| File Name: | Streamlit with Python: Build and Deploy Real-World Data Apps |
| Content Source: | https://www.udemy.com/course/streamlit-with-python-build-and-deploy-data-apps/ |
| Genre / Category: | Programming |
| File Size : | 9.9 GB |
| Publisher: | Uplatz Training |
| Updated and Published: | February 6, 2026 |
Streamlit is an open-source Python framework that lets you build interactive web apps for data, analytics, and machine learning—using only Python. No HTML, CSS, or JavaScript required. If you can write a Python script, you can build a web app. It’s widely used by data scientists, analysts, ML engineers, and Python developers to turn scripts and notebooks into shareable apps in minutes.
How Streamlit Works
Streamlit follows a script-based execution model:
- You write a normal Python script
- You use st * commands (like st button, st dataframe, st line_chart)
- Streamlit runs your script top to bottom
- Every user interaction (button click, slider move) re-runs the script
- Streamlit automatically updates the UI in the browser
Key Idea
Your Python script is your web app
No routes, no callbacks, no frontend state headaches.
Behind the Scenes (What Happens Internally)
- Python code runs on the backend
- Streamlit:
- Detects UI elements
- Sends UI state to the browser
- Re-executes the script on interaction
- Session state keeps track of user-specific data
- Caching prevents unnecessary recomputation
This makes Streamlit:
- Extremely fast to develop
- Easy to reason about
- Ideal for data-driven apps
Main Features of Streamlit
1. Rapid App Development
- Build apps in minutes, not days
- No frontend knowledge required
- Minimal boilerplate code
2. Rich UI Components
Out of the box support for:
- Text, markdown, metrics
- Buttons, sliders, checkboxes
- Forms and input widgets
- Tables and editable dataframes
3. Powerful Data Visualization
- Native charts (st line_chart, st bar_chart)
- Full support for:
- Matplotlib
- Seaborn
- Plotly
- Altair
- Interactive dashboards with minimal code
4. Session State & Caching
- st session_state for user-specific data
- Caching for:
- Data loading
- Expensive computations
- Major performance boost for real apps
5. Multi-Page Applications
- Build multi-page dashboards
- Shared navigation and state
- Clean project structure for large apps
6. File Handling & Media Support
- Upload CSV, Excel, images, audio, video
- Download processed files
- Great for tools and internal utilities
7. Database & API Integration
- Connect to:
- SQL databases
- Cloud databases
- REST APIs
- Build fully data-driven applications
8. Styling & Theming
- Built-in themes
- Custom CSS injection
- Branding-ready UIs
9. Easy Deployment
- Streamlit Community Cloud
- Docker
- AWS, Azure, GCP
- Works well with CI/CD pipelines
What Streamlit Is Best For:
- Data dashboards
- ML model demos
- Internal tools
- Analytics apps
- Rapid prototypes
- Personal or startup projects
DOWNLOAD LINK: Streamlit with Python: Build and Deploy Real-World Data Apps
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part01.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part02.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part03.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part04.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part05.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part06.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part07.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part08.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part09.rar – 1000.0 MB
Streamlit_with_Python_Build_and_Deploy_RealWorld_Data_Apps.part10.rar – 973.0 MB
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