YouTube History Analysis

This project provides insights into the type of YouTube content you consume and how you use the platform. It analyzes the number of videos you watch, when you watch them, their topics, and tags. The analysis is performed for various time periods, allowing you to understand how your viewing habits have changed over time.

Installation

Clone the repository

git clone https://github.com/mattiaferrarini/YouTube-History-Analysis.git

Install the required libraries

pip install -r requirements.txt

YouTube Data API key

Create a project in the Google Developers Console and obtain an API key. Create a file named .env in the project folder and add the created key to it as follows:

YOUTUBE_API_KEY=your-key

YouTube history

You can download your YouTube history from Google Takeout.

Click Deselect all at the top of the export panel. Then scroll all the way down to YouTube and YouTube Music. You should check the box, then only include history in the export and choose JSON as the format.

Once you have downloaded your history, add it to the project folder and rename it to watch-history.json if necessary.

Usage

Virtual environment

You should create an isolated virtual Python environment for the project using virtualenv to avoid problems with dependencies and versions.

Install virtualenv:

pip3 install virtualenv

Create a virtual environment:

virtualenv <your-env>

Activate it:

source <your-env>/bin/activate

To deactivate it afterwards:

deactivate

Process your history

Run the history_processor.py script:

python history_processor.py

The script will process the watch-history.json file, filtering out ads and useless data. It also queries the YouTube Data API in order to get the topics and tags of your watched videos. The final output is a processed-history.json file.

API quota limits

Since the YouTube Data API has a default quota allocation of 10,000 units per day, you may need to run the script on multiple days to process the entire history.

To manage this, history_processor.py generates a last-processed-history.json file which stores the index of the last processed history item. If this file exists when the script is run again, processing will resume from where it left off in the previous session.

Analyse your history

Once you have a processed-history.json file, you can analyse it by running the analysis.ipynb notebook.