A faster way to build and share data apps
Streamlit turns data scripts into shareable web apps in minutes.
All in pure Python. No front‑end experience required.
All in pure Python. No front‑end experience required.
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Learn more:
Why Generative AI + Streamlit are a perfect matchTrusted by over 90% of Fortune 50 companies
As of 2024/11/15
Get started in under a minute
Streamlit’s open-source app framework is a breeze to get started with.
Just choose your adventure:
Install Streamlit locally
And you're ready to go!
or...
Skip installation! Use Community Cloud
- Code in a side-by-side browser editor
- Share instantly
Next, check out our documentation and forums for more.
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 • 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
Effortlessly share, manage and deploy your apps, directly from Streamlit. All for free!
Build powerful apps
Used in the world’s 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!
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
@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!
#python#streamlit#prototyping
Where were you my whole life
@streamlit I wanted someone like you since forever!
If you do ML and work with Data
@streamlit will breathe life into your work.
What an awesome library
@streamlit is!!!!!! 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.
Tried
@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#Python#MachineLearning
This past week I played with
@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
@streamlit and@plotlygraphs tonight. Streamlit is such a pleasure to use and will definitely be my first choice for my dashboarding needshttps://github.com/benjaminjack/streamlit-pca
#datascience
The
@streamlit hype is real, this app went from zero to deployed in one night!#python#DataScience https://nba-roster-turnover.herokuapp.com/
Using
@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#DataScience use-case. Heck it's even enjoyable! They do right everything Jupyter notebooks got wrong.
In building end to end
#MachineLearning#webapps my time distribution was:
Actual logic and ml part : 20 %
Frontend : 80%
After@streamlit it has become:
Logic and ml part: 100%@streamlit is
Streamlit is a blessing for data scientists. There’s 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
@streamlit. Using default Streamlit components. I think it would take 10 times longer with HTML/JS. Now I can focus on functionality, not on div alignment
Code: https://github.com/katanaml/sparrow/tree/main/sparrow-ui
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
@streamlit to build a live animated map
I'd never heard of them before this week but very impressed with the speed from idea->data->visualisations
Every new
@streamlit release feels like Christmas
I just discovered the most beautiful thing ever created.
@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
@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
@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!#python#streamlit#prototyping
Where were you my whole life
@streamlit I wanted someone like you since forever!
If you do ML and work with Data
@streamlit will breathe life into your work.
What an awesome library
@streamlit is
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.
Tried
@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#Python#MachineLearning
This past week I played with
@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
@streamlit and@plotlygraphs tonight. Streamlit is such a pleasure to use and will definitely be my first choice for my dashboarding needs#datascience
The
@streamlit hype is real, this app went from zero to deployed in one night!#python#DataScience https://nba-roster-turnover.herokuapp.com/
Using
@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#DataScience use-case. Heck it's even enjoyable! They do right everything Jupyter notebooks got wrong.
In building end to end
#MachineLearning#webapps my time distribution was:
Actual logic and ml part : 20 %
Frontend : 80%
After@streamlit it has become:
Logic and ml part: 100%@streamlit is
Streamlit is a blessing for data scientists. There’s 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
@streamlit. Using default Streamlit components. I think it would take 10 times longer with HTML/JS. Now I can focus on functionality
Code: https://github.com/katanaml/sparrow/tree/main/sparrow-ui
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
@streamlit to build a live animated map
I'd never heard of them before this week but very impressed with the speed from idea->data->visualisations
Every new
@streamlit release feels like Christmas
I just discovered the most beautiful thing ever created.
@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
@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
@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!#python#streamlit#prototyping
Where were you my whole life
@streamlit I wanted someone like you since forever!
If you do ML and work with Data
@streamlit will breathe life into your work.
What an awesome library
@streamlit is
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.
Tried
@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#Python#MachineLearning
This past week I played with
@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
@streamlit and@plotlygraphs tonight. Streamlit is such a pleasure to use and will definitely be my first choice for my dashboarding needs#datascience
The
@streamlit hype is real, this app went from zero to deployed in one night!#python#DataScience https://nba-roster-turnover.herokuapp.com/
Using
@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#DataScience use-case. Heck it's even enjoyable! They do right everything Jupyter notebooks got wrong.
In building end to end
#MachineLearning#webapps my time distribution was:
Actual logic and ml part : 20 %
Frontend : 80%
After@streamlit it has become:
Logic and ml part: 100%@streamlit is
Streamlit is a blessing for data scientists. There’s 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
@streamlit. Using default Streamlit components. I think it would take 10 times longer with HTML/JS. Now I can focus on functionality
Code: https://github.com/katanaml/sparrow/tree/main/sparrow-ui
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
@streamlit to build a live animated map
I'd never heard of them before this week but very impressed with the speed from idea->data->visualisations
Every new
@streamlit release feels like Christmas
I just discovered the most beautiful thing ever created.
@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
@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.
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