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Python for Pricing

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Learn how and why to adopt python in your pricing workflows

Welcome to our comprehensive Python for Pricing Course, built specifically for insurance pricing professionals.

Whether you're leading a pricing team, new to coding, or looking to modernise your workflow, this course shows you how Python can transform the way you work; from one-off analyses in Excel to reproducible, scalable, and automated systems.

You'll learn why Python is the right choice for pricing, how to get set up with the right tools, and how to apply engineering best practices - including version control, modular code, and automation, to improve the quality of analysis.

Author

My face

My name is Ralph and I've been using Python for the majority of my career in Pricing.

To me it's not just a different tool to use, it's a different approach to analysis, and provides a variety of ways to step up the efficiency and quality of your pricing work.

I've written this course to share my experience of building end-to-end pricing systems with the wider industry and encourage analysts to explore the world of modern analytics.

Connect with me on LinkedIn: linkedin.com/in/ralph-clayton/

Read more about Pricing Frontier LTD: pricing-frontier.co.uk

You're joining a community

You're not learning in isolation - you're joining community of pricing analysts modernising their skillsets. Whether you're just starting out or leading change in your team, this course is here to support your next step.

Chapter 0: Overview

Understand why Python is a powerful tool for pricing teams. This chapter introduces the core motivations for adopting Python - from automation and reproducibility to innovation and collaboration - and outlines how this course is structured to support different experience levels.

Chapter 1: Principles

Explore the key principles that underpin modern pricing workflows in Python. Topics include transparency, automation, modularity, and collaboration - all essential for building robust, auditable, and most importantly reproducible analytical processes.

Chapter 2: Setting up

Get hands-on with your development environment. This section walks you through installing Python, using Visual Studio Code, managing virtual environments, and version-controlling your work with Git.

Chapter 3: Data Manipulation

Learn the core Python syntax and tools needed to work with data effectively. You’ll cover notebooks, python syntax, libraries, and how to manipulate dataframes - the foundation for any pricing model or analysis.

Chapter 4: Visualisation

Bring your analysis to life with charts and visuals. This chapter shows how to communicate insights clearly using Python visualisation libraries, making it easier to explain models and results to both technical and non-technical audiences.

Chapter 5: Machine Learning

Dive into predictive modelling for pricing. Learn how to build, validate, and deploy machine learning models using Python - with a focus on workflows that are explainable, production-ready, and aligned to pricing use cases.

Chapter 6: Production

Take your work from notebooks to production. This section covers key engineering concepts like Docker, CI/CD, cloud deployment, and working with platforms like Databricks - enabling your models and analytics to scale.