tubeframes 0.3.2

A Python package for retrieving YouTube data, including video statistics, captions, and channel information. TubeFrames outputs results in a user-friendly pandas DataFrame format, making it ideal for data analysis workflows — especially in Jupyter Notebooks.

TubeFrames - A YouTube Data Analysis Library

A Python package for retrieving YouTube data, including video statistics, captions, and channel information. TubeFrames outputs results in a user-friendly pandas DataFrame format, making it ideal for data analysis workflows — especially in Jupyter Notebooks.

Table of Contents

• TubeFrames - A YouTube Data Analysis Library
  • Table of Contents
  • Features
  • Attribution
  • Setup
    • Requirements
    • API Key Setup
      • Setting as Environment Variable
    • Installation
  • Usage
    • Basic Search
    • Working with Captions
    • Channel Search
    • Channel Information
  • Parameter Reference
    • Search Class
    • ChannelInfo Class
  • Applications
  • Contributing
  • License

Features

• 🔍 YouTube Search: Query and retrieve results in DataFrame format
• 📊 Video Statistics: View counts, likes and comments count
• 📝 Caption Extraction: Extract video captions in multiple languages
• 📺 Channel Information: Data collection from specific channels

Attribution

This project uses the YouTube Data API and is not affiliated with or endorsed by YouTube or Google. All YouTube content and trademarks are the property of their respective owners.

Setup

Requirements

• Python 3.6+
• YouTube Data API key
• Required dependencies are installed automatically

API Key Setup

To use tubeframes, create a YouTube Data API key following the official Google documentation.

Setting as Environment Variable

Linux: edit ~/.profile and add:

export YOUTUBE_DEVELOPER_KEY=<YOUR_YOUTUBE_DEVELOPER_KEY>

Windows: Set via System Properties → Environment Variables (under User variables)

Installation

pip install tubeframes

Usage

Basic Search

Create a search object to retrieve video information:

import tubeframes as yt
tubeframes_search = yt.Search("Test", developer_key=<YOUR_YOUTUBE_DEVELOPER_KEY>)
tubeframes_search.df  # DataFrame with YouTube infos (likes, views, title, etc.)

Results include:

videoId	publishedAt	channelId	title	…	viewCount	likeCount	favoriteCount	commentCount
abcde1234	2021-06-01 10:00:00+00:00	abcde1234abc	Video title example 1	…	100000	6000	0	200
abcde1235	2021-06-01 11:00:00+00:00	abcde1234abc	Video title example 2	…	200000	5000	1	210
abcde1236	2021-06-01 12:00:00+00:00	abcde1234abd	Video title example 3	…	100000	4000	0	150

Working with Captions

To include video captions in your results, use the argument captions=True:

import tubeframes as yt
# YOUTUBE_DEVELOPER_KEY is not necessary if set as environment variable
tubeframes_search = yt.Search("Test", caption=True)
tubeframes_search.df  # A new column with captions "video_caption" will appear

Results with captions:

videoId	publishedAt	channelId	title	…	commentCount	video_caption
abcde1234	2021-06-01 10:00:00+00:00	abcde1234abc	Video title example 1	…	200	What they say; words and more words; thanks for watching
abcde1235	2021-06-01 11:00:00+00:00	abcde1234abc	Video title example 2	…	210	None
abcde1236	2021-06-01 12:00:00+00:00	abcde1234abd	Video title example 3	…	150	Words and more words and more words; thanks for watching
…	…	…	…	…	…	…

Channel Search

To search for channels instead of videos:

import tubeframes as yt
tubeframes_search = yt.Search("Test", item_type="channel")
tubeframes_search.df  # DataFrame with YouTube channel information

Channel search results:

channelId	publishedAt	title	description	channelTitle	publishTime
abcde1234abc	2021-06-01 10:00:00+00:00	Example channel 1	Description of example channel 1	Example channel 1	2021-06-01 10:00:00+00:00
abcde1234abd	2021-06-01 11:00:00+00:00	Example channel 2	Description of example channel 2	Example channel 2	2021-06-01 11:00:00+00:00

Channel Information

To get information and captions from videos of specific channel(s), use the ChannelInfo class:

import tubeframes as yt
channel_info = yt.ChannelInfo(
    channel_ids=["<A CHANNEL ID>"],
    max_results=10,
    accepted_caption_lang=['pt', 'en'],
)
channel_info.df  # DataFrame with video information and captions

Channel information results:

channelId	videoId	title	publishedAt	caption	thumbnailUrl
EXAMPLE_CHANNEL_ID1	EXAMPLE_VIDEO_ID1	Example Video Title 1	2025-03-22 22:00:39+00:00	Example caption text; More example text; Thanks...	https://example.com/sddefault.jpg
EXAMPLE_CHANNEL_ID1	EXAMPLE_VIDEO_ID2	Example Video Title 2	2025-03-22 18:00:22+00:00	Example caption text; Follow us on social media...	https://example.com/maxresdefault.jpg

Parameter Reference

Search Class

The Search class accepts the following arguments:

Parameter	Type	Required	Default	Description
term	string	Yes	-	YouTube search term
caption	boolean	No	False	Whether to include video captions
maxres	integer	No	50	Maximum number of results to return
accepted_caption_lang	list	No	['en']	List of accepted languages for captions
item_type	string	No	"video"	Type of search: "video" or "channel"
developer_key	string	No	-	YouTube API key (optional if set as environment variable)

Example with all parameters:

import tubeframes as yt

tubeframes_search = yt.Search(
    term="Python Tutorial",
    caption=True,
    maxres=100,
    accepted_caption_lang=['pt', 'en'],
    item_type="video",
    developer_key="<YOUR_DEVELOPER_KEY>"
)

# Access the resulting DataFrame
df = tubeframes_search.df

ChannelInfo Class

The ChannelInfo class accepts the following arguments:

Parameter	Type	Required	Default	Description
channel_ids	string/list	Yes	-	Channel ID or list of channel IDs
max_results	integer	No	10	Maximum number of results per channel
accepted_caption_lang	list	No	['pt', 'en']	List of accepted languages for captions
developer_key	string	No	-	YouTube API key (optional if set as environment variable)

Example with all parameters:

import tubeframes as yt

channel_info = yt.ChannelInfo(
    channel_ids=["<CHANNEL ID 1>", "<CHANNEL ID 2>"],
    max_results=20,
    accepted_caption_lang=['pt', 'en', 'es'],
    developer_key="<YOUR_DEVELOPER_KEY>"
)

# Access the resulting DataFrame
df = channel_info.df

Applications

TubeFrames is particularly useful for:

• Sentiment Analysis: Extract captions for sentiment analysis
• Text Mining: Identify keywords and topics from YouTube channels
• Academic Research: Dataset creation for video engagement studies
• Content Marketing: Channel performance analysis and strategy optimization
• Competitor Research: Tracking metrics of competitor channels

Contributing

Contributions are welcome! Open an issue or submit a pull request on GitHub.

License

This project is licensed under the GNU General Public License v3.0.