Hands On Data Analysis With Pandas

Abstract

1. Introduction

This is the code repository for my book Hands-On Data Analysis with Pandas, published by Packt on July 26, 2019.

Data analysis has become an essential skill in a variety of domains where knowing how to work with data and extract insights can generate significant value.

By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analysis across multiple domains.

2. Objective

Materials for following along with Hands-On Data Analysis with Pandas.

This project demonstrates how Jupyter Notebook, Machine Learning, 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 with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• 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/stefmolin/Hands-On-Data-Analysis-with-Pandas.git
cd Hands-On-Data-Analysis-with-Pandas

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 stefmolin 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.