A faster way to build and share data apps
Turn your data scripts into shareable web apps in minutes.
All in pure Python. No frontβend experience required.
All in pure Python. No frontβend experience required.
Play
Learn more with the
Streamlit crash course on YouTube Trusted by over 90% of Fortune 50 companies
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Get started in under a minute
Streamlit is an open-source app framework that is a breeze to get started with.
Just install it like any other Python library:
And that's it! Next, check out our documentation and forums for more.
Or you can skip local installation altogether:
- βTry a live playground in your browserThe easiest way to try Streamlit before you install.
- βBuild in public with Streamlit Community CloudPublic apps only. Totally free. You just need a GitHub account.
- βBuild like a pro on SnowflakeUnlimited private apps. Enterprise-grade reliability and security.
Streamlit builds upon
three simple principles
Embrace scripting
Build an app in a few lines of code with our magically simple API. Then see it automatically update as you iteratively save the source file.
MyApp.py
- import streamlit as st
- import pandas as pd
- st.write("""
- # My first app
- Hello *world!*
- """)
- df = pd.read_csv("my_data.csv")
- st.line_chart(df)
My App β’ Streamlit
My first app
Hello world!
Weave in interaction
Adding a widget is the same as declaring
a variable. No need to write a backend, define routes, handle HTTP
requests, connect a frontend, write HTML, CSS, JavaScript, ...
Deploy instantly
The choice is yours — show off your public apps for free on Streamlit Community Cloud, go with Snowflake for enterprise‑grade deployment, or pick something else entirely!
Build powerful apps
Used in the worldβs top data science groups
Neil Treat
Google X
"Write production-level code while producing shareable artifacts."
Kevin Zielnicki
Stitch Fix
"...a great way to share machine learning models and analyses."
Emmanuel Ameisen
Insight Data Science
"Streamlit bridges experimentation and production."
Dominik Moritz
Vega-Lite
"It's the next step in ML and data science tools."
Danny Nguyen
Yelp
"Streamlit apps are way easier to put together and iterate on."
Koen Havlik
Uber
"Streamlit democratizes building data apps."
and...
Compatible with
Basically everything!
- Bokeh
- Altair
- PyTorch
- OpenCV
- Deck.Gl
- Pandas
- Vega-Lite
- Matplotlib
- NumPy
- Scikit Learn
- Tensorflow
- Plotly
- Keras
And even more, with Streamlit Components!
Build your own, share with the community, bask in the glory.
Build your own, share with the community, bask in the glory.
See why developers Streamlit
Really really pleased with
(ει€) @ (ει€γγγΎγ§)streamlit so far. Used it to build a clickable prototype for a complex piece of a web application. It turned out faster and more flexible than everything else I could find. Highly recommended! π(ει€) # (ει€γγγΎγ§)python(ει€) # (ει€γγγΎγ§)streamlit(ει€) # (ει€γγγΎγ§)prototyping
Where were you my whole life
(ει€) @ (ει€γγγΎγ§)streamlit I wanted someone like you since forever!
If you do ML and work with Data
(ει€) @ (ει€γγγΎγ§)streamlit will breathe life into your work.
What an awesome library
(ει€) @ (ει€γγγΎγ§)streamlit is πππ!!!!!! So much productive, easy and flexible.
From coding to deployment in just 2 days (since it was new for me).
Probably i should boost up and do more projects using it.π
Tried
(ει€) @ (ει€γγγΎγ§)streamlit today and believe me I regret spending those hours working on HTML and Javascript to build a Web apps for my Algorithms for demo.
It's one of the fastest and simple way to make a web app and showcase your work using python(ει€) # (ει€γγγΎγ§)Python(ει€) # (ει€γγγΎγ§)MachineLearning
This past week I played with
(ει€) @ (ει€γγγΎγ§)streamlit to bring some advanced models + visuals to a non-technical team. Very easy to build & deploy and very impressive final product.
Honestly, thinking more about it, I think this is a game-changer like IPython Notebooks were in 2013. https://twitter.com/calogica/status/1180844807259734016
I spent the day playing with Streamlit, which is like Shiny for python, and here's my initial review:
It is very good.
