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When you're working with data, it is extremely valuable to visualize that data quickly, interactively, and from multiple different angles It can become slow or crash when handling complex visualizations. That's what streamlit is actually built and optimized for

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Streamlit’s allure lies in its ability to transform python scripts into interactive web applications with minimal effort In my case this is an. However, when you introduce large datasets into the mix, that.

I tested 4 major libraries with the same dataset and discovered some surprising performance bottlenecks and usability quirks

Here's what i learned from a frontend. My streamlit app reads and visualizes data from a large csv file (~100mb) The app takes several seconds to load What are the best practices for improving.

Streamlit supports several different charting libraries, and our goal is to continually add support for more Right now, the most basic library in our arsenal is matplotlib If you are working with large or complex datasets, i highly recommend giving streamlit a try I am trying to visualise on the map a large dataset (could be millions of polygons)

I am using pydeck polygon layers and a pandas dataset