Python

Reddit Scraper Code using Python & Reddit API

In this post, you will get Python code sample using which you can search Reddit for specific subreddit posts including hot posts. Reddit API is used in the Python code. This code will be helpful if you quickly want to scrape Reddit for popular posts in the field of machine learning (subreddit –  r/machinelearning), data science (subreddit – r/datascience), deep learning (subreddit – r/deeplearning) etc.  

There will be two steps to be followed to scrape Reddit for popular posts in any specific subreddits.

  • Python code for authentication and authorization
  • Python code for retrieving the popular posts

Check the Reddit API documentation page to learn about Reddit APIs.

Python code for Authentication / Authorization

Here is the code you would need to use for authenticating and authorization purposes. Here are the key steps:

  • Open an account in the reddit. The username and password will be used with login method having grant type as password.
  • Create an app by visiting the app page. Select the option script if you want to invoke the app from the Python script


  • Note the CLIENT_ID (as personal use script) and SECRET_TOKEN (as secret) in the image depicted with yellow color.


  • With above steps done, you are all set. Paste this code in your Jupyter notebook and execute to get the token which will be used to retrieve the subreddits popular posts.
import requests
 
# note that CLIENT_ID refers to 'personal use script' and SECRET_TOKEN to 'token'
auth = requests.auth.HTTPBasicAuth('qfK5a5tkC-bkV3adVR5d2w', 'AX01I3U9WQ4eSkGfK67kk4AEgkIKbM')
 
# here we pass our login method (password), username, and password
data = {'grant_type': 'password',
        'username': 'vitalflux',
        'password': 'vitalflux'}
 
# setup our header info, which gives reddit a brief description of our app
headers = {'User-Agent': 'vitalflux-pybot/0.0.1'}
 
# send our request for an OAuth token
res = requests.post('https://www.reddit.com/api/v1/access_token',
                    auth=auth, data=data, headers=headers)
 
# convert response to JSON and pull access_token value
TOKEN = res.json()['access_token']
 
# add authorization to our headers dictionary
headers = {**headers, **{'Authorization': f"bearer {TOKEN}"}}

Python code for retrieving the popular posts

  • Paste the following code to retrieve the popular (hot) posts for specific subreddit. In the code below, the subreddit, deeplearning is used. Make a note of params which is used to limit the number of posts which need to be retrieved. In the example below, the limit is set to 10.
params = {'limit' : 10}
res = requests.get("https://oauth.reddit.com/r/deeplearning/hot",
                   headers=headers,
                   params=params)
 
for post in res.json()['data']['children']:
  print('\n=========================================================================================================', 
        '\nTitle: ', post['data']['title'], 
        '\nUps: ', post['data']['ups'], ' -- Upvote ratio: ', post['data']['upvote_ratio'], 
        '\nText: ', post['data']['selftext'])

The following is what gets printed.

Putting it all together

Here is the entire Python code which can be used to retrieve the subreddit’s popular posts. Ensure to put your own username/password and, client id/secret token

import requests
 
# note that CLIENT_ID refers to 'personal use script' and SECRET_TOKEN to 'token'
auth = requests.auth.HTTPBasicAuth('qfK5a5tkC-bkV3adVR5d2w', 'AX01I3U9WQ4eSkGfK67kk4AEgkIKbM')
 
# here we pass our login method (password), username, and password
data = {'grant_type': 'password',
        'username': 'vitalflux',
        'password': 'vitalflux'}
 
# setup our header info, which gives reddit a brief description of our app
headers = {'User-Agent': 'vitalflux-pybot/0.0.1'}
 
# send our request for an OAuth token
res = requests.post('https://www.reddit.com/api/v1/access_token',
                    auth=auth, data=data, headers=headers)
 
# convert response to JSON and pull access_token value
TOKEN = res.json()['access_token']
 
# add authorization to our headers dictionary
headers = {**headers, **{'Authorization': f"bearer {TOKEN}"}}

# Print the subreddit popular posts
params = {'limit' : 10}
res = requests.get("https://oauth.reddit.com/r/deeplearning/hot",
                   headers=headers,
                   params=params)
 
for post in res.json()['data']['children']:
  print('\n=========================================================================================================', 
        '\nTitle: ', post['data']['title'], 
        '\nUps: ', post['data']['ups'], ' -- Upvote ratio: ', post['data']['upvote_ratio'], 
        '\nText: ', post['data']['selftext'])

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Recent Posts

Agentic Reasoning Design Patterns in AI: Examples

In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…

3 weeks ago

LLMs for Adaptive Learning & Personalized Education

Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…

4 weeks ago

Sparse Mixture of Experts (MoE) Models: Examples

With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…

1 month ago

Anxiety Disorder Detection & Machine Learning Techniques

Anxiety is a common mental health condition that affects millions of people around the world.…

1 month ago

Confounder Features & Machine Learning Models: Examples

In machine learning, confounder features or variables can significantly affect the accuracy and validity of…

1 month ago

Credit Card Fraud Detection & Machine Learning

Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…

1 month ago