ytapi-kit

Python helpers for YouTube's Analytics, Reporting, and Data APIs.

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Table of Contents

• Overview
• Installation
• Authentication
• Data API
• Analytics API
• Reporting API
• Roadmap
• Contributing
• License

Overview

ytapi-kit is a single Python wrapper around YouTube's Data, Analytics, and Reporting APIs.

Under the hood the library is organized around three client classes - DataClient, AnalyticsClient, and ReportingClient, each containing 1-to-1 methods that mirror Google's REST endpoints (e.g. reports_query(), list_videos(), list_jobs(), etc.)

In addition to those low-level calls, we have added functions that pre-fill the most common parameters and return tidy pandas DataFrames in a single line of code (e.g. video_geographies(), get_latest_report(), etc.). More details are provided in subsequent sections.

Installation

python -m pip install ytapi-kit

# For the development version:
git clone https://github.com/davisj95/ytapi-kit.git
cd ytapi-kit && python -m pip install -e '.[dev]'

Requires Python ≥ 3.9. Dependencies (pandas, google-auth, requests) install automatically.

Authentication (OAuth 2.0)

While Google allows several authentication methods (API key, OAuth 2.0, etc.), currently this package uses OAuth 2.0 since all three APIs support OAuth.

1. Create a project in Google Cloud Console → enable YouTube Data. Analytics, and Reporting APIs (or whichever ones are applicable for your needs).
2. Download OAuth client secrets JSON → save as client_secrets.json

from ytapi_kit import user_session, AnalyticsClient

session = user_session("client_secrets.json")  # browser popup when authentication required
yt = AnalyticsClient(session)

ytapi-kit caches/refreshes tokens automatically (default ~/.ytapi.pickle).

Quickstart

from ytapi_kit import user_session, AnalyticsClient, DataClient, ReportingClient

session = user_session("client_secrets.json")

# 1) Analytics: fetch daily views for a video
yt_analytics = AnalyticsClient(session)
views = yt_analytics.video_stats(
    video_ids="dQw4w9WgXcQ",
    start_date="2023-01-01",
    end_date="2023-02-01",
)
print(views.head())

# 2) Data: lookup video metadata
yt_data = DataClient(session)
meta = yt_data.video_metadata("dQw4w9WgXcQ")
print(meta["title"], meta["viewCount"])

# 3) Reporting: get latest channel_basics_a2 report
yt_reporting = ReportingClient(session)
report_types = yt_reporting.get_latest_report("channel_basics_a2")
print(report_types.head())

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Data API

The YouTube Data API lets you discover, inspect, create, update, or delete nearly every YouTube resource—videos, channels, playlists, comments, and more. You interact with it through the DataClient, which exposes both:

• 1‑to‑1 endpoint wrappers (list_videos(), list_playlists(), list_comments(), etc.) for advanced users who need every optional parameter, and

• Convenience helpers like video_metadata() and channel_videos() that hide pagination and pre‑fill the most common parts.

What can you do?

• Search public YouTube for any query and get back the same results users see on YouTube.

• Pull public stats (views, likes, duration, thumbnails) for any video on the platform—not just your own.

• Enumerate an entire channel’s library.

Currently, only list endpoints have been written for this package, with others on the way.

Examples

1. Get all of your channel videos

all_vids = yt_data.channel_videos(mine=True)

2. Search for "Never Gonna Give You Up"

rick_results = yt_data.list_search(q="Never Gonna Give You Up")

3. Show a video's metadata (Title, description, runtime, etc)

vid_meta = yt_data.video_metadata(video_id="dQw4w9WgXcQ")

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Analytics API

The YouTube Analytics API provides analytics that can be found in YouTube Studio, providing in-depth insights for areas such as the following:

• Resources
• Geographic areas
• Time Periods
• Playback Locations
• Playback Details
• Traffic Sources
• Devices

and more. For the most customization in an api request, you can call the reports_query method, but wrapper functions have been written to simplify calling data and making your code easier to read. Below are some examples.

Examples

1. Channel stats (all-time)

df = yt_analytics.channel_stats(metrics=("views","averageViewDuration","subscribersGained"))

2. Last month's views by country

df = yt_analytics.channel_geography(
        geo_dim    ="country",
        start_date ="2025-05-01",
        end_date   ="2025-05-31",
        metrics    =("views",)          # default is views + minutesWatched
)

3. Audience retention for a single video

df = yt_analytics.video_audience_retention(
        video_ids   ="dQw4w9WgXcQ",
        audience_type="ORGANIC",        # or AD_INSTREAM, AD_INDISPLAY
        start_date  ="2025-01-01",
        end_date    ="2025-01-31",
)

Every helper returns a pandas.DataFrame.

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Reporting API

The YouTube Reporting API is designed for high-volume, historical reports that are exported daily. You first create a job, YouTube will generate the report on its schedule, and then you download the resulting CSV file. As mentioned above, you can interact with each endpoint directly, but a convenient wrapper get_latest_report() combines a multi-step workflow into one easy-to-use function to get the latest report.

Example

Get the latest "channel_basics_a2" report

latest_report = yt_reporting.get_latest_report("channel_basics_a2")

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Roadmap

• Add remaining endpoints in YouTube Data API
• Add Groups and Groupitems endpoints in YouTube Analytics API
• Added support for service account authentication and other methods of authentication
• Potential CLI wrapper: ytapi-kit geostats video_id --last 30d --csv out.csv

Up-vote an issue or open a PR to help me prioritize

Contributing

1. Fork: git clone
2. python -m pip install -e '.[dev]'
3. pytest && ruff check .
4. Submit a pull-request

Even small tweaks are welcome.

License

ytapi-kit is released under the MIT License (seeLICENSE)

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api/_data
api/_analytics
api/_reporting