Python is an attractive coding language as it's easier to use and has much less noise than you'd find in other coding languages.
You can use the language for various purposes, including programming and data analysis. You can also use it for web scraping. Python web scraping helps you to extract data from websites, such as the price of commodities and a list of items. The scraped data helps in better decision-making.
You can store all the data in a database or CSV file.
So, how do you scrape data from a web page using Python?
How Can You Use Python Code to Scrape Data?
Web scraping helps you to scrape public data from various web pages. In minutes, you can use a web scraper to extract massive data from public sites. Before you can start web scraping, you should look out for:
- The terms and conditions of the website. Understanding the terms and conditions helps you use the scraped data legally. Most websites don't allow the use of extracted data for commercial purposes.
- Also, ensure you don't do anything that can break the website. Downloading data rapidly and extracting large amounts of data may cause the website to break frequently. The site may also block you from extracting data there.
Inspecting a Website
Before trying to extract data from a site, it's helpful to find out whether it contains the links to the files you want to extract.
Inspecting a website and web scraping requires you to have a basic understanding of HTML. The files in a website are in various levels of HTML tags. Understanding HTML content will help you find relevant codes that contain your data.
To find out if the website has the relevant links, right-click on the site and go to "inspect." The process will allow you to find out the code behind the website.
A console with an arrow symbol on the top left will pop up when you click "inspect." If you want to see the code for a particular item, you should click the arrow symbol and an area on the website. The code will be highlighted in the console.
Developing a Web Scraper
A web scraper is an essential tool used in web scraping. It acts as a map that helps you identify the specific data you want to extract from a website.
Building a web scraper starts with installing Python programming language on your device. If installing it on Windows, ensure you check the "Path installation." The installation will add some executables to the Windows Command Prompt executable search.
Windows will then recognize various commands, including "pip" and "python," without the user's input.
Go to the Python Library
Python programming language stands out due to its massive selection of libraries used in data scraping from websites. The libraries include:
- Beautiful Soup
- Requests library
The Beautiful Soap library works by parsing HTML pages to extract data. The library can also convert an invalid markup into a parse tree.
However, the library cannot HTML documents since its purpose is parsing and not requesting data from servers.
Beautiful Soup helps in scraping data from the page source. It makes it possible to navigate the HTML tag though it still needs a parser.
Some python libraries cannot extract data from dynamic web pages. Selenium comes in handy as it helps extract data from static web pages.
Selenium is an open-source data automation tool that helps automate various processes, including logging into social media platforms.
Selenium needs three components to function, including:
- The Selenium package
- A web browser: the supported web browsers include Firefox, Safari, Edge, Google Chrome
- Browser drivers
You can install the selenium package from the terminal:
pip install selenium
After installing the selenium package, you can import the appropriate class for the browser. You'll then need to create an object of the browser class that requires the driver executable's path. For example:
from selenium.webdriver import Chrome driver = Chrome(executable_path='/path/to/driver')
After that, you can use the get() method to load your desired web page into the browser.
Selenium allows you to use XPath and CSS selectors to extract HTML elements.
Therefore, you should use selenium if you don't mind the slow speed or you are not extracting data on a large scale.
If you want to scrape websites, you start by sending HTTP requests to the server. The server then returns a response with your desired data.
Standard Python HTTP libraries require bulky lines of code to be effective in data extraction, making them challenging to use.
However, the requests library makes it easier to send HTTP requests by minimizing the lines of code. It makes it easier for users to understand and debug the requests without affecting their effectiveness.
You can use the pip command to install the requests library from within the terminal:
pip install requests
One downside of the requests library is that it doesn't parse the scraped HTML data. It makes it difficult to manipulate and analyze scraped data since it doesn't convert it into a more readable format.
The lxml library is an easy-to-use, powerful, and fast Python library that you can use to parse HTML and XML files.
If you want to extract data from large datasets, the lxml Python library is excellent.
However, it features poorly designed HTML, which affects its parsing capabilities.
You can use the pip command to install the lxml Python library:
pip install lxml
To work with HTML, XML requires a HTML string. You can use the Requests Library to retrieve the HTML string.
After getting the HTML string, you can use the fromstring method to build a tree. For instance:
After response = requests.get()
from lxml import html
tree = html.fromstring(response.text)
you can then use XPath to query the tree object, which will return all the HTML elements that match the XPath.
Can You Scrape a Website Using Python?
If you have a web scraping project, a Python file will help you extract valuable data from each web page.
Unlike other coding languages, Python doesn't require you to have an advanced level of coding. It makes it easy for you to carry out various tasks, such as how to implement web scraping and import requests.
Preparing developer tools for scraping websites is also effortless while using Python. The programming language features several libraries such as XML and selenium, making it easy to create web scrapers.
Is Python the Best Option for Web Scraping?
To decide whether a language is the best for scraping websites, you should consider various factors. Such factors include:
- Ease of use
- Web scraping efficiency
- Ability to convert data to a readable format for analysis and data manipulation
Although several coding languages exist, Python is the best language for website data extraction. It's an all-rounded language that helps handle most of the processes, including post requests and request data.
The coding language offers several libraries, including selenium, Beautiful Soup, lxml, and Requests Library, that help in data extraction. The highly evolved Python libraries make it the best for scraping websites.
Additionally, the Python syntax isn't challenging to learn and understand. Writing Python code is as simple as making a statement in English. You can also carry out enormous tasks with a simple line of code.
Can You Be in Trouble for Web Scraping?
Is web scraping legal or illegal?
Scraping websites to extract valuable data isn't entirely illegal. However, before you can scrape a website, it'll be best to check the website's terms to see if it allows web scraping or not.
Some companies have court orders barring data extraction from their websites.
Even if a website doesn't prohibit scraping, you should be careful with how you scrape data from the site. Rapid data extraction can cause a site to break. A website can bar you from scraping data if you do it frequently or maliciously.
The use of scraped data for commercial purposes is not allowed. You can use the data for market research and other non-commercial purposes.
Final Word on How to Web Scrape With Python
Web scraping with Python is a straightforward process that may not require you to have advanced coding language. However, you require a basic understanding of HTML. It'll be best to understand the Python libraries to select the best choice for your function.