Python Data Analysis, Fourth Edition

This is the code repository for Python Data Analysis, Fourth Edition, published by Packt.

Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering

Avinash Navlani, Cornellius Yudha Wijaya

                       

About the book

Modern data analysis goes beyond cleaning and visualizing data. Today's practitioners need to build scalable data pipelines, apply machine learning, work with text and image data, and understand emerging AI techniques such as Generative AI and Large Language Models (LLMs). This guide shows you how to tackle these challenges using Python's modern data ecosystem. Unlike books focused on a single library or technique, this book provides an end-to-end approach to Python data analysis. You'll learn how to move from data preparation and exploratory analysis to machine learning, NLP, image analytics, scalable processing, and AI-powered workflows. Starting with statistical foundations, you'll learn how to clean, transform, wrangle, and visualize data. You'll then explore time series analysis, signal processing, forecasting, and predictive analytics before applying machine learning techniques such as regression, classification, clustering, PCA, probabilistic methods, and Bayesian approaches. The book also covers graph analytics, sentiment analysis, NLP, image analytics, Generative AI, and LLMs. Finally, you'll learn to scale analytics workflows using Dask, Modin, Ray, and PySpark. By the end of the book, you'll be able to build end-to-end data analysis pipelines and apply modern data science and AI techniques to solve real-world challenges.

Key Learnings

• Prepare, clean, and transform data for exploratory data analysis and data wrangling
• Analyze and visualize data using Python and pandas
• Perform time series analysis, forecasting, and signal processing
• Apply machine learning with Python using scikit-learn techniques
• Use regression, classification, clustering, PCA, and Bayesian methods
• Perform sentiment analysis, NLP, graph analytics, and image analytics
• Accelerate workflows using Dask, Modin, and Ray
• Build scalable big data analytics pipelines with PySpark

Chapters

Chapters	Colab	Kaggle	Gradient	Studio Lab
Chapter 1: Getting Started with Python Libraries
Chapter 2: NumPy and Pandas
2_NumPy_and_pandas.ipynb
Chapter 3: Statistics for Data Insights
3_Statistics.ipynb
Chapter 4: Linear Algebra
4_Linear Algebra.ipynb
Chapter 5: Data Visualization
5_Data_Visualization.ipynb
Chapter 6: Retrieving, Processing, and Storing Data
6_Retrieving_Processing_and_Storing_Data.ipynb
Chapter 7: Cleaning Messy Data
7_cleaning_messy_data.ipynb
Chapter 8: Time-Series Analysis
8_time_series_analysis.ipynb
Chapter 9: Supervised Learning: Regression and Classification
9_supervised_learning_regression_classification.ipynb
Chapter 10: Unsupervised Learning: Dimensionality Reduction, Clustering, Anomaly Detection
10_unsupervised_learning.ipynb
Chapter 11: Ensemble Methods: Bagging and Boosting Methods
11_ensemble_methods.ipynb
Chapter 12: Artificial Neural Networks and Deep Learning
12_Artificial_Neural_Network_and_Deep Learning.ipynb
Chapter 13: Analyzing Text Data
13_analyze_text_data.ipynb
Chapter 14: Analyzing Image Data
14_analyze_image_data.ipynb
Chapter 15: LLMs and Gen AI
15_llm_and_generative_ai.ipynb
Chapter 16: Parallel Computing Using Dask, Modin, and Ray
16_parallel_computing.ipynb
Chapter 17: Big Data Analytics using PySpark
17_Big_Data_Analytics_using_PySpark.ipynb

Requirements for this book

Each chapter in this book includes practical examples, and some use different Python libraries depending on the topic. For this reason, it is recommended to maintain a dedicated Python environment for the book whenever possible.

Basic familiarity with Python will be helpful, and some prior exposure to data analysis or machine learning concepts may make the later chapters easier to follow. However, the book is structured progressively, so readers can build their understanding step by step through the examples and explanations.

For a smoother learning experience, tools such as Jupyter Notebook, JupyterLab, or VS Code are recommended for running the code and experimenting with the examples. Some of the more advanced chapters, particularly those involving deep learning, large language models, parallel computing, or big data tools, may also benefit from stronger hardware or cloud-based environments.

To get the most out of the book, readers are encouraged to work through the examples actively, test variations, and explore how the techniques can be applied to their own datasets and problems.

Get to know Authors

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.

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.

Other Related Books

• Python Data Cleaning Cookbook, Second Edition
• Bayesian Analysis with Python, Third Edition