Python Programming for Data Analysis

This textbook is a comprehensive guide to Python programming for data analysis, covering key language concepts, low-level language features, package management, object-oriented programming, third-party modules, and visualizations. It is ideal for readers with some Python programming experience and is designed to help them get the most out of Python's vast ecosystem.

Format: Paperback / softback
Length: 263 pages
Publication date: 06 May 2022
Publisher: Springer Nature Switzerland AG

This comprehensive textbook has been meticulously crafted as a valuable resource for individuals seeking to excel in the field of programming for data analysis. It originated from meticulously prepared notes used by the author while teaching the ECE143 Programming for Data Analysis course at the University of California, San Diego. This textbook serves as an essential guide for both graduate and undergraduate students pursuing degrees in Machine Learning and Data Science, as it encompasses the fundamental concepts and techniques required for effective programming in Python.

The book begins by delving into the core language features essential for programming proficiency, particularly in data analysis applications. It provides detailed explanations of key concepts such as variable assignment, data types, loops, functions, and object-oriented programming. The author emphasizes the importance of understanding these foundational elements to write efficient and scalable code.

Furthermore, the textbook explores low-level language features, including Python memory management and data structures. It offers in-depth discussions of these topics, enabling readers to gain a deep understanding of how Python handles memory allocation and organizes data efficiently. This knowledge is crucial for developing robust and reliable data analysis programs.

To maximize the benefits of Python, the book emphasizes the importance of leveraging its vast ecosystem. It discusses Python package management, showcasing various tools and techniques for installing, managing, and utilizing third-party modules. This empowers readers to tap into a wide range of libraries and tools, enhancing the capabilities and functionality of their programs.

The section on object-oriented programming delves into the language's features that facilitate common programming patterns. It explains concepts such as classes, inheritance, and polymorphism, enabling readers to develop modular and reusable code. This approach not only enhances code maintainability but also promotes code reusability, contributing to the development of efficient and scalable data analysis solutions.

After developing the key Python language features, the book proceeds to explore third-party modules that are fundamental for effective data analysis. It begins with a comprehensive discussion of Numpy, a powerful library for numerical computations and data manipulation. The author provides detailed explanations of Numpy's array data structures, memory usage, and various mathematical operations. This foundational knowledge enables readers to perform complex data analysis tasks with ease.

The book then moves on to Pandas, a widely used data manipulation and analysis library. It discusses the library's extensive features for data processing, alignment, and visualization. The author highlights the importance of strong visualizations in communicating data analysis findings, and thus, the book extensively develops key modules such as Matplotlib, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly. These tools provide readers with powerful tools for creating informative and visually appealing data visualizations.

Throughout the text, the author shares numerous tricks-of-the-trade and best practices that help readers avoid common pitfalls and enhance their programming skills. These insights are invaluable for newcomers to both Python and programming for data analysis, as they provide valuable guidance for developing robust and reliable programs.

In addition to its technical content, the textbook offers a comprehensive introduction to the Python mindset. The author aims to empower readers to think like Python programmers and make informed design choices in their codes. This approach promotes a deeper understanding of the language and its underlying principles, enabling readers to write code that is not only efficient but also expressive and maintainable.

By presenting a well-structured and comprehensive approach, this textbook serves as an invaluable resource for individuals seeking to excel in programming for data analysis. Whether you are a novice programmer or an experienced professional, this book will provide you with the knowledge and skills necessary to develop effective and efficient data analysis programs in Python.

Weight: 427g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030689544
Edition number: 1st ed. 2021