Put together this simple PCA dashboard with
(ει€) @ (ει€γγγΎγ§)streamlit and(ει€) @ (ει€γγγΎγ§)plotlygraphs tonight. Streamlit is such a pleasure to use and will definitely be my first choice for my dashboarding needs π₯ https://github.com/benjaminjack/streamlit-pca(ει€) # (ει€γγγΎγ§)datascience
The
(ει€) @ (ει€γγγΎγ§)streamlit hype is real, this app went from zero to deployed in one night!(ει€) # (ει€γγγΎγ§)python(ει€) # (ει€γγγΎγ§)DataScience https://nba-roster-turnover.herokuapp.com/
Using
(ει€) @ (ει€γγγΎγ§)streamlit and for the first time in a very long while, or ever, I don't swear under my breath while writing the UI/demo code for a(ει€) # (ει€γγγΎγ§)DataScience use-case. Heck it's even enjoyable! They do right everything Jupyter notebooks got wrong.
In building end to end
(ει€) # (ει€γγγΎγ§)MachineLearning(ει€) # (ει€γγγΎγ§)webapps my time distribution was:
Actual logic and ml part : 20 %
Frontend : 80%
After(ει€) @ (ει€γγγΎγ§)streamlit it has become:
Logic and ml part: 100%(ει€) @ (ει€γγγΎγ§)streamlit is β€οΈ
Streamlit is a blessing for data scientists. Thereβs no two ways about it. It not only helps them to build ML web applications, but also conveniently share and demonstrate their models to stakeholders, customers and colleagues especially if they are non-technical
It took me ~1 hour to build this dashboard (data is dummy) layout in
(ει€) @ (ει€γγγΎγ§)streamlit. Using default Streamlit components. I think it would take 10 times longer with HTML/JS. Now I can focus on functionality π, not on div alignment π€£
Code: https://github.com/katanaml/sparrow/tree/main/sparrow-ui
π Streamlit
This one is just impressive. Create and deploy data-driven web apps in the simplest way possible. These apps look great, are easy to update, and can even be interactive. Check it out: https://streamlit.io/
Just spent this week using
(ει€) @ (ει€γγγΎγ§)streamlit to build a live animated map πΊ
I'd never heard of them before this week but very impressed with the speed from idea->data->visualisations π
Every new
(ει€) @ (ει€γγγΎγ§)streamlit release feels like Christmas ππ πβ
I just discovered the most beautiful thing ever created.
(ει€) @ (ει€γγγΎγ§)streamlit I friggin love you
No hassle,no complications,no drama
Just straight up works like a dream
Ugh.....I could cry
Machine learning just got a whole lot fun-er
Que belleza celestial streamlit y su simplicidad para crear dashboards con Python en 2 segundos
Not gonna lie. The hours I spend each week working in
(ει€) @ (ει€γγγΎγ§)streamlit are my favorites.
Productionizing your machine learning model is a mandatory part of your ML project lifecycle.
In that context, I have found Streamlit to be very effective and practical, not to mention how fun it is.
Really really pleased with
(ει€) @ (ει€γγγΎγ§)streamlit so far. Used it to build a clickable prototype for a complex piece of a web application. It turned out faster and more flexible than everything else I could find. Highly recommended! π(ει€) # (ει€γγγΎγ§)python(ει€) # (ει€γγγΎγ§)streamlit(ει€) # (ει€γγγΎγ§)prototyping
Where were you my whole life
(ει€) @ (ει€γγγΎγ§)streamlit I wanted someone like you since forever!
If you do ML and work with Data
(ει€) @ (ει€γγγΎγ§)streamlit will breathe life into your work.
What an awesome library
(ει€) @ (ει€γγγΎγ§)streamlit is πππ!!!!!! So much productive, easy and flexible.
From coding to deployment in just 2 days (since it was new for me).
Probably i should boost up and do more projects using it.π
Tried
(ει€) @ (ει€γγγΎγ§)streamlit today and believe me I regret spending those hours working on HTML and Javascript to build a Web apps for my Algorithms for demo.
