You're reading from Python Data Analysis Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering

Product type Paperback

Published in Jun 2026

Publisher Packt

ISBN-13 9781806022878

Length 766 pages

Edition 4th Edition

Languages

Python

Tools

Plotly

Concepts

Data Analysis

Authors (2):

Avinash Navlani

Cornellius Yudha Wijaya

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Table of Contents (25) Chapters

Preface

1. Part 1: Foundations for Data Analysis

2. Getting Started with Python Libraries FREE CHAPTER

3. NumPy and Pandas

4. Statistics for Data Insights

5. Linear Algebra

6. Part 2: Exploratory Data Analysis and Data Cleaning

7. Data Visualization

8. Retrieving, Processing, and Storing Data

9. Cleaning Messy Data

10. Time-Series Analysis

11. Part 3: Deep Dive into Machine Learning

12. Supervised Learning: Regression and Classification

13. Unsupervised Learning: Dimensionality Reduction, Clustering, Anomaly Detection

14. Ensemble Methods: Bagging and Boosting Methods

15. Artificial Neural Networks and Deep Learning

16. Part 4: NLP, Image Analytics, and Parallel Computing

17. Analyzing Text Data

18. Analyzing Image Data

19. LLMs and Gen AI

20. Parallel Computing Using Dask, Modin, and Ray

21. Big Data Analytics Using PySpark

22. Unlock Access to the Code Bundle and the PDF Version

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23. Other Books You May Enjoy

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24. Index

Summary

In this chapter, we began with Matplotlib to build static visualizations and learned how chart elements such as titles, labels, legends, subplots, and annotations make figures clearer and more presentation-ready. We then used Pandas to create quick visual summaries directly from DataFrames, and Seaborn to produce statistically meaningful views of distributions, relationships, correlations, and group-level patterns with less code. In addition, we learned how to create interactive visualizations using Plotly, including features like hover interactions, zooming, dropdowns, sliders, custom buttons, and multi-layer plots. Finally, we introduced the Dash framework for building interactive web-based dashboards, enabling the development of applications with interactive charts, data tables, multi-page layouts, and real-time data updates.

Because this chapter covers several chart types and visualization libraries, it is useful to pause and compare when each option is most appropriate...

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Authors (2)

Avinash Navlani

Avinash Navlani, PhD in Data Science, is a senior data scientist, researcher, and educator with 14 years of experience in data science, including 9 years in industry, 4 years in academia, and 1 year in research. He has developed machine learning models, optimization solutions, NLP systems, scalable data pipelines, and cloud-based MLOps platforms across healthcare, retail, finance, oil & gas, and manufacturing. His expertise includes Python, PySpark, Airflow, Databricks, Azure ML, MLflow, and Data Engineering. A former lecturer and speaker, he is passionate about applying analytics to solve real-world problems.

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Cornellius Yudha Wijaya

Cornellius Yudha Wijaya has over eight years of experience in data science, machine learning, and artificial intelligence. He currently works as a data scientist manager, where he leads AI initiatives, manages team members, and helps drive the development of practical data and AI solutions. Over the course of his career, he has worked across data science, AI product development, and technical education, with experience in building machine learning systems, supporting business decision-making, and making advanced analytics more usable in real-world settings. He has also written extensively on data science, Python, machine learning, and generative AI, with a strong focus on practical learning and applied problem-solving.

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