Crawlers are the backbones of search engines like Google, Bing, Yahoo, etc.
A Web scraper is built specifically to handle the structure of a particular website. The scraper then uses this site-specific structure to extract individual data elements from the website. Unlike a web crawler, a web scraper extracts specific information such as pricing data, stock market data, business leads, etc.
Is web scraping legal?
Although web scraping is a powerful technique in collecting large data sets, it is controversial and may raise legal questions related to copyright and terms of service. Most times a web scraper is free to copy a piece of data from a web page without any copyright infringement. This is because it is difficult to prove copyright over such data since only a specific arrangement or a particular selection of the data is legally protected.
Legality is totally dependent on the legal jurisdiction (i.e. Laws are country and locality specific). Publicly available information gathering or scraping is not illegal, if it were illegal, Google would not exist as a company because they scrape data from every website in the world.
Terms of Service
Although most web applications and companies include some form of TOS agreement, it lies within a gray area. For instance, the owner of a web scraper that violates the TOS may argue that he or she never saw or officially agreed to the TOS
Some forms of web scraping can be illegal such as scraping non-public data or disclosed data. Non-public data is something that isn’t reachable or open to the public. An example of this would be, the stealing of intellectual property.
Ethical Web Scraping
If a web scraper sends data acquiring requests too frequently, the website will block you. The scraper may be refused entry and may be liable for damages because the owner of the web application has a property interest. An ethical scraping tool or professional web scraping services will avoid this issue by maintaining a reasonable requesting frequency. We talk in other guides about how you can make your scraper more “polite” so that it doesn’t get you into trouble.
What’s next?
Let’s do something hands-on before we get into web page structures and XPaths. We will make a very simple scraper to scrape Reddit’s top pages and extract the title and URLs of the links shared.
Check out part 2 and 3 of this post in the link here – A beginners guide to Web Scraping: Part 2 – Build a web scraper for Reddit using Python and BeautifulSoup
Web Scraping Tutorial for Beginners – Part 3 – Navigating and Extracting Data – Navigating and Scraping Data from Reddit
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