Streamlit: A Python Library for Crafting Stunning Data Apps

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Streamlit: A Python Library for Building Beautiful Data Apps

Introduction

Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. Streamlit is a great choice for building data apps because it is:

  • Easy to learn and use
  • Highly interactive
  • Responsive and mobile-friendly
  • Deployable to the web or on-premise

Getting started with Streamlit

To get started with Streamlit, you first need to install the library. You can do this by running the following command in your terminal:

pip install streamlit

Once Streamlit is installed, you can create your first Streamlit app by creating a new file and writing some code. The following code is a simple Streamlit app that displays a welcome message:

import streamlit as st

st.title("Welcome to Streamlit!")

To run this app, you can save the file and then run the following command in your terminal:

streamlit run my_app.py

This will open a web browser window and display your Streamlit app.

Using Streamlit widgets

Streamlit widgets are a way to add interactivity to your Streamlit apps. There are many different types of Streamlit widgets, including:

  • Text boxes
  • Buttons
  • Sliders
  • Charts
  • Tables

To use a Streamlit widget, you first need to import it. For example, to use a text box, you would import the st.text_input() function. Then, you would call the function and pass in the name of the widget and the default value. For example, the following code creates a text box that asks the user for their name:

name = st.text_input("What is your name?")

Deploying your Streamlit app

Once you have created your Streamlit app, you can deploy it to the web. There are two ways to do this:

  • You can deploy your app to Streamlit Cloud, which is a hosted service that makes it easy to deploy and share your Streamlit apps.
  • You can deploy your app to your own server. This requires you to have a web server and Python installed.

Advanced features of Streamlit

Streamlit has many advanced features that you can use to create even more powerful data apps. Some of these features include:

  • Caching: This allows you to cache data so that it does not have to be reloaded every time the app is run.
  • Theming: This allows you to change the look and feel of your app.
  • Streamlit Components: This is a library of pre-built widgets that you can use in your apps.

Conclusion

Streamlit is a powerful tool that can be used to create beautiful, interactive data apps. It is easy to learn and use, and it has a wide range of features. If you are looking for a way to build data apps, Streamlit is a great option.

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