A simple yet powerful SDK for the YouTube Analytics API.
- 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
To install the latest stable version of analytix, use the following command:
pip install analytixYou can also install the latest development version using the following command:
pip install git+https://github.com/parafoxia/analytixYou may need to prefix these commands with a call to the Python interpreter depending on your OS and Python configuration.
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.
| Name | Min. version | Required? | Usage |
|---|---|---|---|
urllib3 |
2.2.0 | Yes | Making HTTP requests |
jwt |
1.2.0 | No | Decoding JWT ID tokens (from v5.1) |
openpyxl |
3.0.0 | No | Exporting report data to Excel spreadsheets |
pandas |
~1.3.0 | No | Exporting report data to pandas DataFrames |
polars |
0.15.17 | No | Exporting report data to Polars DataFrames |
pyarrow |
~5.0.0 | No | Exporting report data to Apache Arrow tables and file formats |
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.
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.
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)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.
CPython versions 3.8 through 3.13 and PyPy versions 3.9 and 3.10 are officially supported*. CPython 3.14-dev is provisionally supported*. Windows, MacOS, and Linux are all supported.
*For base analytix functionality; support cannot be guaranteed for functionality requiring external libraries.
Contributions are very much welcome! To get started:
- Familiarise yourself with the code of conduct
- Have a look at the contributing guide
The analytix module for Python is licensed under the BSD 3-Clause License.