.. toctree::
:caption: Documentation
:maxdepth: 2
:hidden:
getting_started
main_concepts
tutorial/index
caching
advanced_caching
advanced_concepts
api
cli
pre_release_features
changelog
.. toctree::
:caption: Support
:maxdepth: 2
:hidden:
troubleshooting/index
Frequently Asked Questions < https://github.com/streamlit/streamlit/wiki/FAQ>
Community forum < https://discuss.streamlit.io/>
Bug tracker <https://github.com/streamlit/streamlit/issues>
GitHub <https://github.com/streamlit/streamlit>
Streamlit is an open-source Python library that makes it easy to build beautiful custom web-apps for machine learning and data science.
To use it, just pip install streamlit
, then import it, write a couple lines
of code, and run your script with streamlit run [filename]
. Streamlit watches
for changes on each save and updates the app live while you're coding. Code
runs from top to bottom, always from a clean state, and with no need for
callbacks. It's a simple and powerful app model that lets you build rich UIs
incredibly quickly. To learn more about how Streamlit works, see Main
concepts.
You may also want to check out this four-part video recorded at our PyData talk on December 2019. In it we describe the motivation behind Streamlit, then go over how to install and create apps with it.
.. raw:: html
<iframe
width="560"
height="315"
src="https://www.youtube.com/embed/sxLNCDnqyFc"
style="margin: 0 0 2rem 0;"
frameborder="0"
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
Follow these steps and to get a sample app running in less than 5 minutes.
- Make sure that you have Python 3.6 or greater installed.
- Install Streamlit using PIP:
$ pip install streamlit
- Run the hello world app:
$ streamlit hello
- That's it! In the next few seconds the sample app will open in a new tab in your default browser.
The easiest way to learn how to use Streamlit is to actually try it out. Our get started guide walks you through the basics of building a Streamlit app.
.. raw:: html
<iframe
width="560"
height="315"
src="https://www.youtube.com/embed/VtrFjkSGgKM"
style="margin: 0 0 2rem 0;"
frameborder="0"
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
Create an app to explore a dataset of Uber ride pickups in New York City. You'll learn about caching, drawing charts, plotting data on a map, and how to use interactive widgets.
The quickest way to get help is to reach out on our community forum. We love to hear our users' questions, ideas, and bugs — please share!