Here’s a list of 100 chapter titles for learning the Streamlit framework, organized from beginner to advanced levels. These chapters cover a wide range of topics, from basic web app development to advanced techniques and real-world applications using Streamlit:
- Introduction to Streamlit and Its Features
- Setting Up Streamlit: Installation and Environment Setup
- Creating Your First Streamlit App
- Understanding Streamlit's Execution Model
- Displaying Text with
st.write and st.markdown
- Adding Titles, Headers, and Subheaders
- Displaying DataFrames and Tables
- Using
st.dataframe and st.table
- Displaying Static Images with
st.image
- Adding Interactive Widgets: Buttons and Checkboxes
- Using Sliders and Select Sliders
- Working with Text Input and Text Areas
- Adding Dropdowns with
st.selectbox
- Using Radio Buttons and Multiselect Widgets
- Displaying Progress Bars with
st.progress
- Adding Date and Time Inputs
- Uploading Files with
st.file_uploader
- Displaying JSON and Code Snippets
- Adding Sidebars to Your Streamlit App
- Customizing the Layout with Columns
- Using
st.columns for Multi-Column Layouts
- Adding Expanders and Containers
- Displaying Charts with
st.line_chart and st.bar_chart
- Using
st.area_chart and st.pyplot
- Introduction to Streamlit's Caching Mechanism
- Using
st.cache to Optimize Performance
- Handling User Input and State Management
- Building a Simple Calculator App
- Creating a To-Do List App
- Deploying Your First Streamlit App
- Advanced Data Visualization with Plotly
- Integrating Plotly Charts in Streamlit
- Using Altair for Interactive Visualizations
- Building Dashboards with Streamlit
- Creating Multi-Page Apps with Streamlit
- Using
st.beta_container for Advanced Layouts
- Adding Animations and Dynamic Content
- Working with Session State in Streamlit
- Persisting State Across Reruns
- Building Forms with
st.form
- Handling Form Submissions and Validation
- Integrating Streamlit with Pandas
- Building Data Exploration Tools
- Creating Interactive Data Filters
- Using Streamlit with SQL Databases
- Querying and Displaying Database Results
- Integrating Streamlit with APIs
- Fetching and Displaying API Data
- Building a Weather App with Streamlit
- Creating a Stock Market Dashboard
- Using Streamlit with Machine Learning Models
- Building a Model Prediction Interface
- Deploying Machine Learning Models with Streamlit
- Integrating Streamlit with TensorFlow and PyTorch
- Building a Image Classification App
- Creating a Natural Language Processing (NLP) App
- Using Streamlit with Scikit-learn
- Building a Recommendation System Interface
- Adding Authentication to Streamlit Apps
- Securing Your Streamlit App with Login
- Advanced Caching Techniques in Streamlit
- Using
st.cache_data and st.cache_resource
- Optimizing Performance for Large Datasets
- Building Real-Time Dashboards
- Using WebSockets with Streamlit
- Integrating Streamlit with Kafka
- Building Real-Time Data Pipelines
- Advanced Layout Customization with HTML and CSS
- Embedding Custom JavaScript in Streamlit
- Creating Custom Components for Streamlit
- Using Streamlit Components for Advanced Interactivity
- Building a Custom Map Component
- Integrating Streamlit with GeoPandas
- Creating Geospatial Visualizations
- Using Streamlit with Deck.gl for 3D Maps
- Building a Real-Time Chat App
- Integrating Streamlit with Firebase
- Using Streamlit with GraphQL APIs
- Building a GraphQL Query Interface
- Advanced State Management Techniques
- Using Redux with Streamlit
- Building Complex Multi-Step Forms
- Creating a Survey App with Streamlit
- Integrating Streamlit with Docker
- Containerizing Your Streamlit App
- Deploying Streamlit Apps on Kubernetes
- Using Streamlit with CI/CD Pipelines
- Automating Deployment with GitHub Actions
- Monitoring and Logging Streamlit Apps
- Scaling Streamlit Apps for High Traffic
- Building Custom Themes for Streamlit
- Customizing the Look and Feel of Your App
- Using Streamlit with Advanced Machine Learning Pipelines
- Building a Real-Time Object Detection App
- Creating a Deep Learning Model Training Interface
- Integrating Streamlit with Reinforcement Learning
- Building a Real-Time Simulation Dashboard
- Using Streamlit with Quantum Computing Libraries
- Building a Quantum Circuit Simulator
- Scaling Streamlit for Enterprise Applications
This structured approach ensures a comprehensive learning path, starting from the basics and gradually moving to advanced and expert-level topics. Each chapter builds on the previous one, providing a solid foundation for mastering Streamlit and becoming proficient in building interactive web applications for data science and machine learning.