Bibliography and Reference Materials

Python for Business for Beginners: Coding for Every Person

All books listed here are real, published works. Each entry follows the format: Author(s). Title. Publisher, Year.

Python Fundamentals

Matthes, Eric. Python Crash Course: A Hands-On, Project-Based Introduction to Programming. 3rd ed. No Starch Press, 2023.

The most widely recommended introduction to Python for absolute beginners. Covers the core language through three substantial projects. Excellent for readers who want to reinforce the concepts from this book's early chapters with a different perspective and additional exercises.

Lutz, Mark. Learning Python. 5th ed. O'Reilly Media, 2013.

A comprehensive reference covering Python in extensive detail. At over 1,500 pages, this is not a book you read cover to cover — it is a reference you return to when you need the full treatment of a specific topic. Covers Python 3 thoroughly. Best used as a complement to more project-oriented resources.

Beazley, David, and Brian K. Jones. Python Cookbook: Recipes for Mastering Python 3. 3rd ed. O'Reilly Media, 2013.

A collection of practical code recipes organized by category: data structures, strings, algorithms, file and I/O, networking, and more. Each recipe is explained with context and alternatives. Suitable for practitioners who have a foundation and want to expand their repertoire with professional-quality patterns.

Ramalho, Luciano. Fluent Python: Clear, Concise, and Effective Programming. 2nd ed. O'Reilly Media, 2022.

An advanced treatment of Python's deeper features: data model, sequences, iterables, generators, decorators, concurrency. Not appropriate as a first book, but essential for practitioners who want to understand why Python works the way it does and write code that is truly idiomatic. Referenced in this book's discussions of Pythonic style.

Data Analysis with Python

McKinney, Wes. Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter. 3rd ed. O'Reilly Media, 2022.

Written by the creator of pandas, this is the definitive reference for pandas-based data analysis. Covers Series, DataFrame, time series, groupby, data loading and cleaning, and visualization in greater depth than any other single resource. The third edition is updated for pandas 2.0. Directly relevant to Chapters 10-16 and 25-27 of this textbook.

VanderPlas, Jake. Python Data Science Handbook: Essential Tools for Working with Data. 2nd ed. O'Reilly Media, 2022.

Covers NumPy, pandas, Matplotlib, and scikit-learn with a focus on data science workflows. Available free online at jakevdp.github.io/PythonDataScienceHandbook. Complements this textbook's data analysis chapters with more depth on NumPy and a different approach to pandas workflows.

Molin, Stefanie. Hands-On Data Analysis with Pandas: A Python Data Analysis Library to Efficiently Perform Data Collection, Wrangling, Analysis, and Visualization. 2nd ed. Packt, 2021.

A project-focused introduction to pandas with strong emphasis on business data scenarios. Good supplementary reading for readers who want more worked examples alongside Chapters 10-16.

Automation

Sweigart, Al. Automate the Boring Stuff with Python: Practical Programming for Total Beginners. 2nd ed. No Starch Press, 2019.

Available free online at automatetheboringstuff.com. The essential reference for task automation: Excel manipulation, PDF reading, web scraping, scheduling, email automation, and working with files. Directly relevant to Chapters 17-22 of this textbook. Highly recommended for any reader whose primary goal is business automation.

Sweigart, Al. Beyond the Basic Stuff with Python: Best Practices for Writing Clean Code. No Starch Press, 2020.

The follow-up to Automate the Boring Stuff, covering code quality, testing, debugging, version control, and object-oriented programming. Bridges the gap between writing working code and writing professional code.

Business Analytics

Bowen, Jesse, and Matt Bogard. Python for Finance: Mastering Data-Driven Finance. 2nd ed. O'Reilly Media, 2018. (Authored by Yves Hilpisch)

Note: the standard reference is Hilpisch, Yves. Python for Finance: Mastering Data-Driven Finance. 2nd ed. O'Reilly Media, 2018. The foundational text for quantitative finance with Python. Covers financial data structures, time series analysis, derivatives pricing, and portfolio optimization. Directly relevant to this book's financial analysis chapters.

Gries, Campbell, et al. Practical Programming: An Introduction to Computer Science Using Python 3.6. 3rd ed. Pragmatic Bookshelf, 2017.

A computer science–oriented introduction that covers programming fundamentals with business and scientific examples. Useful background reading for practitioners who want a more rigorous foundational understanding.

Machine Learning

Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3rd ed. O'Reilly Media, 2022.

The standard introduction to machine learning with Python. Covers classical ML with scikit-learn in Part I (directly relevant to this textbook) before advancing to deep learning with TensorFlow in Part II. Well written, extensively practical, and updated for current versions. The primary recommended resource for readers pursuing the machine learning path described in Chapter 40.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: with Applications in Python. 2nd ed. Springer, 2023.

