Dataprep

Abstract

1. Introduction

DataPrep.EDA is the fastest and the easiest EDA (Exploratory Data Analysis) tool in Python. It allows you to understand a Pandas/Dask DataFrame with a few lines of code in seconds.

2. Objective

Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

This project demonstrates how Python can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Automatic dependency creation: When there are dependency among the SQL files, and those tables are not yet in the database, the lineage module will automatically tries to find the dependency table and creates it.
• Clean and simple but very interactive user interface: The user interface is very simple to use with minimal clutters on the page while showing all of the necessary information.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/sfu-db/dataprep.git
cd dataprep

pip install -U dataprep

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by sfu-db and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.