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Using Python Web Scraping to Analyze Reddit Posts

If you’re interested in getting a unique data set consisting of user-generated posts, Python web scraping can help you get the job done. In this article, we’ll show you how to scrape text data from the web and give you inspiration about what to do with it.

Web scraping is the process of downloading data from the source code of a webpage. This data can be anything – text, images, videos, or even data in tables. Web scraping with Python can be a great way to get your hands on a unique dataset for your next data science project. However, there is no one-size-fits-all approach to web scraping. The Python libraries and methods you use will depend on the webpage and the information you want to download.

Why Use Python Web Scraping with Reddit?

Reddit is a social media site where users (called redditors) can post content on various subjects. This content could be text, images, or links to other content. These posts are organized into ‘subreddits’ like ‘r/science’ (where users can discuss the latest scientific findings) and ‘r/gaming’ (where lovers of gaming can connect and share content). The most popular subreddits have more members than some medium-sized countries have citizens!

As such, Reddit can be a valuable resource if you’re looking for advice and opinions. In this article, we’ll scrape some of this potentially valuable data, including the heading and posts from a subreddit.

This article is targeted at budding data analysts and others who already have some Python experience. Even if you know the fundamentals, there’s always more to learn. Our Data Processing with Python track includes 5 interactive courses designed to teach you everything from working with different data structures to writing different file types.

This is only one of our courses for more experienced programmers. To get an idea of what you can learn in our interactive courses, take a look at Learn How to Work with Files and Directories in Python for more information.

How Are Websites Built?

To be effective at web scraping, you need to know how websites are built. Websites are constructed with a combination of static and dynamic elements; this creates a complex environment to navigate when trying to scrape data. Static elements, such as HTML (HyperText Markup Language) and CSS (Cascading Style Sheets), provide the basic structure and styling of a webpage. They remain consistent each time the page is loaded. You can right-click any webpage and select ‘View Page Source’ to see the page’s static HTML content. It looks roughly like this:

	<!DOCTYPE html>
		<title>Webpage Title</title>
		<h1>Main Heading</h1>
		<h2>Secondary Heading</h2>
		<p>Paragraph text</p>

The structure includes a <head> section containing the title of the webpage and a <body> section. Inside the body section, there’s a main heading (<h1>), secondary heading (<h2>), and paragraph (<p>). (Most web pages have multiple secondary headings, as well as different heading levels (H3, H4, etc.). They also have more than one paragraph, as well as other elements like links, images, tables, and so on.) Each element is enclosed within an opening tag (<h1>) and a closing tag (</h1>); these tags define the beginning and end of the content they contain.

Dynamic elements, on the other hand, are usually written in JavaScript and other server-side scripts. These elements enable real-time updates and user interaction with the webpage – think live chat widgets, content feeds on social media platforms, or interactive forms which validate your input in real time. When you right click on a web page element and select ‘Inspect’, you can see these dynamic elements as they are rendered in real-time by the browser.

This dual nature of websites presents unique challenges and opportunities for web scraping. Scraping tools must effectively navigate and extract data from both static and dynamically generated content.

Using Python for Web Scraping

Python’s simplicity and useful libraries have made it a popular language for web scraping. Two of the most widely used tools for web scraping in Python are the requests library and the testing tool Selenium. The requests library is ideal for retrieving static content from websites. It allows developers to easily send HTTP requests and handle responses, making it perfect for straightforward scraping tasks where the data is readily available in the HTML source.

For more complex scraping tasks that involve interacting with dynamic content, Selenium is the tool of choice. It’s a powerful web automation framework that can simulate user actions like clicking buttons, filling forms, and scrolling, effectively mimicking a real user’s interaction with a web page. This makes it particularly useful for scraping sites that rely heavily on JavaScript to dynamically load content.

Selenium can work with various web browsers, providing a flexible solution for accessing and extracting data from the most interactive web pages. Take a look at the article Web Scraping With Python Libraries for more details and examples.

Scraping r/Python with the requests Library
Reddit can be a useful tool for those new to Python who are looking for information, advice, and other programmers to share information with. The r/Python subreddit is dedicated to discussions about the Python programming language and serves as an online hangout for Python enthusiasts of all skill levels. Members of the subreddit share a wide array of content, including tutorials, code snippets, project showcases, and industry news. It's a place where users can seek advice on coding challenges, explore new libraries and tools, and stay updated on the latest developments in the Python ecosystem. The collaborative nature of the community encourages continuous learning, making it a valuable resource for anyone looking to deepen their understanding of Python.

