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What is Streamlit?

Streamlit is an open-source Python library designed to make it easy to build custom web-apps for machine learning and data science, from the company of the same name in San Francisco. Streamlit also hosts its community's Streamlit Component offered…

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What is Streamlit?

Streamlit is an open-source Python library designed to make it easy to build custom web-apps for machine learning and data science, from the company of the same name in San Francisco. Streamlit also hosts its community's Streamlit Component offered via API to help users get started.

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Product Demos

Python Initial Setup | #python #Streamlit #virtualEnvironment


How to customize the themes of your Streamlit web apps | Streamlit #24


Google Maps Data Visualization using Streamlit


Build a data storytelling web app in Python with ipyvizzu | Streamlit tutorial


Real time Face emotion detection using streamlit and deep-learning CNN model.


Create Dashboard with Folium Map, Streamlit and Python - Full Course

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Product Details

What is Streamlit?

Streamlit Technical Details

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Reviews and Ratings



(1-1 of 1)
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Score 10 out of 10
Vetted Review
Verified User
Our Data Science team has built an entire multi-page reporting dashboard for the executive team to analyze all of their metrics (and we've started including machine learning models in the pages) in a really short amount of time. When a new report / metric page is needed, we can completely deliver (using Streamlit) in a day or two max. That's with authentication, logging, design, computing and testing.

We've also built a few integrations with access to our internal databases that allow a non-technical person to query a database themselves, in a simple manner and get answers they're looking for. One of our Customer Success agents wanted a joined file from multiple different databases and be able to download it whenever she wanted. Not knowing SQL or anything, she has a user interface that allows her to select checkboxes and build the query she wants without having any technical knowledge.
  • Incredibly Easy
  • Customizable
  • Quick and powerful
  • Recent Security issues (they quickly released an update to combat this though...)
  • Requires a bit of HTML knowledge to really customize. If you're going quick, you don't need HTML though. Streamlit commands will pump your page out fast.
- Don't want to pay Tableau $1,000 / seat? Use Streamlit
- Want fully custom views and navigation? Use Streamlit
- Want access to Machine Learning and not just your dev team? Use Streamlit
- Want to keep things internal and secure? Use Streamlit
- Want your Data Science team to be able to crank out projects quickly? Use Streamlit
- Sick of Jupyter Notebooks and Business Leaders not understanding them? Use Streamlit

Our D.S. strategy has moved completely to delivering pages in Streamlit. I can hand an executive a Jupyter notebook and it'll get lost in translation. I can give them sign-in access to a page and they can answer all of their own "What-If?" questions! We've used Streamlit to productize our Data Science and Machine Learning capabilities.
  • Incredibly Easy to Use and Learn
  • Gives end users an interface that's clean and doesn't require non-python work
  • Renders pages & data really quickly.
  • They've really thought through a ton of their features and provide help docs that are easy to get started.
  • I've scaled my team 2x since using Streamlit. We show off actual results that users can play with
  • We're building a customer facing page that we're going to monetize.
  • Incredible amounts of visibility into my team and what we're accomplishing.
I started using Streamlit when it first came out and thought it was really useful and powerful. A few years later and they've really hit their stride! The features / widgets / materials they provide have been well researched, well designed, and well implemented. I will take Streamlit to any future companies I go to as well as be a strong promoter wherever I'm currently at. It's free. It's easy to use. It is really powerful. Sure? You could go pay for a larger system but your Data Science team should be able to handle Streamlit easily. I'd argue a non-technical person spending a few weeks in python could pick up Streamlit really quickly.
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