Streamlit
Streamlit is an open-source Python framework for building interactive web applications, particularly for data and machine-learning applications.
Build Python Web Apps with Streamlit
Streamlit is an open-source Python framework for building interactive web applications, especially data and machine-learning applications.
- How it is used
- Turns Python scripts into browser-based interfaces for data apps, internal tools, AI chatbots, and quick prototypes.
- How deep it goes
- Works through installation, browser testing, and iterative reruns, while identifying performance and customization limits.
- Examples
- Uses a die-roll chart, tabbed page, histogram, editable dataframe, and chatbot to show visible UI results.
- What you can do after
- Build it from scratch as a Python browser app, test interactions, and share a public version through Streamlit Community Cloud.
Read how Build Python Web Apps with Streamlit explains Streamlit →
The Well-Grounded Data Analyst
Streamlit is an open-source Python library for building interactive data applications with minimal application code.
- How it is used
- Builds interactive web-based proof-of-concept applications for exploratory analysis and stakeholder-facing data questions.
- How deep it goes
- Explains targeted implementation through data loading, caching, layout, widgets, filtering, charts, metrics, and tables, without broader framework detail.
- Examples
- Uses a Wales house-price explorer with geographic dropdowns, ridgeplots, time series, property-type charts, metrics, and raw data.
- What you can do after
- Build it from scratch as a focused proof of concept using existing analytical functions and a small set of Streamlit elements.
Read how The Well-Grounded Data Analyst explains Streamlit →
DuckDB in Action
Streamlit is an open-source Python framework for building interactive data and machine-learning applications.
- How it is used
- Builds accessible interactive frontends for DuckDB-backed data and machine-learning applications.
- How deep it goes
- Explains installation, app structure, database connection, components, queries, and rendering, while deferring broader chart design.
- Examples
- Demonstrates an ATP player-search app with DuckDB queries, two search boxes, win counts, and a match-results table.
- What you can do after
- Build it from scratch, launch it locally, connect DuckDB, add interactive inputs, and render query results.
Python How-To
Streamlit is an open-source Python library for building interactive data applications and web interfaces from Python scripts.
- How it is used
- Builds the browser frontend for a Python task manager, including task views, entry forms, sorting, and filtering.
- How deep it goes
- Explains installation, launching, widgets, script reruns, session tracking, and project organization, but defers a comprehensive widget tutorial.
- Examples
- Uses a task manager with a sidebar, task pages, stored sorting choices, and a `streamlit run` command.
- What you can do after
- Apply Streamlit to build and run a browser-based task manager while retaining page and filter choices across script reruns.
Build Applications with Local AI Models on a Mac
Streamlit is a Python framework for creating interactive web applications from Python code.
- How it is used
- Provides the browser interface for a local AI chatbot connected to a Python API layer and Ollama backend.
- How deep it goes
- Explains generated page elements, demonstrates basic widgets, and outlines reruns and local development; stops short of specialized large-platform infrastructure.
- Examples
- Uses a Hello World page, title and text-input calls, an installation command, and a local development-server command.
- What you can do after
- Build it from scratch as a local Python web interface with interactive widgets and layouts.
Read how Build Applications with Local AI Models on a Mac explains Streamlit →
Effective Data Analysis
Streamlit is an open-source Python framework for building and sharing interactive data applications.
- How it is used
- Supports rapid development and sharing of prototypes, interactive reports, and dashboards for stakeholder feedback.
- How deep it goes
- Explains core capabilities and deployment options but stops short of implementation instructions or framework internals.
- Examples
- Uses a rat-sighting forecast app combining weather data, regression, derived variables, and a National Weather Service API.
- What you can do after
- Build and share interactive Python data applications, prototypes, reports, and dashboards.
AI Agents in Action
Streamlit is an open-source Python framework for rapidly building interactive web applications for data science and machine learning.
- How it is used
- Builds Python web interfaces for machine learning dashboards, chat applications, and rapidly modified agent platforms.
- How deep it goes
- Explains controls, reruns, temporary session state, chat flow, debugging, and streaming, without detailing long-term database storage.
- Examples
- Uses a ChatGPT-like clone, prompt input, message history, a waiting spinner, and streamed responses.
- What you can do after
- Build it from scratch: create basic Python interfaces, connect chat controls to an API, debug execution, and stream responses.
Knowledge Graphs and LLMs in Action
Streamlit is an open-source Python framework for creating interactive web applications from data and machine-learning code.
- How it is used
- Provides a low-overhead interactive frontend for validating expert-emulating question-answering pipelines.
- How deep it goes
- Explains interface requirements and implementation patterns, but defers specialized production-interface concerns.
- Examples
- Uses chat input, MessageHistory, placeholders, Markdown, tables, maps, graph selections, and progressive pipeline responses.
- What you can do after
- Build it from scratch as a Python interface for interactive pipeline testing and visualization.
Read how Knowledge Graphs and LLMs in Action explains Streamlit →
Natural Language Processing in Action, Second Edition
Streamlit is an open-source Python framework for rapidly creating interactive web applications.
- How it is used
- Deploys Python-based question-answering applications as accessible interactive web interfaces.
- How deep it goes
- Explains basic interface construction and deployment workflow, while deferring a deeper treatment of Streamlit.
- Examples
- Uses a Hello World QA app, text-input echo, retriever-reader pipeline, and displayed answer and context.
- What you can do after
- Build it from scratch by turning a Python QA script into an interactive app and deploying it through Hugging Face Spaces.
Read how Natural Language Processing in Action, Second Edition explains Streamlit →
Related concepts
- Pythonuses · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
- web applicationproduces · A web application is software that provides functionality through web technologies and is accessed by users through a browser or other web client.
- frameworkkind of · A software framework is a reusable structure that provides components, conventions, services, or extension points for building applications or other software systems.
- Terminalused by · Terminal is the macOS application for accessing a command-line interface.
- pipused by · pip is Python’s standard package installer, a command-line tool for installing and managing Python packages and their dependencies.
- widgetuses · A widget is a self-contained user-interface component that displays information or provides a focused interaction.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.