Websites are loaded with valuable data, and procuring data involves a complex process of manually copy-pasting the information or adhering to the format used by the company — irrespective of its compatibility with the users’ system. This is where web scraping pitches in.
Web Scraping — What is it?
Web Scraping is the process of scooping out and parsing data from a website which in turn is converted to a format that makes it resourceful to the users.
Although web scraping can be done manually, the process becomes complex and tedious when a large amount of raw data gets involved. This is where automated web scraping tools come into effect as they are faster, efficient, and relatively inexpensive.
Web Scrapers are dynamic in their features and functions as their utility varies according to the configurations and forms of websites.
How to Web Scrape useful data?
The process of web scraping begins with providing the users with one or more URLs. Scraping tools generate an HTML code for the web page that needs to be scrapped.
The scraper then scoops out the entire data available on the web page or only the selected portions of the page, depending upon the user’s requirement.
The extracted data is then converted into a usable format.
Why don’t some websites allow web scraping?
Some websites blatantly block their users from scraping their data. But why? Here are the reasons why:
- To protect their sensitive data: Google Maps, for instance, does not allow the users to get faster results if the queries are too many.
- To avoid frequent crashes: A website’s server might crash or slow down if flooded with similar requests as they consume a lot of bandwidth.
Different categories of Web Scrapers
Web scrapers differ from each other in a lot of aspects. Four types of web scrapers are in use.
- Pre-built or self-built
- Browser extensions
- User Interface (UI)
- Cloud & local
1. Self-built web scrapers
Building a web scraper is so simple that anybody can do it. However, the knowledge of handling scraping tools can be obtained only if the user is well versed with advanced programming.
A lot of self-built web scrapers are available for those who are not strong in programming. These pre-built tools can be downloaded and used right away. Some of these tools are equipped with advanced features like Scrape scheduling, Google sheet export, JSON, and so on.
2. Browser Extensions
Two forms of web scrapers that are widely in use are browser extensions and computer software. Browser extensions are programs that can be connected to the browser like Firefox or Google Chrome. The extensions are simple to run and can be easily merged into browsers. They can be used for parsing data only when placed inside the browser, and advanced features placed outside the browser cannot be implemented using scraper extensions.
To alleviate that limitation, scraping software can be used by installing it on the computer. Though it is not as simple as extensions, advanced features can be implemented without any browser limitations.
3. User Interface (UI)
Web scrapers differ in their UI requirements. While some require only a single UI and command line, others may require a complete UI in which an entire website is provided to the user to enable them to scrape the required data in a single click.
Some web scraping tools have the provision to display tips and help messages through the User Interface to help the user to understand every feature provided by the software.
4. Cloud or Local
Local scrapers run on the computer feeding on its resources and internet connection. This has the disadvantage of slowing down the computer when the scrapers are in use. It also affects the ISP data caps when made to run on many URLs.
On the contrary, cloud-based scraping tools run on an off-site server provided by the company that develops the scrapers. This ensures to free-up computer resources, and the users can work on other tasks while simultaneously scraping. The users are given a notification once the scraping is complete.
Web scraping using different methods
The four methods of web scraping that are widely in use are:
- Parsing data from the web using string methods
- Parsing data using regular expressions
- Extracting data using HTML parser
- Scraping data by interacting with components from other websites.
Parsing data from the web using string methods
- This technique procures data from websites using string methods. To search the desired data from HTML texts, the find () tool can be used. Using this tool, the title tag can be obtained from the website.
- If the index of the first and last character of the title is known, a string slice can be used to scrape the title.
- The tool. find () will return the first substring occurrence, and then the index of the starting <title> tag can be obtained by using the string ” <title> to get. find ().
- The data of interest is the title index and not the index of the <title>. To obtain an index for the first letter in the title, the length of the string “<title> can be added to the title index.
- Now, to get the index of the final part </title>, the string “</title>” can be used.