It's one of the fastest and simple way to make a web app and showcase your work using python(ει€) # (ει€γγγΎγ§)Python(ει€) # (ει€γγγΎγ§)MachineLearning
This past week I played with
(ει€) @ (ει€γγγΎγ§)streamlit to bring some advanced models + visuals to a non-technical team. Very easy to build & deploy and very impressive final product.
Honestly, thinking more about it, I think this is a game-changer like IPython Notebooks were in 2013. https://twitter.com/calogica/status/1180844807259734016
I spent the day playing with Streamlit, which is like Shiny for python, and here's my initial review:
It is very good.
Put together this simple PCA dashboard with
(ει€) @ (ει€γγγΎγ§)streamlit and(ει€) @ (ει€γγγΎγ§)plotlygraphs tonight. Streamlit is such a pleasure to use and will definitely be my first choice for my dashboarding needs π₯ https://github.com/benjaminjack/streamlit-pca(ει€) # (ει€γγγΎγ§)datascience
The
(ει€) @ (ει€γγγΎγ§)streamlit hype is real, this app went from zero to deployed in one night!(ει€) # (ει€γγγΎγ§)python(ει€) # (ει€γγγΎγ§)DataScience https://nba-roster-turnover.herokuapp.com/
Using
(ει€) @ (ει€γγγΎγ§)streamlit and for the first time in a very long while, or ever, I don't swear under my breath while writing the UI/demo code for a(ει€) # (ει€γγγΎγ§)DataScience use-case. Heck it's even enjoyable! They do right everything Jupyter notebooks got wrong.
In building end to end
(ει€) # (ει€γγγΎγ§)MachineLearning(ει€) # (ει€γγγΎγ§)webapps my time distribution was:
Actual logic and ml part : 20 %
Frontend : 80%
After(ει€) @ (ει€γγγΎγ§)streamlit it has become:
Logic and ml part: 100%(ει€) @ (ει€γγγΎγ§)streamlit is β€οΈ
Streamlit is a blessing for data scientists. Thereβs no two ways about it. It not only helps them to build ML web applications, but also conveniently share and demonstrate their models to stakeholders, customers and colleagues especially if they are non-technical
It took me ~1 hour to build this dashboard (data is dummy) layout in
(ει€) @ (ει€γγγΎγ§)streamlit. Using default Streamlit components. I think it would take 10 times longer with HTML/JS. Now I can focus on functionality π, not on div alignment π€£
Code: https://github.com/katanaml/sparrow/tree/main/sparrow-ui
π Streamlit
This one is just impressive. Create and deploy data-driven web apps in the simplest way possible. These apps look great, are easy to update, and can even be interactive. Check it out: https://streamlit.io/
Just spent this week using
(ει€) @ (ει€γγγΎγ§)streamlit to build a live animated map πΊ
I'd never heard of them before this week but very impressed with the speed from idea->data->visualisations π
Every new
(ει€) @ (ει€γγγΎγ§)streamlit release feels like Christmas ππ πβ
I just discovered the most beautiful thing ever created.
(ει€) @ (ει€γγγΎγ§)streamlit I friggin love you
No hassle,no complications,no drama
Just straight up works like a dream
Ugh.....I could cry
Machine learning just got a whole lot fun-er
Que belleza celestial streamlit y su simplicidad para crear dashboards con Python en 2 segundos
Not gonna lie. The hours I spend each week working in
(ει€) @ (ει€γγγΎγ§)streamlit are my favorites.
Productionizing your machine learning model is a mandatory part of your ML project lifecycle.
In that context, I have found Streamlit to be very effective and practical, not to mention how fun it is.
Really really pleased with
(ει€) @ (ει€γγγΎγ§)streamlit so far. Used it to build a clickable prototype for a complex piece of a web application. It turned out faster and more flexible than everything else I could find. Highly recommended! π(ει€) # (ει€γγγΎγ§)python(ει€) # (ει€γγγΎγ§)streamlit(ει€) # (ει€γγγΎγ§)prototyping
Where were you my whole life
(ει€) @ (ει€γγγΎγ§)streamlit I wanted someone like you since forever!
If you do ML and work with Data
(ει€) @ (ει€γγγΎγ§)streamlit will breathe life into your work.
