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using-streamlit-custom-components

Integrate third-party custom components into Streamlit applications.

1|1|Updated Nov 9, 2025
Search Tags:#data visualization#web development#python#ui/ux#streamlit#custom components
One-click install
npx skills add https://github.com/Paldom/databricks-apps-streamlit-vibe-coding-starter --skill using-streamlit-custom-components-paldom
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: using-streamlit-custom-components
Source: https://github.com/Paldom/databricks-apps-streamlit-vibe-coding-starter/tree/main/.agents/skills/developing-with-streamlit/skills/using-streamlit-custom-components
Command: npx skills add https://github.com/Paldom/databricks-apps-streamlit-vibe-coding-starter --skill using-streamlit-custom-components-paldom

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires streamlit-keyup, streamlit-bokeh, streamlit-aggrid, streamlit-folium, pygwalker, streamlit-extras, and includes references (resource) components.

What problem does it solve?

This Skill helps you enhance your Streamlit applications by integrating powerful features from the community through custom components, going beyond Streamlit's core capabilities.

Core Features & Use Cases

  • Third-Party Integration: Easily add advanced UI elements and functionalities not available in standard Streamlit.
  • Component Discovery: Provides examples and guidance on popular custom components like streamlit-keyup, streamlit-bokeh, streamlit-aggrid, streamlit-folium, pygwalker, and streamlit-extras.
  • Use Case: You need a live search input that updates results as the user types, or an interactive data grid with advanced sorting and filtering. This Skill guides you on how to find and implement such components.

Quick Start

Install the streamlit-aggrid package and use AgGrid to display your DataFrame with editable cells and filtering enabled.

Frequently Asked Questions about using-streamlit-custom-components

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add interactive data visualization to a Streamlit app using custom components?▼

You can add interactive data visualization to Streamlit by integrating community packages like streamlit-bokeh and pygwalker. These custom components extend core functionalities to provide advanced UI elements for data exploration and presentation.

Can I use streamlit-aggrid to display a DataFrame with sorting and filtering in Streamlit?▼

Yes, you can use streamlit-aggrid to display DataFrames with editable cells, sorting, and filtering enabled. It acts as a custom component to provide an interactive data grid that goes beyond standard Streamlit tables.

What is the best way to create a live search input in Streamlit that updates results as the user types?▼

The best way to create a live search input is by integrating the streamlit-keyup custom component. This package captures keystrokes directly, enabling dynamic UI updates without requiring users to submit a form.

Does Streamlit support interactive maps for location data without built-in GIS features?▼

Streamlit supports interactive maps through the streamlit-folium custom component. This integration allows you to embed advanced Folium maps into your application, extending your UI with specialized geographical data visualization.

How do I install and use third-party community packages in Streamlit?▼

You install third-party Streamlit packages using standard Python package managers like pip. After installation, you import the package into your script and call its specific functions to render the custom UI elements within your Streamlit application.

What are the limitations of using custom components for web development in Streamlit?▼

Custom components for web development in Streamlit require managing external package dependencies and potential version conflicts. You should anticipate integration overhead when maintaining specialized UI widgets not supported by the core framework.

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