Skimpy

Abstract

1. Introduction

It is recommended that you set your datatypes before using skimpy (for example converting any text columns to pandas string datatype), as this will produce richer statistical summaries. However, the skim() function will try and guess what the datatypes of your columns are.

A light weight tool for creating summary statistics from dataframes.

Think of it as a super-charged version of pandas' df.describe(). You can find the documentation here.

2. Objective

skimpy is a light weight tool that provides summary statistics about variables in data frames within the console.

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

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/aeturrell/skimpy.git
cd skimpy

from skimpy import skim

skim(df)

from skimpy import generate_test_data, skim

df = generate_test_data()
skim(df)

$ pip install skimpy

$ pip install git+https://github.com/aeturrell/skimpy.git

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 aeturrell 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.