Get HTML elements

Let’s take advantage of this great resource and download some information. We’ll start off by getting some of the headings from the HTML data for the r/Python subreddit. We’ll use the requests library to send a GET request to retrieve the HTML for the web page. (HTTP (HyperText Transfer Protocol) is the protocol used for transmitting data over the web, and GET is an HTTP method that allows you to send a request for information to a server. The server returns a request status and,  if the request is granted, the information.) Then, with the help of the Beautiful Soup library, we’ll parse the HTML and extract the main headings. Start by installing the two libraries:

pip install requests

pip install beautifulsoup4

Now we can send our GET request to the target URL:

>>> import requests
>>> from bs4 import BeautifulSoup
>>> url = ''
>>> r = requests.get(url)

The object r is the response from the host server and contains the result of the GET request. To see if the request was successful, check the HTTP status code with:

	>>> print(r.status_code)

A 2xx status code indicates the request was successful. If you plan on using this in a script to automate this task, it’s a good practice to do some error handling by raising an exception if the status code isn’t 200.

Now we can extract the text and parse the HTML as follows:

text = r.text
html_data = BeautifulSoup(text, 'html.parser')

The main headings have the ‘h1’ HTML tag. We can find all of these elements using the find_all() method. This returns a list of all ‘h1’ elements. Then we can print the first element:

>>> h1_headings = html_data.find_all('h1')
>>> print(h1_headings[0].text)


Get Your Hands on Reddit Posts

Now we’re interested in getting the content of the posts on this subreddit. We’ll once again use the requests library to scrape the title and content of posts, along with a wealth of metadata such as timestamp, post scores, number of comments, and much more.

Here, we want to send a new GET request to the target URL. The posts can be sorted by ‘Hot’, ‘New’, ‘Top’, or ‘Rising’, which appears in the browser URL. If you append a ‘.json’ to the end of the URL address, the posts will appear in a JSON dataset instead of being rendered to the screen. This makes life much easier.

>>> base_url = ''
>>> subreddit = '/r/python'
>>> sort_by = '/hot'
>>> url = base_url + subreddit + sort_by + '.json'
>>> r = requests.get(url)

The JSON data structure is based on nestable key-value pairs, so it  resembles a Python dictionary. (You can learn more about working with JSON data in our How to Read and Write JSON Files in Python course.) The JSON data can be accessed by executing the following code:

>>> json_data = r.json()

The first post can be accessed by using the ‘data’ and ‘children’ keys, which returns a list. The first post is at index zero. A subset of this data is shown below:

>>> print(json_data['data']['children'][0])
{'kind': 't3',
 'data': {'approved_at_utc': None,
  'subreddit': 'Python',
  'selftext': "# Weekly Thread: What's Everyone Working On This Week? ???\n\nHello /r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea…

There’s a lot of information here. To extract a list of the posts and the associated metadata, just execute the following:

>>> posts = [post['data'] for post in json_data['data']['children']]

For each post, you can see the title (‘title’ key), the post content (‘selftext’ key) and the number of up and down votes (‘ups’ and ‘downs’ keys, respectively). The first post can now be accessed with:

>>> posts[0]
{'approved_at_utc': None,
 'subreddit': 'Python',
 'selftext': "# Weekly Thread: What's Everyone Working On This Week? ???\n\nHello /r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea…

If you’re interested in saving this to a file, you can do this using pandas. Just install the library with pip and do the following:

>>> import pandas as pd
>>> df = pd.DataFrame(posts)
>>> df.to_excel('r-python_posts.xlsx')

The final data set looks like this:

Using Python Web Scraping to Analyze Reddit Posts

Working with files is a fundamental skill for every Python programmer. For more information on working with different file types, read our article How to Write to File in Python. For more examples of using the requests library to download content, take a look at How to Download a File in Python.

Where Next with Python Web Scraping?

We’ve learnt how to scrape information from the r/Python subreddit. This is a valuable dataset created by your fellow Python programmers. You could use the number of up and down votes to find the best posts and read through them to find out what’s hot in the Python world. Or you could do a keyword search to find posts about job opportunities. This dataset could also form the basis of a larger natural language processing project. You could do a topic analysis to find the themes of the popular posts or use the up and down votes as labels to classify popular posts. There are many possible ways forward with this unique dataset.

There are also other ways to download content from the Internet. Our article cURL, Python requests: Downloading Files with Python shows additional examples of working with the requests library, as well as a little-known command line tool.

If you’re just starting out with data analysis in Python, check out our interactive Data Processing with Python track. It includes five interactive courses designed to teach you everything from working with different data structures to writing different file types.