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Getting Started With Streamlit For Data Science

Bo Cox

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Published in Getting Started With Streamlit For Data Science: Create And Deploy Streamlit Web Applications From Scratch In Python

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Streamlit is an open-source Python library that makes it easy to build interactive data science applications. With Streamlit, you can quickly and easily create dashboards, visualizations, and other interactive elements that can be shared with others.

Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python

by Tyler Richards

4.5 out of 5

Language	:	English
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This guide will teach you how to get started with Streamlit for data science. We'll cover everything from installing Streamlit and creating your first app to deploying your app to the web.

Installing Streamlit

To install Streamlit, simply run the following command in your terminal:

pip install streamlit

Once Streamlit is installed, you can create your first app by running the following command:

streamlit new my_app

This will create a new directory called `my_app` that contains all of the files you need to create your app.

Creating Your First App

To create your first app, open the `my_app` directory in your favorite code editor. The `my_app` directory contains three files:

* `app.py`: This file contains the Python code for your app. * `requirements.txt`: This file contains the Python packages that your app requires. * `streamlit_app.py`: This file contains the Streamlit code for your app.

To create your first app, open the `app.py` file and add the following code:

import streamlit as st

st.title("My First Streamlit App")

st.write("This is my first Streamlit app.")

This code creates a simple Streamlit app that displays the title "My First Streamlit App" and the text "This is my first Streamlit app."

To run your app, open your terminal and navigate to the `my_app` directory. Then, run the following command:

streamlit run app.py

This will start your app in a web browser.

Deploying Your App

Once you've created your app, you can deploy it to the web so that others can use it. To do this, you can use a service like Heroku or AWS Elastic Beanstalk.

To deploy your app to Heroku, you'll need to create a Heroku account and then create a new app. Once you've created a new app, you can deploy your code to Heroku by running the following command:

heroku create my-app

This will create a new Heroku app called `my-app`. To deploy your code to Heroku, run the following command:

git push heroku master

This will deploy your code to Heroku and make your app available at `https://my-app.herokuapp.com`.

Streamlit is a powerful tool that makes it easy to build interactive data science applications. With Streamlit, you can quickly and easily create dashboards, visualizations, and other interactive elements that can be shared with others.

In this guide, we've covered everything from installing Streamlit and creating your first app to deploying your app to the web. Now that you know the basics of Streamlit, you can start building your own interactive data science applications.

Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python

by Tyler Richards

4.5 out of 5