What an awesome library
(ει€) @ (ει€γγγΎγ§)streamlit is πππ!!!!!! So much productive, easy and flexible.
From coding to deployment in just 2 days (since it was new for me).
Probably i should boost up and do more projects using it.π
Tried
(ει€) @ (ει€γγγΎγ§)streamlit today and believe me I regret spending those hours working on HTML and Javascript to build a Web apps for my Algorithms for demo.
It's one of the fastest and simple way to make a web app and showcase your work using python(ει€) # (ει€γγγΎγ§)Python(ει€) # (ει€γγγΎγ§)MachineLearning
This past week I played with
(ει€) @ (ει€γγγΎγ§)streamlit to bring some advanced models + visuals to a non-technical team. Very easy to build & deploy and very impressive final product.
Honestly, thinking more about it, I think this is a game-changer like IPython Notebooks were in 2013. https://twitter.com/calogica/status/1180844807259734016
I spent the day playing with Streamlit, which is like Shiny for python, and here's my initial review:
It is very good.
Put together this simple PCA dashboard with
(ει€) @ (ει€γγγΎγ§)streamlit and(ει€) @ (ει€γγγΎγ§)plotlygraphs tonight. Streamlit is such a pleasure to use and will definitely be my first choice for my dashboarding needs π₯ https://github.com/benjaminjack/streamlit-pca(ει€) # (ει€γγγΎγ§)datascience
The
(ει€) @ (ει€γγγΎγ§)streamlit hype is real, this app went from zero to deployed in one night!(ει€) # (ει€γγγΎγ§)python(ει€) # (ει€γγγΎγ§)DataScience https://nba-roster-turnover.herokuapp.com/
Using
(ει€) @ (ει€γγγΎγ§)streamlit and for the first time in a very long while, or ever, I don't swear under my breath while writing the UI/demo code for a(ει€) # (ει€γγγΎγ§)DataScience use-case. Heck it's even enjoyable! They do right everything Jupyter notebooks got wrong.
In building end to end
(ει€) # (ει€γγγΎγ§)MachineLearning(ει€) # (ει€γγγΎγ§)webapps my time distribution was:
Actual logic and ml part : 20 %
Frontend : 80%
After(ει€) @ (ει€γγγΎγ§)streamlit it has become:
Logic and ml part: 100%(ει€) @ (ει€γγγΎγ§)streamlit is β€οΈ
Streamlit is a blessing for data scientists. Thereβs no two ways about it. It not only helps them to build ML web applications, but also conveniently share and demonstrate their models to stakeholders, customers and colleagues especially if they are non-technical
It took me ~1 hour to build this dashboard (data is dummy) layout in
(ει€) @ (ει€γγγΎγ§)streamlit. Using default Streamlit components. I think it would take 10 times longer with HTML/JS. Now I can focus on functionality π, not on div alignment π€£
Code: https://github.com/katanaml/sparrow/tree/main/sparrow-ui
π Streamlit
This one is just impressive. Create and deploy data-driven web apps in the simplest way possible. These apps look great, are easy to update, and can even be interactive. Check it out: https://streamlit.io/
Just spent this week using
(ει€) @ (ει€γγγΎγ§)streamlit to build a live animated map πΊ
I'd never heard of them before this week but very impressed with the speed from idea->data->visualisations π
Every new
(ει€) @ (ει€γγγΎγ§)streamlit release feels like Christmas ππ πβ
I just discovered the most beautiful thing ever created.
(ει€) @ (ει€γγγΎγ§)streamlit I friggin love you
No hassle,no complications,no drama
Just straight up works like a dream
Ugh.....I could cry
Machine learning just got a whole lot fun-er
Que belleza celestial streamlit y su simplicidad para crear dashboards con Python en 2 segundos
Not gonna lie. The hours I spend each week working in
(ει€) @ (ει€γγγΎγ§)streamlit are my favorites.
Productionizing your machine learning model is a mandatory part of your ML project lifecycle.
In that context, I have found Streamlit to be very effective and practical, not to mention how fun it is.
Deploy on enterprise
Try Streamlit in Snowflake. Code in the browser, collaborate with Git, deploy in one click. With the security and reliability of Snowflake.