Skip to content

Latest commit

 

History

History
74 lines (49 loc) · 2.63 KB

README.md

File metadata and controls

74 lines (49 loc) · 2.63 KB

Google Calendar Analysis Notebook

Overview

This notebook was used to write the blog post A Year in Time: My 2024 Google Calendar Analysis, which analyzes how I spent my time in 2024 based on my Google Calendar data. The gcal_analysis_2024.ipynb etches, processes, and visualizes event data, generating insights on time spent across categories, word clouds, and monthly hour distributions. It can also be adapted for other years.

Features

  • Fetches events from Google Calendar using OAuth 2.0 authentication.
  • Analyzes event data to calculate total hours spent on various categories.
  • Generates a word cloud visualization of event summaries.
  • Provides insights into event distribution over weeks and months.

Requirements

To run this project, you need the following Python packages:

  • pytz
  • matplotlib
  • pandas
  • collections
  • statistics
  • google-auth
  • google-auth-oauthlib
  • google-api-python-client

You can install the required packages using pip:

pip install pytz matplotlib pandas google-auth google-auth-oauthlib google-api-python-client

Setup

  1. Clone the Repository:

    Clone this repository to your local machine:

    git clone [email protected]:eltsai/gcal_analysis.git
cd gcal_analysis

  2. Google Calendar API Setup:

    • Go to the Google Cloud Console.
    • Create a new project.
    • Enable the Google Calendar API for your project.
    • Create OAuth 2.0 credentials and download the credentials.json file.
    • Place the credentials.json file in the project directory.

  3. Run the Jupyter Notebook:

    Open gcal_analysis_2024.ipynb and run the cells to fetch and analyze your Google Calendar events.

Usage

  • The notebook is structured into several sections:
    • Fetch Data From Google Calendar: This section handles authentication and fetching events.
    • Load Data: Loads the fetched event data from a JSON file (you'll probably need to rewrite the mapping between color and category).
    • Data Analysis: Analyzes the events to calculate total hours spent on different categories.
    • Visualizations: Generates visualizations such as word clouds and monthly hour distributions.

Important Notes

  • Ensure that you have the necessary permissions to access your Google Calendar data.
  • The project includes a .gitignore file to exclude sensitive files like credentials.json and any generated data files.

License

This project is licensed under the MIT License. See the LICENSE file for more details.