Abstract
Hands On Data Analysis With Pandas 2nd Edition is an open-source Data Science project. Materials for following along with Hands-On Data Analysis with Pandas – Second Edition. 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. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. It is built using Jupyter Notebook, Machine Learning, Pandas. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
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. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn.
This is the code repository for my book Hands-On Data Analysis with Pandas, published by Packt on July 26, 2019 (1st edition) and April 29, 2021 (2nd edition).
Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data.
2. Objective
Materials for following along with Hands-On Data Analysis with Pandas – Second Edition
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
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition.git
cd Hands-On-Data-Analysis-with-Pandas-2nd-editionFull 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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- 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.
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