Every data tells a story. It’s easier to understand them through visual representations rather than looking at thousands of records.Data Scientists often leverage graphs such as bar charts, line plots, area curves, etc to communicate this story to their stakeholders.Python and R have been preferred language of choice when working with data. There are multiple libraries such as Matplotlib, Seaborn, pyVis, etc through which one creates visualizations inside the Jupyter Notebooks or stand-alone apps using Bokeh, Flask, for example.

In this article, I will try to introduce you to Streamlit and get familiar with it. At the end of this article, you will be able to create an interactive app based on your data. Let’s take a publicly available data set from Kaggle. In the end, we will be able to create an app like this.

#streamlit #python-app-development #dashboard #deep-learning

Build Quick and Beautiful Apps using Streamlit
11.20 GEEK