Meet Taipy: A Pure-Python, Fast, and Scalable Application Builder
From Time Series to Chatbots: Bring your Python Models to Life with Taipy
Taipy is a Python application builder with one clear promise: deploy your data applications in real production environments. It’s the ideal tool for creating scalable, interactive apps that bring your models, analytics, and algorithms to life. Whether you’re building dashboards, optimization tools, or AI-powered chatbots, Taipy helps data professionals turn prototypes into powerful, end-user applications. With Getting Started with Taipy, you’ll learn how to build complete applications from the ground up, deploy them confidently, and explore real-world examples and advanced use cases that showcase Taipy’s full potential.
Python has long been the go-to language for data professionals, not because they’re developers, but because Python makes complex work accessible. Analysts, data scientists, and AI engineers use it to model data, run analytics, and visualize results.
But when it comes to turning those models into real applications for end users, things get tricky. Building a web app the traditional way, with backend frameworks, databases, and front-end stacks, is often out of reach for data teams. It demands skills, time, and coordination that slow everything down and increase costs.
Tools like Power BI or Tableau help visualize data, but they can’t truly run Python code or offer the flexibility of a full application. Python frameworks like Streamlit, Dash, Panel, or Gradio solve the problem partially. Each has trade-offs. To give an example, Streamlit is a great library for prototyping: it’s very easy to learn, and you can create demos in no time. While you can take Streamlit applications to production, they are harder to scale because they don’t optimize the way code runs, and they run on their own server (you can’t run them in a WSGI server). What this means is you can create useful applications for end users if they make limited use of the app, or if you don’t need to process large amounts of data.
That’s where Taipy comes in!
Taipy lets you create scalable, production-grade applications directly in Python. Whether for time series, optimization, geospatial analysis, or even LLM chatbots, Taipy is designed for performance and scalability. You can deploy Taipy apps on WSGI servers, handle multiple users efficiently, and still build everything using pure Python.
What’s in the book?
The book is organized into three parts.
Part I introduces the core of Taipy: how to build complete applications, design visual interfaces, manage scenarios, and deploy your projects. By the end, you’ll be able to create fully functional Taipy apps from scratch.
Chapter 1 offers a broad introduction to Taipy and guides you through setting up your environment.
Chapter 2 — the longest in the book — walks you through building complete user interfaces with Taipy. By the end, you’ll have all the elements you need to create fully functional applications.
Chapters 3, 4, and 5 dive into Scenario Management, one of Taipy’s key differentiators from other application builders. In Chapter 4, you’ll put this into practice by training a simple regression model and running it through Scenario Management within a Taipy UI.
Finally, Chapter 6 explains how to deploy your Taipy applications. Since Taipy is built on Flask, you can deploy your app just like any Flask application. This is a feature that makes Taipy both reliable and highly scalable.
Part II dives into four practical use cases: time series forecasting, optimization, geospatial analysis, and LLM-based chatbots. You’ll also learn to build interactive dashboards that display historical data and KPIs. This section shows the versatility of Taipy across real-world applications.
The goal of this section is to show how different algorithms can be integrated into Taipy applications. Each chapter highlights a specific feature of Taipy and uses hands-on examples to inspire new ideas for your own projects.
In this part, the applications make broader use of CSS to achieve a more polished look.
You’ll also find discussions about design choices, implementation alternatives, and performance considerations. Most examples use Parquet files for efficiency and simplicity (databases would be overkill for a book setting). Chapter 7, for instance, includes a discussion on data modeling and how it affects the performance of Taipy applications (although this would impact any type of application!)
In Chapter 8, you’ll code an optimization model to compare different scenarios. This application is the example that best shows the potential of Taipy’s Scenario Management!
In Chapter 9, you’ll code an app that uses an external service (European Space Agency — ESA) to process geographical information about Paris’ parks. This application shows how you can use Taipy to interact with remote services and how to display image data.
Finally, Chapter 10 walks you through building an LLM chatbot. You’ll learn how to switch between interfaces using Taipy partials, handle user inputs through buttons, change chatbot models and parameters like temperature, and even store and reload chat histories to resume past conversations.
Part III explores advanced topics: optimizing performance, handling large datasets, developing real-time apps, and integrating external components. It also covers Taipy Designer, introduces the Enterprise edition, and features insights from real Taipy users.
Chapter 11 covers advanced Taipy tools, including partials, long-running callbacks, and event consumers for Scenario Management. You’ll also learn how to apply parallel processing within Scenarios to boost performance and scalability.
In Chapter 12, you’ll discover how to work with large datasets in Taipy applications using tools like Dask and Spark. You’ll also be challenged to integrate Polars and DuckDB, and explore special Taipy functions designed to visualize large datasets efficiently.
Chapter 13 focuses on real-time and near–real-time data processing. You’ll build an application that tracks earthquakes as they happen, with live mapping and alerts, to see how Taipy handles streaming and batch updates seamlessly.
In Chapter 14, you’ll learn how to enhance your Taipy applications with HTML and JavaScript-based components, including Python libraries such as Folium, which render dynamic, web-ready visuals directly inside your app.
Finally, the book closes with two special chapters: Chapter 15 is a small introduction to Taipy’s enterprise version, which has specific tools and integrations for larger businesses, and Chapter 16 includes three interviews with advanced Taipy users.
Taipy changes the way data professionals build and share their work — transforming Python projects into real, scalable applications.
Whether you’re a data scientist, analyst, or AI engineer, Getting Started with Taipy will guide you step by step from prototypes to production.
Grab your copy, start building, and bring your Python models to life!
If you want to read more about Taipy and other Python and Data Analytics subjects, follow me on Medium, LinkedIn, and YouTube!
