In this digital world, data is now everything. Data can help you gain useful insights and notice things that are too valuable to let go of. To effectively utilize the data, we need a good way to first collect the massive amount of data. Web Scraping helps us achieve this. There are many ways to scrape a website like APIs, online services, and writing your code.

1. Web Scraping Using Python For Beginners Tutorial
2. Python Web Scraping Tutorial
3. Web Scraping Using Python Code

In this web scraping tutorial, I will show you how to scrape any kind of website with python. This tutorial is a little different as we will explore a library called SelectorLib which makes it super easy for us to scrape any website and the web scraping tutorial is aimed at beginners so even if you know only the basics of python you are good to go. So if you want to learn how to scrape data from a website using Python 3 then this tutorial is for you. In this tutorial, you will learn how to :

• Create a web scraping template using Chrome extension
• Use Requests and SelectorLib for scraping the data from the created template
• Build a python script that scrapes Top Rated Movies from IMDB and stores them in a JSON file

What is Web Scraping?

Web Scraping is the process of extracting information from websites by using the power of automation. The data from the websites are usually unstructured so we use web scraping to scrape the data and store it in a database that exists in a structured form. For example, let's say we want to collect the data of top-rated movies for research purposes. Now if we were to do it manually it would take many hours if not days. We will solve this problem by creating a web scraper that will automatically scrape all the data of top-rated movies from the website and store it in a database. This would take only a matter of seconds as the computer will do all the heavy lifting for us.

This is when, Web Scraping or Web Crawling comes into picture. Web Scraping is an easy way to perform the repetitive task of copy and pasting data from the websites. With web scraping, we can crawl/surf through the websites and save and represent the necessary data in a customized format. Let us now understand the working of Web Scraping in the. In this video we are going to learn web scraping with python so what does web scraping really mean? Process of grabbing structured web data in a programmed m.

Why is Python best for Web Scraping?

You can create a web scraper with any programming language like Javascript, Java, C++ etc. But here I will list the reasons why Python is preferred for web scraping.

• Easy to Code - Python is one of the most beginner-friendly languages to learn because it is so easy to code in python unlike languages like Java and C++.
• Huge collection of libraries - Python has one of the largest collections of libraries and frameworks. Are you interested in web development? You have got frameworks like Django and Flask for that. Are you into game development? You have got Pygame for that.
• Good community support - Are you constantly getting an error that you just are just able not to fix? Worry not, as many people would be willing to help you fix the error as there are many python communities on different platforms.

How does web scraping work exactly?

Have you ever seen the source code of any webpage? When you right-click on any web page, you would see the option of 'View Page Source'. Now when you click on it, you would be able to see the whole HTML of the current webpage. That's exactly what our code does.

When we run our code, it will make an HTTP request to the specified URL. Then it will store the whole source code web page in a variable. Now we query for any data that we want. In our case, we would have already created a template that will only fetch the data that we require from the source code. So it's much easier than the conventional methods of querying data.

Web Scraping Example: Scraping Top Rated Movies from IMDB

In this example, we will scrape top-rated movies from the IMDB top rated page. We aim to save the details of all movies along with their title, rating, and year. Here are some things that you should have installed on your system before diving into the tutorial.

• Python 3.x with Selectorlib, Requests libraries installed
• Google Chrome Browser
• SelectorLib extension installed on Google Chrome Browser

Once you have downloaded and installed everything, let us get right into the tutorial.

Step 1: Creating the web scraping template using Chrome extension

First, we will go to this link and right-click there. You will see an option called inspect and you have to click it.

After you click on inspect, you have to click on the small arrow on the top right corner. You would be able to see the option to click on SelectorLib. You have to click on it and then create a new template. You can name it anything you like. I will name it 'Top Movies'.

Step 2: Extracting title, rating, and year of the movies

Now we will begin by adding a selector to our template which will contain the title, rating, and year of the movie. Web scraping heavily relies on using the right CSS selector for our type of data. If you want to learn more about CSS selectors you can go to this link. When we inspect to see the source code of the page we can see that all our data is in a table. So all the individual data must be in <tr> tag. The class of parent element of <tr> is lister-list . So first click on Add and name it 'movies'. Then make the selector as .lister-list tr. Keygen for mac office. Finally, make sure that the multiple option is checked and click on save. The final result would look like this.

Our main selector is now created. All that remains is to create children of this selector which will be in the <tr> tag. So click on 'Add a child to this selector' which is right next to the selector that we just created.

On further inspection, we can observe that the title is in a <td> tag with class as titleColumn. So name the selector as td.titleColumn a. The a here signifies the link tag as our title is in it. Then click on save. We now need to inspect the rating. Like the title, it is also in a <td> tag with the name of the class as imdbRating. So again create a child of the selector and name it 'rating' and name the selector as td.imdbRating. Again click on save. Finally, all that remains is to create a selector for the year. Further inspecting on it reveals that it is in a <span> tag with the name of the class as secondaryInfo. So name the child selector as 'year' and set the selector as span.secondaryInfo.

Now the last part of creating the template is to export the YAML file from the extension. In the top right corner of the extension, there is a button for exporting the file. You have to click on it and download the YAML file.

Step 3: Coding our Web Scraper using Python

Now comes the easy part which is coding our web scraper. Create a scraper.py file and place the file that you downloaded earlier in the same directory as the python file that you just created now.

We will now import all our required modules.

The Extractor module will be used to load our YAML file and convert our unstructured data to structured data. The Requests module will be used to make a GET request to the specified URL. Finally, the json module will be used to save all our data in a .json file.

The extractor variable is used for importing the template file in our code. Then we make a get request to the IMDB link which contains the list of Top Rated Movies. Finally, we use the function extract to get the data that we need and print it to the python console. You can use the following command in your terminal to execute the python script - python scraper.py. We get the following output when we run our python script.

Now all that remains is to save all our data in a JSON file. So we will use the following code to save our data.

Complete Source Code

Here is the complete code in python which we used for our web scraping tutorial.

Conclusion

SelectorLib combined with the Chrome extension is a very handy python library for quickly scraping websites. It is much easier to scrape data using this library compared to other python libraries where you have to use regular expressions or complicated syntax. So go ahead and scrape as many websites as you want with your newly learned skill! I hope you found this web scraping with python tutorial informative and learned something new.

Thank you for reading !!

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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 –

1. What is web scraping?
2. Web scraping use cases
3. Types of web scrapers
4. How does a web scraper work?
5. Difference between a web scraper and web crawler
6. 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.

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

Web Scraping Using Python For Beginners Tutorial

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.

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.

Python Web Scraping Tutorial

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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Web Scraping Using Python Code