A simple yet powerful SDK for the YouTube Analytics API.

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Features

• Pythonic syntax lets you feel right at home
• Dynamic error handling saves hours of troubleshooting and makes sure only valid requests count toward your API quota
• A clever interface allows you to make multiple requests across multiple sessions without reauthorising
• Extra support enables you to export reports in a variety of filetypes and to a number of DataFrame formats
• Easy enough for beginners, but powerful enough for advanced users

Installation

Installing analytix

To install the latest stable version of analytix, use the following command:

pip install analytix

You can also install the latest development version using the following command:

pip install git+https://github.com/parafoxia/analytix

You may need to prefix these commands with a call to the Python interpreter depending on your OS and Python configuration.

Dependencies

Below is a list of analytix's dependencies. Note that the minimum version assumes you're using CPython 3.8. The latest versions of each library are always supported.

OAuth authentication

All requests to the YouTube Analytics API need to be authorised through OAuth 2. In order to do this, you will need a Google Developers project with the YouTube Analytics API enabled. You can find instructions on how to do that in the API setup guide.

Once a project is set up, analytix handles authorisation — including token refreshing — for you.

More details regarding how and when refresh tokens expire can be found on the Google Identity documentation.

Usage

Retrieving reports

The following example creates a CSV file containing basic info for the 10 most viewed videos, from most to least viewed, in the US in 2022:

from datetime import date

from analytix import Client

client = Client("secrets.json")
report = client.fetch_report(
    dimensions=("video",),
    filters={"country": "US"},
    metrics=("estimatedMinutesWatched", "views", "likes", "comments"),
    sort_options=("-estimatedMinutesWatched",),
    start_date=date(2022, 1, 1),
    end_date=date(2022, 12, 31),
    max_results=10,
)
report.to_csv("analytics.csv")

If you want to analyse this data using additional tools such as pandas, you can directly export the report as a DataFrame or table using the to_pandas(), to_arrow(), and to_polars() methods of the report instance. You can also save the report as a .tsv, .json, .xlsx, .parquet, or .feather file.

There are more examples in the GitHub repository.

Fetching group information

You can also fetch groups and group items:

from analytix import Client

# You can also use the client as context manager!
with Client("secrets.json") as client:
    groups = client.fetch_groups()
    group_items = client.fetch_group_items(groups[0].id)

Logging

If you want to see what analytix is doing, you can enable the packaged logger:

import analytix

analytix.enable_logging()

This defaults to showing all log messages of level INFO and above. To show more (or less) messages, pass a logging level as an argument.

Compatibility

CPython versions 3.8 through 3.12 and PyPy versions 3.8 through 3.10 are officially supported*. CPython 3.13-dev is provisionally supported*. Windows, MacOS, and Linux are all supported.

*For base analytix functionality; support cannot be guaranteed for functionality requiring external libraries.

Contributing

Contributions are very much welcome! To get started:

• Familiarise yourself with the code of conduct
• Have a look at the contributing guide

License

The analytix module for Python is licensed under the BSD 3-Clause License.