How to scrape website data with Python?

Imagine that you want to search your competitors’ websites to gather information about their price lists. What would you do in this situation? Copying, pasting or entering data manually will cost you a lot of time and effort, not only that, but it can also easily cause errors. Luckily, you can easily automate this process using the Python programming language.

In this article, we will explore how to scrape websites using Python.

Differences between web scraping libraries

scrape website data with Python

Python is a very popular programming language for retrieving data from the web, thanks to the presence of many powerful third-party libraries. These libraries help us handle complex HTML structures, parse text, and interact with HTML forms with ease. Here are some of the top Python web scraping libraries we can use.

The urllib3 library is a powerful library for creating HTTP clients in Python. It helps us make HTTP requests easily in programs. urllib3 is capable of handling HTTP headers, retries, redirects, and other low-level details, which makes it a great tool for web scraping. Additionally, urllib3 also supports SSL verification, connection pooling, and authorization.

The BeautifulSoup library allows us to parse HTML and XML documents. With BeautifulSoup’s API, we can easily navigate through the HTML document tree and extract information from tags, meta titles, attributes, text, and other content. BeautifulSoup is also known for its powerful error handling capabilities, helping us handle unexpected situations flexibly.

The MechanicalSoup library helps automate the interaction between web browsers and websites effectively. It provides a high-level API for web scraping, simulating human behavior. With MechanicalSoup, we can interact with HTML forms, click buttons, and interact with elements like a real user. This helps us save time and effort when automating the process of retrieving data from the web.

The Requests library is an exceptionally powerful tool that enables Python programmers to effortlessly make HTTP requests. Its simplicity and user-friendly interface make it an ideal choice for developers seeking a hassle-free experience. With Requests, you can effortlessly execute GET and POST requests, seamlessly manage cookies, handle authentication, and leverage various other advanced HTTP functionalities. This exceptional library is widely acclaimed for its unrivaled simplicity and unparalleled ease of use, making it the go-to solution for retrieving data from the web.

Selenium library allows us to automate web browsers like Chrome, Firefox and Safari and simulate human interaction with websites. With Selenium, we can click buttons, fill out forms, scroll pages, and perform other actions like real users. This library is also widely used for web application testing and automating repetitive tasks.

Finally, the Pandas library allows us to store and manipulate data in a variety of formats, including CSV, Excel, JSON, and SQL. With Pandas, we can easily clean, transform, and analyze data extracted from web pages. This library is a useful tool to process data from the web efficiently.

In short, Python provides many powerful libraries to retrieve data from the web. Depending on your requirements, you can choose the right library to automate this process and save your time and effort.

How to scrape data from websites using Python?

Let’s learn the detailed step-by-step process to use Python to scrape data from websites.

Step 1: Select the website and its URL

First, we need to select the website for which we want to get data. In this tutorial, search for the website We will try to extract data about the highest rated movies on this website.

Step 2: Check the website

The next step is to understand the structure of the website. We need to understand the properties of the components we are interested in. Right-click on the web page and select “Inspect” to open the HTML code. Use the inspector tool to see the names of all the components we will use in the code.

Note the class names and ids of these elements, as they will be used in the Python code.

Step 3: Install important libraries

As mentioned earlier, Python has several web scraping libraries. In this article, we will use the following libraries:

request: to make HTTP requests to the website.
BeautifulSoup: to parse HTML code.
pandas: to store extracted data into a data frame.
time: to add delay between requests, to avoid overloading the site.

Install the libraries using the following command:

pip install requests pip install beautifulsoup4 pip install pandas

Step 4: Write Python code

Now, we will write the main Python code. The code will perform the following steps:

Use request to send an HTTP GET request to the website.
Use BeautifulSoup to parse HTML code.
Extract necessary data from HTML code.
Store information into pandas dataframe.
Add delay between requests to avoid overloading the site.

Here is an example of Python code to filter the highest rated movies from the IMDb site:

Step 5: Export extracted data

Now, we will export the extracted data as a CSV file. We can use pandas library to do this.

Step 6: Verify the extracted data

Open the CSV file to verify that the data was successfully scanned and stored.

We hope that this guide will help you collect data from websites easily and effectively.

How to scrape HTML forms using Python?

In order to scrape HTML forms utilizing Python, there exists a plethora of libraries at your disposal, including the likes of BeautifulSoup, lxml, and mechanize. Allow me to elucidate the general steps involved in this process:

Initiate an HTTP request directed towards the designated URL of the webpage housing the form you intend to scrape. As you await a response, the server graciously furnishes you with the HTML content encapsulating said webpage.

Once you have successfully accessed the aforementioned HTML content, you can employ the services of an HTML parser to pinpoint the precise location of the form that piques your interest. As an example, the illustrious BeautifulSoup’s “find()” method diligently assists you in locating the desired form tag.

Once the form has been successfully located, it is time to delve into the realm of extracting the invaluable input fields along with their corresponding values, all made possible by the prowess of the HTML parser. Take, for instance, BeautifulSoup’s “find_all()” method, which proves instrumental in unearthing all input tags ensconced within the form. Thereafter, you can meticulously extract their name and value attributes, a task of paramount importance.

Now armed with this invaluable data, you are bestowed with the power to either submit the form or embark upon further data processing endeavors, ultimately propelling your project to unfathomable heights of success.