Welcome back to our journey in video scraping! In previous lessons, you've learned how to transcribe videos using gpt-4o-transcribe and PyDub, as well as download videos from public Google Drive links. In this lesson, we'll take things further by downloading videos from LinkedIn using yt-dlp. This browser-based tool simplifies accessing video content across multiple platforms.

In this lesson, you will:

• Identify and validate a range of LinkedIn URLs.
• Discover how yt-dlp facilitates downloading from LinkedIn.
• Manage temporary files and address potential legal concerns when downloading videos.

Our objective is to leverage yt-dlp, which was initially designed for YouTube but is flexible enough for use with LinkedIn. The key is recognizing valid LinkedIn URLs and downloading videos efficiently.

LinkedIn URLs can be in formats such as:

• Full length: https://www.linkedin.com/feed/update/urn:li:activity:VIDEO_ID
• Post based: https://www.linkedin.com/posts/USERNAME_activity-VIDEO_ID

Understanding these structures is crucial for initiating the download process.

For your own projects, you'll need to install yt-dlp using pip:

Note that yt-dlp is already pre-installed in the CodeSignal environment, so you can use it directly in the practice exercises without any additional setup.

We'll start by verifying whether a URL belongs to LinkedIn:

This function checks for linkedin.com in the URL's net location and confirms whether a recognizable path is present, ensuring accurate URL validation.

The urlparse() function breaks down the URL into components like netloc (network location/domain) and path (the part after the domain). The parsed.netloc contains the domain portion, while parsed.path contains the URL path. The any() function returns True if at least one of the valid path patterns is found in the URL path.

Once the URL is validated, we proceed with the download using yt-dlp:

Here's how it works:

• A temporary directory holds the video.
• The output is organized with the video's title and format (mp4).
• yt-dlp options specify the format, templating, quiet download, and hooks to track progress.

The tempfile.mkdtemp() creates a unique temporary directory that will be automatically cleaned up later. The output_template uses yt-dlp's template syntax where %(title)s gets replaced with the video title and %(ext)s with the file extension. The progress_hooks parameter accepts a list of callback functions that get called during download to report status updates. The with statement ensures the yt-dlp object is properly closed after use, and os.listdir() lists all files in the temporary directory to find our downloaded video.

Mastering LinkedIn video downloads with yt-dlp enables the collection of educational videos, supports offline access, and aids in backing up personal content. Always be aware of potential legal issues, ensuring compliance with terms of service and copyright laws.

Now that you understand the downloader's potential, take the upcoming practice section as an opportunity to solidify your knowledge with hands-on tasks.