With caching, data is only loaded into the app once. In our case, for example, without caching, every time a user interacts with the app and the app is refreshed, the data is reloaded and re-computed which could make the app run slow and lead to poor user experience. Second, Streamlit provides a caching mechanism that allows your app to stay performant when loading, reading or manipulating large amount of data that may cause expensive computations. applying filters), Streamlit reruns your entire Python script from top to bottom. Every time if anything is changed in the code or a user interacts with the app (e.g. ![]() In the code above, a few import things to notice/remember:įirst, streamlit reads and executes your script from top to bottom. Remember, open source is the trend (like Python replaces SAS’s domination in analytics industry within just a few years) so think forward and prepare yourself ahead of the curve! When I came across Streamlit, I immediately fell in love with it! It is all python so if you already know python (like many data scientists do) it will be super easy to leverage Streamlit to get your dashboards or data apps up and running with just a few lines of code, without needing to have any front-end web application development knowledge.Īnd the best part? It’s free! It’s free! It’s free! Important things said three times!□ So if you are a freelance data scientist or thinking about starting your own analytics consulting business, I strongly recommend that you explore Streamlit and add it to your toolkit for its simplicity, flexibility, scalability and free of cost.įor Tableau users, I would also recommend that you take some time to explore and learn Python and Streamlit. While I like Tableau for its comprehensive analytics and visualization capabilities, I have been searching for a flexible, free-cost and scalable alternative for my own analytical projects. I am a long-time Tableau user and have been using Tableau as my primary tool to create interactive dashboards and data visualizations for all kinds of analytical tasks at work. Why You Should Add Streamlit To Your Data Scientist’s Toolkit The dashboard/web app we are building will look something like this. You can save this example as a starter template that you can re-use and expand in your future projects. You will go through the complete end-to-end process of creating this web app by coding in python and Streamlit using two open datasets that are available to download for free.īy the end of this tutorial, you should have a solid understanding of the core concepts of Streamlit and its wonderful capabilities. ![]() In this tutorial, I’ll give you a brief introduction about what Streamlit is, why I love it and recommend it to anyone who works in the analytics and data science field, and how to install it on your computer.Īfter you are all set with the installation, I’ll walk you through, step-by-step, a detailed hands-on example of using Streamlit to create your first interactive dashboard by visualizing the U.S. ![]() ![]() In my opinion, Streamlit should be a must-have tool in any data scientist’s toolkit for its simplicity, flexibility, robustness and free of cost. I have long wanted to write a Streamlit tutorial ever since I started to use this fantastic python-based framework to build interactive dashboards and machine learning web apps.
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