Python Data Analysis, Fourth Edition: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering

Python Data Analysis, Fourth Edition: A Modern Analytics Toolkit

Python Data Analysis, Fourth Edition is built for readers who want more than isolated Python snippets. It maps a path from the language and libraries that make data work possible to the modern analytics stack: machine learning, deep learning, generative AI, LLMs, and data engineering. Published by Packt in 2026, this fourth edition is written by Avinash Navlani and Cornellius Yudha Wijaya, bringing together academic, industry, and applied AI experience.

A Practical Path Through the Python Data Stack 💻

The early chapters establish the working vocabulary of Python analytics. Readers encounter the essential libraries, NumPy arrays, pandas DataFrames, statistical reasoning, and linear algebra. Rather than treating these as separate topics, the book connects them to the daily tasks of cleaning, querying, and interpreting real data.

From Exploration to Visualization 📊

Exploratory data analysis and data cleaning receive sustained attention. The book covers missing values, date handling, grouping and joining, probability sampling, hypothesis testing, and correlation. Visualization chapters show how to build scatter plots, line charts, histograms, subplots, and seaborn graphics, helping readers see patterns before they model them.

Modern Analytics, Not Just the Basics 🧠

The fourth edition’s subtitle points to the broader landscape: machine learning, deep learning, generative AI, large language models, and data engineering. This is a book that positions Python data analysis as part of a larger production and AI workflow, not as a standalone classroom exercise.

Who Will Get the Most From This Edition

Analysts moving beyond spreadsheets, data scientists who need a structured Python reference, developers entering analytics, and students or educators looking for a practical course companion will find the progression useful. The material assumes a willingness to work with code and data, and it rewards that effort with a broad, current view of the field.

Why the Fourth Edition Matters

Data analysis has changed since the first edition appeared in 2014. This update reflects that shift, adding contemporary coverage of GenAI, LLMs, deep learning, and data engineering while keeping the foundational Python skills that remain essential. The result is a reference that can sit beside you through several stages of your analytics work.

Start Working with Data in Python

If you want a single guide that moves from NumPy and pandas to statistical thinking, visualization, machine learning, and modern AI-era data work, Python Data Analysis, Fourth Edition is a strong addition to your digital library. It is available from Digital Delights as an ebook for readers who prefer to learn and reference on screen.