analytix 1.1.1

A simple yet powerful API wrapper to make getting analytical information from the YouTube Analytics API easier than ever.

analytix

A simple yet powerful API wrapper to make getting analytical information from the YouTube Analytics API easier than ever.

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 per session without reauthorising
• Extra support allows the native saving of CSV files and conversion to DataFrame objects
• Easy enough for beginners, but powerful enough for advanced users

Installation

You need Python 3.7.1 or greater to run analytix. You will also need to have a Google Developers project with the YouTube Analytics API enabled. You can find instructions on how to do that in the YouTube Analytics API docs.

It is recommended you install analytix in a virtual environment. To do this, run the following:

# Windows
> py -3.9 -m venv .venv
> .venv\Scripts\activate
> pip install analytix

# Linux\macOS
$ python3.9 -m venv .venv
$ source ./.venv/bin/activate
$ pip install analytix

To install analytix outside of a virtual environment instead, run the following:

# Windows
> py -3.9 -m pip install analytix

# Linux/macOS
$ python3.9 -m pip install analytix

You can also install the development version by running the following (this assumes you're on Linux/macOS):

$ git clone https://github.com/parafoxia/analytix
$ cd analytix
$ git checkout develop  # Any valid branch name can go here.
$ python3.9 -m pip install -U .

Usage examples

The following example shows you how easy analytix can be to use. This retrieves day-by-day analytics for the last 28 days using all metrics.

from analytix.youtube import YouTubeAnalytics, YouTubeService

service = YouTubeService("./secrets.json")  # Load from secrets file
service.authorise()
analytics = YouTubeAnalytics(service)
report = analytics.retrieve(dimensions=("day",))
report.to_csv("./analytics-28d.csv")

Of course not all requests will be that simple, but analytix can handle all the complicated stuff too. This example retrieves day-by-day analytics for live streams in January 2021, split by device type and subscription status, and sorted by views. It then saves the report as a CSV, converts to a dataframe for further analysis, and saves a boxplot figure.

import datetime as dt

import matplotlib.pyplot as plt
import seaborn as sns
from analytix.youtube import YouTubeAnalytics, YouTubeService

service = YouTubeService("./secrets.json")
service.authorise()
analytics = YouTubeAnalytics(service)

report = analytics.retrieve(
    metrics=("views",),
    dimensions=("day", "deviceType", "subscribedStatus"),
    filters={"liveOrOnDemand": "LIVE"},
    start_date=dt.date(2021, 1, 1),
    end_date=dt.date(2021, 1, 31),
    sort_by=("views",),
)
report.to_csv("./live-device-types.csv")
df = report.to_dataframe()

fig = plt.figure()
sns.boxplot(data=df, x="day", y="views", hue="deviceType")
fig.savefig("./live-device-types.png")

To read up further, have a look at the documentation.

License

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