Taipy: an open source Python library for data scientists for efficient data analysis and AI applications

General Introduction

Taipy is a powerful Python library developed by Avaiga designed for data scientists and machine learning engineers to rapidly build and deploy data-driven web applications.Taipy provides a full suite of tools and components that enable users to focus on data and AI algorithms without worrying about the complexity of development and deployment. Whether it's a simple pilot project or a production-grade application, Taipy delivers a high-performance, customizable and scalable solution.

Function List

• User Interface Generation: Generate interactive user interfaces from simple Python code.
• Scenarios and Data Management: Manage complex data processing and machine learning scenarios.
• quick start: Detailed installation and quick start guides are provided to help users get started quickly.
• high performance: Optimized performance for large-scale data processing and real-time applications.
• scalability: Support for customization and expansion to meet different project needs.
• Community Support: Active community and detailed documentation with ongoing support and updates.

Using Help

Installation process

1. Installation with pip: Run the following command in a terminal to install Taipy:

   pip install taipy

2. Configuration environment: It is recommended to use a Conda environment for isolation and management to ensure dependency compatibility.
3. Installation of dependencies: Install the other necessary Python libraries as required by your project.

Guidelines for use

Quick Start

1. Create a project: Create a new Python file in the project directory, for example main.pyThe
2. Import Taipy: Import the Taipy library in a file:

   import taipy as tp

3. Defining Scenarios: Create a simple scenario such as a movie recommendation system:

   import pandas as pd
from taipy import Config, Scope, Gui
def filter_genre(initial_dataset: pd.DataFrame, selected_genre):
filtered_dataset = initial_dataset[initial_dataset['genres'].str.contains(selected_genre)]
filtered_data = filtered_dataset.nlargest(7, 'Popularity %')
return filtered_data
if __name__ == "__main__":
Config.load("config.toml")
scenario_cfg = Config.scenarios["scenario"]
tp.Orchestrator().run()
scenario = tp.create_scenario(scenario_cfg)
genres = ["Action", "Adventure", "Comedy", "Drama", "Horror", "Sci-Fi"]
df = pd.DataFrame(columns=["Title", "Popularity %"])
selected_genre = "Action"
my_page = """
# Film Recommendation
## Choose Your Favorite Genre
<|{selected_genre}|selector|lov={genres}|on_change=on_genre_selected|dropdown|>
## Here are the Top Seven Picks by Popularity
<|{df}|chart|x=Title|y=Popularity %|type=bar|title=Film Popularity|>
"""
Gui(page=my_page).run()

Detailed Functions

• User Interface Generation: With simple Python code, you can quickly generate interactive web interfaces that support a wide range of charts and controls.
• Scenarios and Data Management: Provides powerful data processing and scenario management capabilities to support complex machine learning pipelines and data streams.
• Extension and Customization: Users can customize and extend Taipy's functionality to meet specific business needs based on project requirements.

common problems

• How do you handle large-scale data?: Taipy is optimized to handle large-scale data efficiently, and distributed computing and parallel processing techniques are recommended.
• Does it support multi-user collaboration?: Taipy supports multi-user collaboration, where users can configure permissions and roles to enable team collaboration.
• How do I get technical support?: Users can submit questions and get technical support and help through the official documentation, community forums, and GitHub.