- Now that the first and closing part of the title is obtained, the entire title can be parsed by slicing the HTML string. Here’s the program to do so:
>>> url = “http://olympus.realpython.org/profiles/poseidon“
>>> page = urlopen(url)
>>> html = page.read().decode(“utf-8”)
>>> start_index = html.find(“<title>”) + len(“<title>”)
>>> end_index = html.find(“</title>”)
>>> title = html[start_index:end_index]
‘\n<head>\n<title >Profile: Poseidon’
Notice the presence of HTML code in the title.
Parsing Data using Regular expressions
- Regular Expressions, a.k.a regexes are patterns that are used for searching a text inside a string. Regular expression parsers are supported by Python through its re module.
- To start with regular expression parsing, the re module should be imported first. Special characters called metacharacters are used in regular expressions to mention different patterns.
- For example, the special character asterisk (*) is used to denote 0.
- An example of using findall () to search text within a string can be seen below.
>>> re. findall (“xy*, “ac”)
- In this python program, the first argument and the second argument denote the regular expression and the string to be checked, respectively. The pattern “xy* z” will match with any portion of the string that starts with “x” and ends with “z”. The tool re. findall () returns a list that has all the matches.
- The “xz” string matches with this pattern, and so it is placed in the list.
- A period(.) can be used to represent any single character in a regular expression.
Extracting data using HTML parser
Though regular expressions are effective in matching patterns, an HTML parser exclusively designed to scrape HTML pages is more convenient and faster. The soup library is most widely used for this purpose.
- The first step in HTML parsing is installing beautiful soup by running:
$ python3 -m pip install beautifulsoup4.
The details of the installation can be viewed by using Run pip. Here is the program to create the beautiful soup object:
from urllib.request import urlopen
url = “http://olympus.realpython.org/profiles/dionysus”
page = urlopen(url)
html = page.read().decode(“utf-8”)
pattern = “<title.*?>.*?</title.*?>”
match_results = re.search(pattern, html, re.IGNORECASE)
title = match_results.group()
title = re.sub(“<.*?>”, “”, title) # Remove HTML tags
- Run the program for beautiful soup using python. The program will open the required URL, read the HTML texts from the webpage as a string, and delegate it to the HTML variable. As a result, a beautiful soup object is generated and is given to the soup variable.
- The beautiful soup object is generated with two arguments. The first argument has the HTML to be scraped, and the second argument has the string “html. parser” that represents Python’s HTML parser.
Scraping data by interacting with components from other websites.
The module ” url lib” is used to obtain a web page’s contents. Sometimes the contents are not displayed completely, and some hidden contents become inaccessible.
- The Python library does not have options to interact with web pages directly. A third-party package like Mechanical Soup can be used for this purpose.
- The Mechanical soup installs a headless browser, a browser with no graphic UI (User Interface). This browser can be controlled by python programs.
- To install Mechanical soup, run the following python program.
$ python3 -m pip install MechanicalSoup
- The pip tool displays the details of the installed package.
Purpose of web scraping
The following list shows the common purposes for which web scraping is done.
- Scraping the details of stock prices and loading them to the API app.
- Procure data from yellow pages to create leads.
- Scraping data from a store finder to identify effective business locations.
- Scraping information on the products from Amazon or other platforms for analyzing competitors.
- Scooping out data on sports for betting or entertainment.
- Parsing data on finance for studying and researching the market.
Data is everywhere, and there is no shortage of resourceful data. The process of converting raw data into a usable format has become simple and faster with the advent of new technologies in the market. Python’s standard library offers a wide variety of tools for web scraping, but those offered by PyPI simplifies the process. Scraping data can be used to create many exciting assignments, but it is particularly important to respect the privacy and conditions of the websites and to make sure not to overload the server with huge traffic.
If you would like to learn more about data science, we recommend you join our 12-month Executive Program in Data Science course from IIIT Bangalore, where you’ll be familiarised with machine learning, statistics, EDA, analytics, and other algorithms important for processing data. With exposure to 60+ projects, case studies, and capstone projects, you’ll master four programming tools and languages, including Python, SQL, and Tableau. You also stand to benefit from the peer-learning advantage that upGrad offers students by providing access to a learner base of over 40,000.
You’ll learn from India’s leading Data Science faculty & industry experts during the course of over 40 live sessions who will also provide 360° career support and counselling to help you get placed in top companies of your choice.