How to export Reddit data to CSV

What is in the exported CSV

Every Reddscan export ships two clean column sets — one shape for posts, one for comments — so you can analyze each without parsing nested JSON. The columns mirror the Reddit API fields, normalized to plain types (no nested objects, no markdown HTML), and timestamps come in both Unix epoch and ISO 8601.

Skip the JSON-to-dataframe gymnastics. The CSV is already flat, normalized, and UTF-8 — open it in pandas with a one-liner.

Three methods compared

Reddit data export tools split into three approaches. API-backed dashboards (Reddscan) hide auth + rate-limiting behind a UI and ship clean CSVs. Code-based libraries (PRAW for Python, snoowrap for Node, PullPush for archival search) give you total control at the cost of setup time. DOM-scraping browser extensions read whatever is on screen — useful for a one-thread dump, useless for anything else.

The fastest method depends on what you optimize for. If you want a CSV in your downloads folder in under a minute, use the dashboard. If you live in a Jupyter notebook and want to script the export end-to-end, use Python. The browser extension path is a trap for anything beyond a single visible thread — covered below for completeness.

Method 1: Reddscan dashboard

The dashboard path is two clicks away from having nicely formatted data. Open the Extract tool, pick what you want, configure the limits, kick off the job, and the CSV is in your downloads folder by the time you finish reading this paragraph.

1. Step 1

   Sign in and open Extract

   Create an account at reddscan.com/signup if you do not have one yet, then go to /dashboard/extract. Extract is the dedicated page for one-off CSV exports — separate from the continuous keyword monitors, which run on a different cadence.
2. Step 2

   Pick a data source

   Four sources are supported: a whole subreddit (e.g. r/SaaS), every post + comment from a single user profile, all comments from one post URL, or a keyword search across all of Reddit. Pick the tab that matches the data you need.
3. Step 3

   Configure the export

   Pick the content type (posts, comments, or both), the sort order (new / hot / top / controversial), the time filter (only relevant for top / controversial), and a limit up to 1,000 items. The defaults — posts, hot, no time filter, 1,000 items — are sane for most exploratory exports.
4. Step 4

   Kick off the job, watch progress

   Click Start. The job is queued, deduplicated as items come in, and progress updates in real time over a WebSocket — you do not need to refresh or poll. Most exports finish in well under a minute. Exports from very active subreddits can take 1-2 minutes.
5. Step 5

   Download the CSV

   When the status flips to Completed, the Download button appears. Two files come back if you exported both posts and comments — one CSV each, named with the source + timestamp so you can chain multiple exports in one folder without collisions.

10-Day Pass

The $9 10-Day Pass unlocks full Starter limits: up to 1,000 items per single export with no monthly ceiling, plus all 5 notification channels. After 10 days you can upgrade to a subscription or buy another pass. See the pricing page for the full breakdown.

Method 2: Python + PRAW

If you live in a Jupyter notebook and want the export to be part of a larger pipeline, PRAW (Python Reddit API Wrapper) is the standard. It wraps the Reddit API in clean Python objects, handles pagination, and respects the rate limit for you. You will need Python 3.x, pip install praw, and a Reddit app from reddit.com/prefs/apps for credentials.

Here is a minimal script that pulls the top 100 posts of r/technology into reddit_export.csv:

import praw
import csv

# instantiate the reddit client with your app credentials.
# create a script-type app at reddit.com/prefs/apps to get these.
reddit = praw.Reddit(
    client_id     = 'YOUR_CLIENT_ID',
    client_secret = 'YOUR_CLIENT_SECRET',
    user_agent    = 'reddit-csv-export/1.0 by u/your_username'
)

# pick the subreddit + listing type. swap .hot for .top, .new,
# or .controversial; pass time_filter='month' for top/controversial.
subreddit = reddit.subreddit('technology')
posts     = subreddit.hot(limit = 100)

# stream the rows out to a utf-8 csv. praw paginates internally,
# so you can ask for any limit up to 1000 in a single call.
with open('reddit_export.csv', 'w', newline = '', encoding = 'utf-8') as f:

    writer = csv.writer(f)
    writer.writerow([
        'post_id', 'subreddit', 'author', 'title',
        'score', 'num_comments', 'created_utc', 'permalink'
    ])

    for post in posts:
        writer.writerow([
            post.id,
            post.subreddit.display_name,
            str(post.author) if post.author else '[deleted]',
            post.title,
            post.score,
            post.num_comments,
            post.created_utc,
            'https://reddit.com' + post.permalink
        ])

print('Export complete.')

When PRAW falls short

PRAW gives you posts cleanly. Comment trees are recursive (replies have replies have replies), and flattening them into a CSV is meaningfully more code — usually a depth-first walk with a parent-comment column. PRAW also caps keyword search at the ~250 most recent results, so for historical keyword exports the API path simply can not get you what the dashboard pipeline does.

Method 3: Browser extensions

A handful of Chrome and Firefox extensions advertise "export Reddit to CSV." They all work the same way: scrape the DOM of the page you have open, parse out post cards, and serialize them. That is fine for a one-off dump of a specific thread that you can already see on screen, but every other use case hits a wall.

The limits:

• Only what you see. If a subreddit has 12 months of posts, you get the ~25 visible on the page. To export more you have to manually scroll-load, which Reddit throttles aggressively.
• No keyword search. Extensions cannot reach across all of Reddit — they only see the DOM of the current tab.
• Brittle. Every Reddit UI tweak (the redesign, new comment widget, mobile-web variants) breaks the selectors. Extensions go stale within weeks of any Reddit ship.
• Privacy. Some Chrome-store extensions in this category have been caught exfiltrating browsing history. Audit the manifest permissions before installing.

For anything beyond a one-off dump of a single thread you can already see in your browser, skip the extension path.

Working with the CSV

Four things to know before you load the file into your tool of choice. They save you the "why are my dates from 1970 and my emojis broken" afternoon.

Frequently asked questions

Keep reading

Now that the data is in CSV, the natural next reads are about what to do with it.