yt-meta 0.3.0

A Python library for finding video and channel metadata from YouTube.

yt-meta

A Python library for finding video and channel metadata from YouTube.

Purpose

This library is designed to provide a simple and efficient way to collect metadata for YouTube videos, channels, and playlists. It simplifies the process of interacting with YouTube's data, handling complexities like network requests, data parsing, and pagination, so you can focus on your analysis.

Architecture

yt-meta is designed around a Facade pattern. The main YtMeta class provides a simple, unified interface for all fetching operations. Internally, it delegates calls to specialized Fetcher classes, each responsible for a specific domain:

• VideoFetcher: Handles fetching video metadata and comments.
• ChannelFetcher: Manages fetching channel metadata, video lists, and shorts.
• PlaylistFetcher: Responsible for fetching playlist details.

This architecture keeps the codebase clean, organized, and easy to maintain.

Installation

This project uses uv for package management. You can install yt-meta from PyPI:

uv pip install yt-meta

To enable persistent caching, you need to install an optional dependency:

# For disk-based caching
uv pip install "yt-meta[persistent_cache]"

Core Features

The library offers several ways to fetch metadata.

1. Get Video Metadata

Fetches metadata for a specific YouTube video.

Example:

from yt_meta import YtMeta

client = YtMeta()
video_url = "https://www.youtube.com/watch?v=B68agR-OeJM"
metadata = client.get_video_metadata(video_url)
print(f"Title: {metadata['title']}")

2. Get Channel Metadata

Fetches metadata for a specific YouTube channel.

Example:

from yt_meta import YtMeta

client = YtMeta()
channel_url = "https://www.youtube.com/@samwitteveenai"
channel_metadata = client.get_channel_metadata(channel_url)
print(f"Channel Name: {channel_metadata['title']}")

3. Get All Videos from a Channel

Returns a generator that yields metadata for all videos on a channel's "Videos" tab, handling pagination automatically.

Example:

import itertools
from yt_meta import YtMeta

client = YtMeta()
channel_url = "https://www.youtube.com/@AI-Makerspace/videos"
videos_generator = client.get_channel_videos(channel_url)

# Print the first 5 videos
for video in itertools.islice(videos_generator, 5):
    print(f"- {video['title']} (ID: {video['video_id']})")

4. Get All Videos from a Playlist

Returns a generator that yields metadata for all videos in a playlist, handling pagination automatically.

Example:

import itertools
from yt_meta import YtMeta

client = YtMeta()
playlist_id = "PL-osiE80TeTt2d9bfVyTiXJA-UTHn6WwU"
videos_generator = client.get_playlist_videos(playlist_id)

# Print the first 5 videos
for video in itertools.islice(videos_generator, 5):
    print(f"- {video['title']} (ID: {video['video_id']})")

5. Get All Shorts from a Channel

Similar to videos, you can fetch all "Shorts" from a channel. This also supports a fast path (basic metadata) and a slow path (full metadata).

Fast Path Example:

This is the most efficient way to get a list of shorts, but it provides limited metadata.

import itertools
from yt_meta import YtMeta

client = YtMeta()
channel_url = "https://www.youtube.com/@bashbunni"
shorts_generator = client.get_channel_shorts(channel_url)

# Print the first 5 shorts
for short in itertools.islice(shorts_generator, 5):
    print(f"- {short['title']} (ID: {short['video_id']})")

Slow Path Example (Full Metadata):

Set fetch_full_metadata=True to retrieve all details for each short, such as like_count and publish_date.

import itertools
from yt_meta import YtMeta

client = YtMeta()
channel_url = "https://www.youtube.com/@bashbunni"
shorts_generator = client.get_channel_shorts(
    channel_url,
    fetch_full_metadata=True
)

# Print the first 5 shorts with full metadata
for short in itertools.islice(shorts_generator, 5):
    likes = short.get('like_count', 'N/A')
    print(f"- {short['title']} (Likes: {likes})")

6. Get Comments from a Video

Fetches comments for a specific video, with options for sorting and filtering. This method returns a generator that yields standardized comment data.

Example:

import itertools
from yt_meta import YtMeta
from yt_meta.fetchers import SORT_BY_POPULAR

client = YtMeta()
video_url = "https://www.youtube.com/watch?v=B68agR-OeJM"

# Find the most popular comments that have been liked by the creator
comment_filters = {
    "is_hearted_by_owner": {"eq": True}
}

comments_generator = client.get_video_comments(
    video_url,
    sort_by=SORT_BY_POPULAR,
    filters=comment_filters
)

print(f"Top 5 hearted comments for video: {video_url}")
for comment in itertools.islice(comments_generator, 5):
    likes = comment.get('like_count', 0)
    print(f"- \"{comment['text']}\" (Likes: {likes})")

Caching

yt-meta includes a flexible caching system to improve performance and avoid re-fetching data from YouTube.

