Google Spreadsheet-generated CSV -> Reddit Flair Python script

Gist af4d272b0b59fb00bf383b183eb0cc52

This example shows an automated pipeline that fetches and reposts top Twitch clips to a subreddit, using a Streamable link as the video source. The code uploads a local video file to Streamable via the `pystreamable` API, polls until the upload reaches 100% processing, and then submits the resulting URL as a Reddit post titled "Top Twitch Clips for This Week." It relies on the `praw` library for Reddit interactions, `requests` and `json` for API calls, `datetime` for timestamps, and `fake_useragent` to rotate user-agent strings. Environment variables supply all credentials, and the script exits if the upload does not complete within six polling attempts.

54% match

Gist a96c926e932dc1beb8054857775aae2d

This example shows a Python script that automatically manages a Fantasy Premier League team by calculating optimal transfers and lineups. It retrieves player data, evaluates potential transfers, and submits changes to the FPL API. The code uses the `requests` library to handle API calls and `pandas` for data manipulation, including pivot tables and filtering based on team constraints. It also demonstrates how to update team sheets and set captaincy through HTTP POST requests. The visualization focuses on the logic and data flow rather than graphical output.

Gist dfbc4b41961e5b098fffce6af3828f57

This visualization scrapes Instagram profile data to compare follower counts, post engagement, and captions across influencers in different industries. Using Selenium to automate Chrome and BeautifulSoup to parse page JSON, the script extracts post dates, like counts, comments, and partnership flags from each profile’s embedded `_sharedData` blob. It then compiles the results into a pandas DataFrame and exports them to CSV. The example highlights the workflow of collecting social-media metrics at scale, with the final chart likely plotting follower growth or engagement trends per industry.

AArizonaTay

Gist 7083cd8264b6aa48ce4bbfaf44546f07

This example builds a profile-scraping pipeline for social media data, turning raw CSV input into a structured DataFrame with fields for user names, follower counts, post dates, likes, comments, captions, and industry tags. The code reads `raw_data.csv` via `pandas.read_csv` and then initializes an empty DataFrame with predefined column names, preparing it for subsequent population. The visualization focuses on the data-preparation stage rather than graphical output, demonstrating how pandas handles tabular ingestion and schema definition. No rendering library like d3 or Three.js appears here; instead, the emphasis is on clean data structuring for later analysis or charting.

AArizonaTay

Gist a99d287be62c6d73536521c0e5e74e58

This example scrapes Instagram profile and post data to track follower counts, likes, comments, captions, and post dates for selected users, associating each entry with an industry topic. The script uses Selenium to automate login and search, BeautifulSoup to parse the page source, and the Instagram GraphQL data embedded in `window._sharedData` to extract metrics. It writes the collected records to a CSV via pandas, pausing with random sleeps to avoid detection. The visualization highlights how social-media engagement varies across profiles and industries over time.

AArizonaTay

Gist de36ebb099ca71e1ac63f67335f7e71e

This example shows an automated pipeline that fetches the most popular Twitch clips of the week, concatenates them into a single video, and uploads the result to Streamable and Reddit. It uses the Twitch Kraken API to retrieve clip metadata and download the actual video files, then MoviePy to resize and concatenate the clips with a fixed intro. The script relies on the `requests` library for HTTP calls, `praw` for Reddit posting, and the `pystreamable` API for uploads. Environment variables supply authentication keys and configuration, while the final video is saved with a timestamped filename before being shared.

52% match

Gist dca53ad15bb437468b52eb951a09f356

This visualization shows the data pipeline for managing Fantasy Premier League player data, fetching gameweek deadlines and team rosters from the official FPL API. It retrieves data using Python's `requests` library, converts JSON responses into DataFrames with `pandas`, and then persists the processed data to Amazon S3 through the `boto3` client. The code also reads configuration parameters from AWS SSM and sends notifications via SNS topics, demonstrating an automated workflow for updating and storing fantasy football statistics.

52% match