Live TV Events - Data Science Guides

Welcome to Live TV Events' Data Science documentation. This content is provided as a resource for Data Science teams to help get them up and running quickly.

If you're looking for API documentation to get familiar with the Live TV Events endpoints and parameters, please take a look at the Live TV Events with the Broadcasts API page.

We provide guides to using our API and data with common Data Science tools and libraries in Python. The articles include a link to Jupyter Notebooks that you can download and run. The guides include code samples and instructions for performing common tasks.

Walk through the TV Events Jupyter Notebook with Data Scientist Andrew Walker

These guides consist of the following three  notebooks:

• Part 1: Data Engineering: shows you how you could call the Live TV Events Broadcast API to extract data to a DataFrame.

• Part 2: Data Exploration provides a guide to exploring PredictHQ's Live TV Events data for Data Science teams.

• Part 3: Feature Engineering provides examples of how to aggregate and extract features from the data.