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: Hardback
Length: 263 pages
Publication date: 05 May 2021
Publisher: Springer Nature Switzerland AG

This comprehensive textbook has been meticulously crafted as a valuable resource for individuals seeking to delve into the realm of programming for data analysis. It originated from meticulously prepared notes meticulously crafted for the ECE143 Programming for Data Analysis course, which is a fundamental requirement for both graduate and undergraduate degrees in Machine Learning and Data Science at the University of California, San Diego. This book is particularly suited for readers who possess a foundational understanding of Python programming. It delves into essential language concepts that are crucial for effective programming, particularly in the context of data analysis applications.

In-depth discussions are conducted on various low-level language features, with a special emphasis on Python memory management and data structures. The book emphasizes the importance of leveraging Python's vast ecosystem to maximize its potential. It explores Python package management, showcases the usage of third-party modules, and provides guidance on structuring your own Python modules.

The section on object-oriented programming elucidates the language's features that facilitate common programming patterns. After laying the foundational groundwork, the book proceeds to delve into third-party modules that are indispensable for efficient data analysis. It begins with a comprehensive exploration of Numpy, covering key concepts and discussing internal array data structures and memory usage. The author then moves on to Pandas, highlighting its extensive functionalities for data processing and alignment. Recognizing the significance of strong visualizations in data analysis, the book delves into the development of key modules such as Matplotlib, along with web-based options like Bokeh, Holoviews, Altair, and Plotly. Throughout the text, numerous practical tips and tricks are shared, aimed at helping readers avoid common pitfalls and enhance their coding proficiency.

Furthermore, the author takes the reader on a journey into the internal logic of the Python language, empowering them to adopt a Python mindset and make informed design choices in their codes. This approach is particularly beneficial for newcomers to both Python and programming in general, aiding in their smooth integration into the field.

In summary, this textbook serves as an indispensable guide for anyone aspiring to excel in programming for data analysis. It provides a comprehensive and in-depth exploration of Python programming, covering key language concepts, low-level language features, third-party modules, object-oriented programming, visualizations, and practical tips. Whether you are a student, professional, or enthusiast, this textbook will empower you to unlock the full potential of Python and apply it to solve complex data analysis challenges.

Weight: 582g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030689513
Edition number: 1st ed. 2021