I Built a Tool That Automates the Most Tedious Part of Data Science

Every data scientist knows the pain of writing the same exploratory analysis code from scratch. Here’s how I fixed it.

There’s a ritual every data scientist goes through when they encounter a new dataset. Open a notebook. Import pandas. Run .info(). Check for nulls. Plot distributions. Hunt for outliers. Repeat.

After going through this cycle for the dozenth time while learning data science, I asked myself a simple question: why am I writing this from scratch every single time?

Sound familiar? This is the loop I wanted to break.

The problem with EDA boilerplate

Exploratory Data Analysis is essential — before you build any model or draw conclusions, you need to understand your data’s shape, quirks, and missing pieces. But the first 80% of that process is almost always identical, regardless of the dataset.

That repetition isn’t just tedious. It’s a cognitive tax. Every minute spent writing setup code is a minute not spent on the actual analysis that matters. So I built a Streamlit web app to handle it automatically.

What the tool does

Upload a CSV and it immediately gets to work. Four things happen automatically, without writing a single line of code:

What I learned building it

The most interesting engineering decision was the outlier detection logic. Most tutorials pick one method and apply it universally. But that’s statistically wrong — Z-Score assumes normality, and IQR doesn’t. Building in the skewness check taught me to think about when to apply a technique, not just how.

Streamlit also proved to be a genuinely enjoyable framework for this kind of tool. The feedback loop between code and UI is remarkably fast, which makes iteration easy. I’d recommend it to any data scientist who wants to build quick internal tools without touching frontend code.

What’s next

The tool is still a work in progress. I have plans for correlation analysis, feature relationship plots, and better handling of datetime columns. If you have a suggestion — or a dataset that breaks it — I’d genuinely love to hear about it in the comments.

Try it yourself or explore the source:

github repo — https://github.com/Devm2512/EDA-Tool

Tool Link: https://devm2512-eda-tool.streamlit.app/