Build Data Apps Fast: A Quick Guide to Streamlit for Python Developers

Want to create interactive data apps without the headache of front-end development? Streamlit is your answer! This open-source Python framework lets you transform data scripts into shareable web apps in minutes. Let's dive in and see how it works.

Streamlit logo

What is Streamlit and Why Should You Use It?

Streamlit simplifies the process of building data-driven web applications. Instead of wrestling with HTML, CSS, and JavaScript, you can use pure Python. This means faster development, easier collaboration, and more focus on your data.

Here's why developers love Streamlit:

Pythonic Simplicity: Write clean, readable code using familiar Python syntax.
Rapid Prototyping: Quickly iterate on your ideas and get feedback.
Live Updates: See your app change in real-time as you edit your code.
Community-Driven: Benefit from a vibrant community and extensive resources.
Free and Open-Source: Use it without restrictions and contribute to the project.

Get Started with Streamlit: Installation and Basic Usage

Ready to give Streamlit a try? Installation is a breeze:

pip install streamlit
streamlit hello

If the "Streamlit Hello" app pops up in your browser, you're good to go! If not, check the official Streamlit documentation for detailed instructions.

Your First Streamlit App: A Simple Example

Let's create a simple app to demonstrate the basics of Streamlit. Create a file named streamlit_app.py and add the following code:

import streamlit as st

x = st.slider("Select a value")
st.write(x, "squared is", x * x)

Now, run the app from your terminal:

streamlit run streamlit_app.py

This simple app creates a slider and displays the square of the selected value. As you move the slider, the output updates instantly – showcasing Streamlit's reactivity.

Little example

Powerful Elements for Interactive Data Apps

Streamlit offers a wide range of elements (widgets, data displays, and layouts) to create sophisticated data apps. These elements empower you to present data in various formats and enable user interactions.

Here's a glimpse of what you can achieve:

Input Widgets: Collect user input with sliders, buttons, text boxes, etc.
Dataframes: Display tabular data in interactive and sortable tables.
Charts: Create stunning visualizations with various chart types.
Layouts: Organize your app's content with columns, tabs, and expanders.
Multi-Page Apps: Build complex applications with multiple interconnected pages.
Streamlit components allows you to extend Streamlit capabilities

Streamlit components

Streamlit App Examples

Want to see what's possible? Explore the Streamlit Gallery for inspiring examples of data apps built with Streamlit. You’ll find apps for:

Large language models and chatbot apps
Science and technology
NLP and language processing
Finance and business
Geography and society

Deploying your Streamlit app for free

Streamlit provides a free option to deploy, share and manage your apps using the community cloud.

Streamlit Deploy

Resources for Learning and Support

To further enhance your Streamlit journey, here are some invaluable resources:

Documentation: https://docs.streamlit.io
Community Forum: https://discuss.streamlit.io
Blog: https://blog.streamlit.io

Streamlit’s active community ensures that help is always available and that new components are shared regularly. This collaborative environment fosters learning and innovation, making Streamlit even more powerful and user-friendly.

What is Streamlit and Why Should You Use It?

Here's why developers love Streamlit:

Pythonic Simplicity: Write clean, readable code using familiar Python syntax.

Rapid Prototyping: Quickly iterate on your ideas and get feedback.

Live Updates: See your app change in real-time as you edit your code.

Community-Driven: Benefit from a vibrant community and extensive resources.

Free and Open-Source: Use it without restrictions and contribute to the project.

Your First Streamlit App: A Simple Example

Let's create a simple app to demonstrate the basics of Streamlit. Create a file named streamlit_app.py and add the following code:

Now, run the app from your terminal:

This simple app creates a slider and displays the square of the selected value. As you move the slider, the output updates instantly – showcasing Streamlit's reactivity.