Default In-Memory Cache

By default, YtMeta uses a simple in-memory dictionary to cache results. This cache is temporary and only lasts for the lifetime of the client instance.

client = YtMeta()
# The first call will fetch from the network
meta1 = client.get_video_metadata("some_url") 
# This second call will be instant, served from the in-memory cache
meta2 = client.get_video_metadata("some_url") 

Persistent Caching

For caching results across different runs or scripts, you can provide a persistent, dictionary-like object to the client. The library provides an optional diskcache integration for this purpose.

First, install the necessary extra:

uv pip install "yt-meta[persistent_cache]"

Then, instantiate a diskcache.Cache object and pass it to the client:

from yt_meta import YtMeta
from diskcache import Cache

# The cache object can be any dict-like object.
# Here, we use diskcache for a persistent, file-based cache.
persistent_cache = Cache(".my_yt_meta_cache")

client = YtMeta(cache=persistent_cache)

# The first time this script runs, it will be slow (fetches from network).
# Subsequent runs will be very fast, reading directly from the disk cache.
metadata = client.get_video_metadata("some_url")

Any object that implements the MutableMapping protocol (e.g., __getitem__, __setitem__, __delitem__) can be used as a cache. See examples/features/19_alternative_caching_sqlite.py for a demonstration using sqlitedict.

Advanced Features

Filtering Videos, Shorts, and Comments

The library provides a powerful filtering system via the filters argument, available on methods like get_channel_videos, get_channel_shorts, and get_video_comments. This allows you to find items matching specific criteria on the server side.

Robust Filter Validation

To improve the developer experience and prevent errors, yt-meta validates your filters dictionary before making any network requests. If you provide a filter field that doesn't exist, an invalid operator for a field, or an incorrect value type, the library will immediately raise a ValueError or TypeError.

This "fail-fast" approach saves you from waiting for a long-running query to complete only to find out there was a typo in your request. See examples/features/23_filter_validation.py for a demonstration.

Two-Stage Filtering: Fast vs. Slow

The library uses an efficient two-stage filtering process for videos and shorts:

• Fast Filters: Applied first, using metadata that is available on the main channel or playlist page (e.g., title, view_count). This is very efficient.
• Slow Filters: Applied second, only on items that pass the fast filters. This requires fetching full metadata for each item individually, which is much slower.

The client automatically detects when a slow filter is used and sets fetch_full_metadata=True for you.

[!NOTE] Comment filtering does not use the fast/slow system. All comment filters are applied after fetching the comment data.

Supported Fields and Operators

The following table lists all supported fields and their valid operators. The validation system will enforce these rules.

Field	Supported Operators	Content Type(s)	Filter Speed
title	contains, re, eq	Video, Short	Fast
description_snippet	contains, re, eq	Video	Fast
view_count	gt, gte, lt, lte, eq	Video, Short	Fast
duration_seconds	gt, gte, lt, lte, eq	Video, Short	Fast
publish_date	gt, gte, lt, lte, eq	Video, Short, Comment	Fast (Video), Slow (Short, Playlist)
like_count	gt, gte, lt, lte, eq	Video, Short, Comment	Slow
category	contains, re, eq	Video, Short	Slow
keywords	contains_any, contains_all	Video, Short	Slow
full_description	contains, re, eq	Video	Slow
text	contains, re, eq	Comment	N/A
is_by_owner	eq	Comment	N/A
is_reply	eq	Comment	N/A
is_hearted_by_owner	eq	Comment	N/A

[!NOTE] Some fields like publish_date can be "fast" for channel videos but "slow" for shorts or playlists because the basic metadata is not always available on those pages.

Example: Basic Filtering (Fast)

This example finds popular, short videos. Since both view_count and duration_seconds are fast filters, this query is very efficient.

import itertools
from yt_meta import YtMeta

client = YtMeta()
channel_url = "https://www.youtube.com/@TED/videos"

# Find videos over 1M views AND shorter than 5 minutes (300s)
adv_filters = {
    "view_count": {"gt": 1_000_000},
    "duration_seconds": {"lt": 300}
}

# This is fast because both view_count and duration are available
# in the basic metadata returned from the main channel page.
videos = client.get_channel_videos(
    channel_url,
    filters=adv_filters
)

for video in itertools.islice(videos, 5):
    views = video.get('view_count', 0)
    duration = video.get('duration_seconds', 0)
    print(f"- {video.get('title')} ({views:,} views, {duration}s)")

Example: Filtering by Date

The easiest way to filter by date is to use the start_date and end_date arguments. The library also optimizes this for channels by stopping the search early once videos are older than the specified start_date.

You can provide datetime.date objects or a relative date string (e.g., "30d", "6 months ago").

