Skip to main content
SimilarExport ZIP
100%

getting options

✓ Published0🌍 Public
KKristinHenry
Last edited Jun 26, 2018
Created on Jun 26, 2018

This example shows a Python script that automates downloading videos and embeds view counts directly into the saved filenames. The code iterates through a list of URLs, constructing a command for the `youtube-dl` library that specifies an output template using `%(title)s-%(view_count)s`. It then invokes the command through `subprocess.call` with the shell enabled, effectively fetching each video and naming the file according to its title and popularity metric as reported by the service. The visualization of this data would be derived from the resulting file names, reflecting the relationship between content and engagement.

AI-generated description

Similar vizzes

Loading thumbnail…

looping over list of video links for downloading

This example shows a bare-bones automation script that downloads a series of videos by iterating through a list of URLs. It constructs a shell command using the `youtube-dl` tool for each link, then executes it via Python’s `subprocess.call` function with `shell=True`. The script relies solely on Python’s standard library, with no external visualization or plotting libraries. The data source is the hardcoded `links` list, and the rendering approach is simply terminal output from the download commands.

KKristinHenry
77% match
Loading thumbnail…

Download youtube video to mp4

This example demonstrates how to download a YouTube video as an MP4 file using the `pytube` library, then shows the final step of saving the file locally with a simple print confirmation. The script first targets a specific video URL and filters available streams for progressive MP4 formats, ordering them by resolution in descending order to select the highest-quality option. After downloading, the code would rely on `moviepy.editor` and `eyed3` for potential post-processing, though the visible portion only handles the download itself. The example uses the `urllib` and `csv` modules implicitly, but the core action centers on `pytube`'s stream API.

WWill Carter
57% match
Loading thumbnail…

Gist 77c69eeb802c8bd617a1f30df0f71e93

This example shows an automated video-pipeline system that fetches video content, edits it, and distributes the result to multiple platforms. The code’s main script orchestrates three distinct stages: retrieving source videos via `get_videos`, processing them through `edit_video`, and finally uploading the finished output—first to Streamable through `upload_to_streamable` and then to Reddit via `upload_to_reddit`. No visualization libraries are used; instead, the concrete APIs are the custom modules `get_videos`, `edit_videos`, and `upload_video`, which handle data acquisition, editing logic, and remote uploads. The gist demonstrates a file-based workflow, with no graphical rendering or interactive charts.

CConorAspell
55% match
Loading thumbnail…

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.

CConorAspell
55% match
Loading thumbnail…

Gist 4eaba7d2be548a560bf0730b6aabf8e7

This example downloads and plays a sequence of Twitch clips, using the Twitch Kraken API to fetch the top clips based on the specified period and mode. It constructs a URL with the environment-specified client ID and query parameters, then parses the JSON response to collect clip URLs while limiting the total duration to under two minutes. The `requests` library downloads each MP4 file to a temporary directory for playback. The visualization demonstrates a straightforward data-fetching pipeline, where the clip list is assembled from the API response and stored locally, without any graphical rendering or charting library involved.

CConorAspell
53% match
Loading thumbnail…

Trending YouTube Video Statistics (US Video)

This visualization displays a raw JSON representation of the first 1000 rows of YouTube trending video statistics from August 2020, loaded from a GitHub-hosted CSV file. The code uses d3.csvParse to fetch and parse the dataset, then d3.select to render the parsed data as formatted text within a <pre> element. The visualization demonstrates a simple data-loading workflow where the state transitions from undefined to a loading message before displaying the final array of video objects.

Fflanagancarlie@gmail.com
51% match
Loading thumbnail…

Gist a803adce92b47f80c229d8375adfa195

This example demonstrates an automated pipeline that fetches Twitch clips, edits them into a compilation video, and uploads the result to Streamable, with Reddit integration for sharing. The workflow is orchestrated through a series of Python scripts (`get_videos.py`, `edit_videos.py`, `upload_video.py`, `main.py`) running inside a Docker container, using environment variables for configuration like `twitch_id` and `streamable_username`. The code relies on the `requests` library for API calls to Twitch and Streamable, while video editing is performed programmatically, though the specific editing library is not visible in the provided files. The container sets up a Python 3 environment and installs dependencies from `requirements.txt`, with all credentials and settings passed via `env.list`.

CConorAspell
51% match
Loading thumbnail…

On Surftourist.com we've had some people putting in a vimeo or youtube link in their descriptions. We want to support video properly, but this is a quick attempt at addressing the solution for the meantime.

This example shows how user-submitted descriptions on Surftourist.com can automatically detect YouTube and Vimeo links and replace them with embedded video players. The code searches an element’s HTML for matching URLs using a regular expression, then for each match it queries either Vimeo’s oEmbed API or Embedly’s oEmbed endpoint (for YouTube) via JSONP, and finally swaps the original link with the returned HTML embed code. The rendering approach relies on jQuery’s `$.ajax` for cross-domain requests, with a maximum embed height of 300 pixels.

DDylanFM
50% match