1 About Python
This chapter introduces Python and sets expectations for the book: a fast, practical path to writing and reading real Python without getting bogged down in esoterica. It targets readers with general programming experience—especially those coming from other languages—and maps out a progression from environment setup and a concise syntax tour to core data types, functions, classes, and packages, with selective advanced topics and a data-focused case study. It also acknowledges the growing role of AI coding assistants, using end-of-chapter problems (starting later in the book) to compare human and AI approaches and to build skill in guiding AI tools effectively.
The case for Python centers on its broad applicability, approachable design, and strong ecosystem. Python is cross-platform, free, and open source; it’s widely used from quick scripts and prototypes to substantial systems, with particular strength in scientific computing, data science, machine learning, and AI-related workflows. Its ease comes from simple syntax, high-level abstractions, and a robust standard library (“batteries included”), which enable rapid development and compact, readable code. Readability is reinforced by required indentation, expressiveness reduces boilerplate, and the language’s ecosystem offers abundant third-party libraries and frameworks, along with variants that target additional runtimes and devices.
The chapter also outlines trade-offs and active improvements. Python’s execution speed can lag compiled languages, but performance is often adequate in practice, with hotspots offloaded to C/C++ and ongoing interpreter optimizations reducing overhead. Dynamic typing means type issues aren’t caught at compile time, but optional type hints and tooling help enforce consistency where needed. Mobile app development remains less mature than on native stacks, though it is improving, and multi-core utilization is constrained by the GIL, with continuing work to better exploit parallelism. The overall message: understand these limits, but recognize that for many projects Python’s development speed, clarity, and ecosystem outweigh the drawbacks.
Summary
- Python is a modern, high-level language with dynamic typing and simple, consistent syntax and semantics.
- Python is multiplatform, highly modular, and suited for both rapid development and large-scale programming.
- It’s reasonably fast and can be easily extended with C or C++ modules for higher speeds.
- Python has built-in advanced features such as persistent object storage, advanced hash tables, expandable class syntax, and universal comparison functions.
- Python includes a wide range of libraries such as numeric processing, image manipulation, user interfaces, and web scripting.
- It’s supported by a dynamic Python community.
FAQ
Who is this book for, and what prior knowledge do I need?
The book targets programmers who want a fast, practical grasp of Python—especially those coming from other languages or looking to level up for data science, machine learning, and scientific work. No prior Python is required, but general programming experience is expected.How is the book organized, and what will I learn first?
It starts with an introduction to Python and guidance on setting up your environment, gives a quick syntax summary, then progresses from built-in data types to functions, classes, packages, some advanced features, and a data-handling case study.How does the book address AI/LLM tools for coding?
While it’s not an AI tutorial, starting with chapter 5 the exercises include example AI-generated answers with brief analyses of what the AI got right and wrong, helping you learn to steer AI tools to produce working code.Why should I choose Python?
Python is cross-platform, scales from quick scripts to large systems, ships with strong libraries (including GUI and web), is central to scientific computing and data/ML/AI work, has a huge user base, and is free.What makes Python easy to use and productive?
Types are tied to objects (not variables), high-level abstractions and a rich standard library reduce boilerplate, and the syntax is simple. For many applications, Python can cut development time and lines of code dramatically compared to C or Java.What does it mean that Python is “expressive”?
Python accomplishes more per line, which speeds development and simplifies maintenance. Common tasks—like swapping two variables—can be done in a single clear statement rather than multiple lines.Why is Python considered readable?
Python enforces indentation for code blocks, encouraging consistent, clean formatting. Readable code is easier to understand, debug, maintain, and modify—benefits you feel immediately on real projects.What does “batteries included” mean in Python?
Python’s standard library is extensive—email, web, databases, OS calls, GUI, and more—so you can do real work without extra installs. For example, serving files over HTTP can be done with just a couple of lines using the built-in modules.How cross-platform and open is Python?
Python runs on Windows, macOS, Linux/UNIX, and more; the same code generally runs anywhere there’s a Python interpreter. Variants like Jython, IronPython, MicroPython, and CircuitPython broaden platform reach. It’s free and open source, widely supported by industry and a strong community you can also contribute to.What are Python’s main areas for improvement?
- Performance: Interpreted bytecode can be slower than C, though core Python keeps getting faster and you can offload hotspots to C/C++.- Types: Variables aren’t statically typed, but optional type hints and tools help catch issues before runtime.
- Mobile: Building and distributing commercial mobile apps in Python is possible but still cumbersome, though improving.
- Multicore: The GIL limits true parallelism in CPython; ongoing work aims to improve multi-core utilization.
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