Partial book profile
The Quick Python Book, Third Edition
A concise, example-driven guide that takes experienced programmers from Python basics through advanced features and real-world data-handling applications as fast as possible.
The full book profile is not available yet.
What it’s about
The Quick Python Book, Third Edition, is the fastest credible path to Python 3 proficiency for programmers who already know how to code. Author Naomi Ceder—chair of the Python Software Foundation and a Python practitioner for over 16 years—strips away the beginner hand-holding and moves directly through the language's essentials: dynamic typing, built-in data structures, control flow, functions, modules, OOP, regular expressions, packages, and the standard library. The second half of the book pivots to practical data work, covering file wrangling, ETL pipelines, network data retrieval, relational and NoSQL databases, and exploratory analysis with pandas and Jupyter. Each topic is illustrated with interactive shell examples and short, purposeful listings rather than toy projects, so readers build real intuitions quickly. The book closes with a full case study that fetches, cleans, stores, and graphs weather data, tying every major concept together. Whether you are migrating from Python 2, switching from another language, or returning to the language after a gap, this is the book that gets you writing idiomatic, Pythonic code in the shortest possible time.
The through-line
- Who it’s for
- An experienced programmer—coming from Java, C, Perl, or another language, or returning to Python after years away—who wants to become productively proficient in Python 3 as quickly and directly as possible without wading through material aimed at absolute beginners.
- The problem
- The reader needs to learn Python 3 fast, but most resources are either too basic (aimed at first-time coders) or too reference-like (exhaustive but not progressive), leaving experienced programmers without a focused, efficient learning path. They feel frustrated and slightly impatient—they already know how to program and don't want to be talked down to, but they also don't want to miss Python-specific idioms that make them look like a novice.
- The plan
- Start with a high-level overview of Python's strengths, weaknesses, and philosophy (Part 1) to calibrate expectations and provide a conceptual map.
- Work through the essentials sequentially: built-in types, control flow, functions, modules, filesystem and file I/O, and exception handling (Part 2).
- Deepen knowledge with advanced features: OOP, regular expressions, type system internals, packages, and the standard library (Part 3).
- Apply everything to practical data problems: file management, ETL, network data, databases, and exploratory analysis with pandas and Jupyter (Part 4).
- Consolidate with a real end-to-end case study that fetches weather data, cleans it, stores it in a database, and graphs it.
- The payoff
- The reader writes idiomatic, Pythonic code confidently—using list comprehensions, context managers, generators, and appropriate data structures—rather than translating patterns from their previous language. · They can build, test, and distribute standalone Python scripts and multi-module packages. · They can read, clean, transform, and store data from a variety of sources (flat files, APIs, databases) using both the standard library and third-party tools like pandas.
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Related profiles we’ve built
Additional reading
- Official Python Documentation · Python Software Foundation
The book repeatedly refers to the official documentation as the authoritative and comprehensive source for information on the standard library, language syntax, and built-in functions.
- PEP 8 -- Style Guide for Python Code · Guido van Rossum, Barry Warsaw, Nick Coghlan
Referenced in Appendix A and throughout the book as the definitive guide to Pythonic coding style, promoting readability and consistency across projects.
- The Zen of Python (PEP 20) · Tim Peters
Included in Appendix A as a summary of the core philosophical principles behind Python's design, which helps explain why the language works the way it does.
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