Build an EDA Playground with Streamlit – Towards AI A hands-on guide to building your own Data Prep & Visualization app with Streamlit We’ve all been there: excited to try a new model on a fresh dataset, only to spend hours filling missing values, encoding categorical variables, scaling features, and so on. And every time we get a new dataset, we end up writing the same code to perform an Exploratory Data Analysis. Last week, while working on a project and cleaning a complex dataset, I asked myself: “Why can’t I just click around and prep my data interactively?” It turns out, we can. In this article, we’ll build a Data Analysis Playground — a Streamlit app that allows users to upload a CSV file, explore the data, perform preprocessing steps interactively, and even download the cleaned dataset. Why Streamlit? For those unfamiliar with Streamlit, it is a Python library that transforms scripts into interactive web applications. It’s ideal for building quick dashboards, experiments, and internal tools. Our app will offer a user-friendly interface for handling common data preparation tasks. Users can upload their data, view summaries, check for missing values, select columns to drop or keep, encode categorical variables, scale numerical features, and more. All of this will be done through simple dropdowns, sliders, and buttons, without requiring any coding. The goal is to create a flexible and reusable tool that can be used for different datasets, saving time and effort during the initial stages of any data science project. The app will also include visualization capabilities, allowing users to explore the data through histograms, scatter plots, and correlation matrices. This makes it easy to understand the distribution and relationships between variables before moving on to model building. By building this interactive EDA playground, we aim to streamline the data preparation process and make it more accessible, especially for those who may not have deep coding expertise. The app will be designed with a clean and intuitive layout, ensuring that users can navigate through the different steps of data analysis effortlessly. It will be built using Python, leveraging libraries such as pandas for data manipulation, matplotlib and seaborn for visualization, and of course, Streamlit for the interactive interface. This project demonstrates how Streamlit can be used to create powerful, user-friendly tools that simplify complex data workflows. Whether you're a data scientist, analyst, or student, having a visual and interactive EDA tool can significantly enhance your productivity and understanding of the data.