Python for Programmers

Python for Programmers: From Fundamentals to Data-Driven Code 🐍

Python has become the working language of data science, automation, and modern software development. Python for Programmers by Paul Deitel and Harvey Deitel is built for readers who already understand programming concepts and want to move into Python without wading through beginner-level explanations. Published by Pearson in 2019 as part of the Deitel Developer Series, the book moves from core syntax to practical, library-driven work.

The Deitel approach has always been hands-on: short explanations, complete examples, and a steady build toward real programs. This volume applies that method to Python with a clear eye on where the language is most useful today—data, cloud, and intelligent applications.

What the Book Explores

• Python foundations: variables, arithmetic, control statements, functions, and object-oriented basics.
• Core data structures: lists, tuples, dictionaries, sets, comprehensions, and sequence processing.
• Array-oriented programming: NumPy arrays, attributes, and vectorized operations.
• Interactive workflows: IPython and Jupyter Notebook test-drives.
• Data science context: descriptive statistics, measures of central tendency and dispersion, simulation, and static/dynamic visualizations.
• Big-picture topics: cloud computing, Internet of Things, big data analytics, and AI at the intersection of computer science and data science.

Built for Readers Who Already Code

Because the book assumes programming experience, it can focus on Python’s idioms, libraries, and data-science connections instead of spending chapters on what a loop is. That makes it a strong fit for developers switching languages, computer science students with prior coursework, and professionals who need Python for analytics, automation, or further study in AI and big data.

Why It Stands Out 💡

Deitel titles are known for clear explanations, worked examples, and a steady progression from concepts to runnable code. This volume extends that tradition into Python’s data-centric ecosystem, showing how the language connects to NumPy, Jupyter, visualization, and the broader data science stack. If you want a structured, example-rich path into Python for professional work, this is a thoughtful addition to your digital shelf.

Available as a digital edition from Digital Delights.