The accessible version of Elements of Statistical Learning. Covers regression, classification, resampling methods, regularization, tree methods, clustering, and more with strong conceptual grounding. Available free at statlearning.com. The Python edition was updated in 2023 to include Python code examples throughout.

Burkov, Andriy. The Hundred-Page Machine Learning Book. Independently published, 2019.

A concise treatment of machine learning fundamentals. Available partially free at themlbook.com. Useful as a conceptual overview before diving into full textbooks.

Software Engineering Practices

Hunt, Andrew, and David Thomas. The Pragmatic Programmer: Your Journey to Mastery. 20th Anniversary Edition. Addison-Wesley Professional, 2019.

The canonical text on software craftsmanship, updated for its 20th anniversary. Language-agnostic principles that apply directly to business Python work: DRY code, broken windows, test-driven development, documentation, personal responsibility for quality. Required reading for any serious practitioner.

Martin, Robert C. Clean Code: A Handbook of Agile Software Craftsmanship. Prentice Hall, 2008.

While its examples are in Java, the principles apply universally: meaningful names, small functions, clear comments, test coverage, and the discipline of continuous improvement. Widely cited in discussions of code quality. The principles inform the code conventions of this textbook.

Percival, Harry J. W., and Bob Gregory. Architecture Patterns with Python: Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices. O'Reilly Media, 2020.

Available free at cosmicpython.com. Covers how to structure Python applications for testability and maintainability. Most relevant for practitioners building complex business applications. Assumes a working knowledge of Python.

Data Engineering

Reis, Joe, and Matt Housley. Fundamentals of Data Engineering: Plan and Build Robust Data Systems. O'Reilly Media, 2022.

The definitive introduction to data engineering as a discipline. Covers the data engineering lifecycle — ingestion, transformation, serving — and the tools involved. Not Python-specific, which is appropriate: data engineering involves many tools and the concepts matter more than the specific implementations. Required reading for readers pursuing the data engineering path.

Kleppmann, Martin. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O'Reilly Media, 2017.

The most thorough treatment of how modern data systems work: relational databases, NoSQL stores, distributed systems, stream processing. Not a beginner text, but the most valuable technical reading investment for practitioners building toward data engineering or serious application development. Read it slowly.

Statistics and Quantitative Methods

Wills, Shane. Statistics for Business and Economics. Pearson, (various editions).

Background reading for the statistical concepts introduced in Chapters 25-28 of this textbook. Any edition from the last ten years provides sufficient coverage.

Bruce, Peter, Andrew Bruce, and Peter Gedeck. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. 2nd ed. O'Reilly Media, 2020.

Covers the statistical concepts most relevant to data science and business analytics: sampling, distributions, statistical experiments, regression, and classification. Python code examples throughout. Directly relevant to this textbook's statistics chapters.

Visualization

Knaflic, Cole Nussbaumer. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, 2015.

The essential guide to presenting data to business audiences. Covers chart selection, annotation, color, and the narrative structure of effective data presentations. Language-agnostic — the principles apply regardless of which Python library you use. Directly informs the visualization chapters of this textbook.

Wilke, Claus O. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly Media, 2019.

Available free at clauswilke.com/dataviz. A comprehensive, language-agnostic treatment of what makes visualizations effective, accurate, and aesthetically sound. Covers color theory, chart types, common mistakes, and design principles for scientific and business visualizations.

Online Resources

Python Software Foundation — Official Documentation docs.python.org/3 The authoritative reference for everything in the Python standard library. The tutorial at docs.python.org/3/tutorial is the official introduction to the language.

pandas Documentation pandas.pydata.org/docs The official documentation for pandas, including the User Guide (conceptual explanations), API Reference (function signatures and parameters), and a collection of worked examples.

Real Python realpython.com A comprehensive collection of Python tutorials, articles, and learning paths with strong practical orientation. Referenced throughout this textbook for supplementary reading.

Stack Overflow — Python tag stackoverflow.com/questions/tagged/python The primary Q&A resource for specific Python programming questions. When you encounter an error message or specific challenge, searching Stack Overflow should be your second step (after reading the error message carefully).

PyCon US Talk Archives pyvideo.org and the PyCon YouTube channel Video recordings of talks from every PyCon conference since 2011. Covers practical Python applications in every domain: data analysis, automation, web development, testing, and business applications.

Kaggle kaggle.com A platform hosting machine learning competitions, datasets, and community notebooks. The datasets section provides thousands of real datasets for practice. The notebooks section shows how experienced practitioners approach similar problems.

The Python Package Index (PyPI) pypi.org The official repository for Python packages. When you encounter a library name in this book or elsewhere, pypi.org provides installation instructions, version history, and links to the project's documentation and source code.

Note: Publication dates and edition numbers are accurate as of the writing of this textbook. Where new editions have been published since the textbook's writing, the most recent edition should be preferred. All books listed are real, published works from established publishers.