Using datetime.date objects:

from datetime import date
from yt_meta import YtMeta
import itertools

client = YtMeta()
channel_url = "https://www.youtube.com/@samwitteveenai/videos"

# Get videos from a specific window
start = date(2024, 1, 1)
end = date(2024, 3, 31)

videos = client.get_channel_videos(
    channel_url,
    start_date=start,
    end_date=end
)

for video in itertools.islice(videos, 5):
    p_date = video.get('publish_date', 'N/A')
    print(f"- {video.get('title')} (Published: {p_date})")

Using relative date strings:

from yt_meta import YtMeta
import itertools

client = YtMeta()
channel_url = "https://www.youtube.com/@samwitteveenai/videos"

recent_videos = client.get_channel_videos(
    channel_url,
    start_date="6 months ago"
)

for video in itertools.islice(recent_videos, 5):
    p_date = video.get('publish_date', 'N/A')
    print(f"- {video.get('title')} (Published: {p_date})")

Important Note on Playlist Filtering: When filtering a playlist by date, the library must fetch metadata for all videos first, as playlists are not guaranteed to be chronological. This can be very slow for large playlists.

Important Note on Shorts Filtering: Similarly, the Shorts feed does not provide a publish date on its fast path. Any date-based filter on get_channel_shorts will automatically trigger the slower, full metadata fetch for each short.

Logging

yt-meta uses Python's logging module to provide insights into its operations. To see the log output, you can configure a basic logger.

Example:

import logging

# Configure logging to print INFO-level messages
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

# Now, when you use the client, you will see logs
# ...

API Reference

YtMeta(cache: Optional[MutableMapping] = None)

The main client for interacting with the library. It inherits from youtube-comment-downloader and handles session management.

• cache: An optional dictionary-like object to use for caching. If None, a temporary in-memory cache is used.

get_video_metadata(youtube_url: str) -> dict

Fetches metadata for a single YouTube video.

• youtube_url: The full URL of the YouTube video.
• Returns: A dictionary containing metadata such as title, description, view_count, like_count, publish_date, category, and more.
• Raises: VideoUnavailableError if the video page cannot be fetched or the video is private/deleted.

get_video_comments(youtube_url: str, sort_by: int = SORT_BY_RECENT, limit: int = -1, filters: Optional[dict] = None) -> Generator[dict, None, None]

Fetches comments for a specific YouTube video. This is an "enrichment" call and is slower than fetching bulk metadata.

• youtube_url: The full URL of the YouTube video.
• sort_by: The sort order for comments. Use SORT_BY_RECENT (default) or SORT_BY_POPULAR.
• limit: The maximum number of comments to fetch. -1 means no limit.
• filters: A dictionary of filter conditions to apply (see filter table below).
• Returns: A generator that yields a standardized dictionary for each comment.

get_channel_metadata(channel_url: str) -> dict

Fetches metadata for a specific channel. Results are cached.

• channel_url: The URL of the channel.
• Returns: A dictionary with channel metadata like title, description, subscriber_count, vanity_url, etc.
• Raises: VideoUnavailableError, MetadataParsingError.

get_channel_videos(channel_url: str, ..., stop_at_video_id: str = None, max_videos: int = -1) -> Generator[dict, None, None]

Yields metadata for videos from a channel.

• start_date: The earliest date for videos to include (e.g., date(2023, 1, 1) or "30d").
• end_date: The latest date for videos to include.
• fetch_full_metadata: If True, fetches detailed metadata for every video. Automatically enabled if a "slow filter" is used.
• filters: A dictionary of advanced filter conditions (see above).
• stop_at_video_id: Stops fetching when this video ID is found.
• max_videos: The maximum number of videos to return.

get_playlist_videos(playlist_id: str, ..., stop_at_video_id: str = None, max_videos: int = -1) -> Generator[dict, None, None]

Yields metadata for videos from a playlist.

• start_date: The earliest date for videos to include (e.g., date(2023, 1, 1) or "30d").
• end_date: The latest date for videos to include.
• fetch_full_metadata: If True, fetches detailed metadata for every video.
• filters: A dictionary of advanced filter conditions.
• stop_at_video_id: Stops fetching when this video ID is found.
• max_videos: The maximum number of videos to return.

clear_cache()

Clears all items from the configured cache (both in-memory and persistent).

Error Handling

The library uses custom exceptions to signal specific error conditions.

YtMetaError

The base exception for all errors in this library.

MetadataParsingError

Library Architecture

For developers and contributors, it's helpful to understand the internal design of yt-meta. The library follows the Facade design pattern.

• YtMeta (The Facade): The main YtMeta class that you instantiate is the public-facing API. Its primary role is to delegate requests to the appropriate specialized "fetcher" class. It holds shared objects like the session and cache but contains no data-fetching logic itself.

• Fetcher Classes (The Subsystems):

  • VideoFetcher: Handles all logic related to individual videos, such as fetching metadata and comments.
  • ChannelFetcher: Manages fetching data from a channel's "Videos" and "Shorts" tabs, including handling pagination.
  • PlaylistFetcher: Responsible for retrieving video lists from a playlist.

This separation of concerns makes the library easier to maintain, test, and extend. If you need to fix a bug related to playlist parsing, for example, you know to look in yt_meta/fetchers.py in the PlaylistFetcher class.