Greetings! I‘m Mike, a AI and data architect with over 15 years of experience coding complex systems in Python. Join me on journey of understanding the Zen mindset required for Python mastery.
A Brief History of My Python Coding Journey
I first learned Python as a computer science student in the early 2000‘s. I was initially drawn to its elegance and simplicity compared to C and Java. But it took years in the trenches of professional software development before the deeper wisdom of "The Zen of Python" sank in…
Over the past decade, I‘ve refactored millions of lines of Python code across various codebases and teams. Often dealing with technical debt, complexity, and stability issues stemming from a lack of Pythonic thinking.
I aim to use my battle-tested experience to help you avoid common pitfalls I‘ve observed and internalize the Pythonic philosophy faster.
Origins of The Zen of Python
The principles behind excellent Python code were first penned in the "Zen of Python" poem by Tim Peters in 1990. But the concepts reflect guidance by Python‘s creator Guido van Rossum going back as early as 1991.
Known simply as the "GNOME manifesto", it first appeared when posted to the comp.lang.python Usenet group and quickly became a guiding light for the community. Let‘s analyze each aphorism to see how it leads towards code enlightenment…
Beautiful is Better than Ugly
We software engineers tend to indulge our problem solving abilities, often overlooking aesthetics of our code. But beauty should be an important consideration according to the Zen.
Some examples of "ugly" Python code that should be avoided:
- Deeply nested conditional logic and loops
- Functions with high cyclomatic complexity
- Classes with poor cohesion doing too many unrelated things
- Hacking built-in types instead of defining classes
- Using a library without understanding it under the hood
Over years, I‘ve come to agree much ugly code starts out as prototype code without proper encapsulation and abstraction. Python allows us to iterate quickly, but that is no excuse for not refactoring towards more beautiful designs later!
Some signs of beautiful Python code include:
- Simple, linear control flow and flat structure
- Small focused modules with narrowly defined responsibilities
- Short functions and classes focused on doing one thing well
- Using Python standard libraries where appropriate
- Picking the right data structures for the job
Beauty should be our North star even if it means a bit more up front work. Clean, pragmatic designs stand the test of time and stress much better. Prioritize beauty, and functionality will follow!
Explicit is Better than Implicit
One of Python‘s greatest strengths lies in it being an extremely explicit language without too much "magic" happening under the covers. This explicitness aids readability and limits ambiguity greatly. Some examples of explicitness including:
- Lacking variable type declarations
- Requiring indentation for blocks
- Errors on undefined variables
- Selected use of dunder methods instead of operator overloading
The Zen remind us it is better for code behavior to not depend too heavily on side effects or context. Make return values, types, call semantics and other signatures explicit.
For example, I once helped migrate a large Python 2 codebase to Python 3. Much behavior depended on whether builtin types returned lists or iterators when calling various methods. Such implicitness led to problems.
I have also debugged scientific computing code that abused operator overloading heavily creating matrices you could multiply with either * or @. While cute initially, the cleverness caused real confusion longer term.
Simple is Better Than Complex
This feels almost tautological – of course we strive for simplicity, right? But I‘m continually amazed at how quickly complexity creeps into large Python program.
Some indicators your software is getting overly complex:
- Models with too many related classes and indirection
- Code cannot fit on printed sheet or screen without scrolling
- Developers cannot fully explain system architecture
- Adding a feature breaks unrelated parts of system
- Fixing bugs creates more bugs
When we view the system only through narrow perspectives, we miss forest for the trees losing structural integrity.
Simplicity requires continuously zooming out to understand the big picture and fundamental essence:
- Refactor and prune unnecessary code
- Break apart dense classes lacking cohesion
- Clean up bad abstractions hiding simple concepts
- Understand real business needs before coding models
Follow the Unix philosophy. Have components that do one thing well and use pipes to connect.
Complex is Better Than Complicated
This one sounds contradictory on surface. Isn‘t our goal simplicity? Yes absolutely… up to a point.
In practice, even simple systems must manage some innate complexity. For example, cleanly integrating multiple data sources and APIs requires a certain complexity threshold. However there is complexity due to essence vs that due to poor implementations.
Essential complexity is inherent to problem domain and can appear as:
- Advanced statistical methods
- Intelligent caching schemes
- Background worker processes
- Distribution across networks
Accidental complexity comes from implementations, not problem space:
- Unnecessary coupling between components
- Bloated classes doing too many things
- Duplicate logic copied across modules
- Excessive subclasses and inheritance
Understand the problem space first, then solve with well-abstracted tools before piling on custom code. Master essential complexity, while eliminating accidental.
Flat is Better Than Nested
Python pioneered a highly legible style with uniform indentation to denote blocks, scoping, etc. Deeply nested flows are challenging for humans to parse.
I love exploring nested list, dict, and generator comprehensions in Python. However when overused without restraint they quickly increase cognitive load.
[[x for x in a if x > 1] for a in input_lists if len(a) > 5]Typically I restrict comprehensions to a single level. Beyond that, breaking into for loops is better for readability and debugability.
In large Python systems, I also recommend limiting function nesting depth. Functions over 3-4 levels deep signals violation of single responsibility principle – split into multiple modules or classes instead!
Readability Counts
Nothing is real in coding until expressed clearly enough for another practitioner to understand. And Python was designed first and foremost for extreme readability from the start.
Some indicators of highly readable code:
- Excellent docstrings documenting behavior
- Comments explaining why non-obvious logic is needed
- Using descriptive variable/function names
- Small code size fitting on one screenful
- Simple linear control flow
- Preferring English keywords over punctuation
Readability directly correlates with ability to maintain and extend code longer term.
I once did analysis using software quality tools on two Python codebases – one commercial closed-source and one open source project. The proprietary system scored extremely poor on readability metrics like function size, complexity, formatting inconsistencies, etc. The open source project fared over 3X better and had faster contributor velocity.
Write code first for other humans – computers will cope either way!
There Should Be One Obvious Way To Do It
The Zen advice sounds reasonable initially. However Python offers incredible flexibility with often many approaches to a problem that are equally valid depending on angles like readability, speed, memory usage, idiomatic style etc.
So this should not be interpreted as dogma, rather a guiding star to avoid needless wondering – if libraries provide an obvious path forward leverage conventions. Expressiveness and freedom are integral to Python too!
The tension reminds of early debates in Python community around one style of formatting and indentation (tabs vs spaces). After much discussion, the preference was made explicit in PEP 8 Style Guide. But allowances made for overriding when reasonable.
Different goals may justify alternative implementations. Optimize for clarity first with comments explaining why deviations needed. Establish as few arbitrary restrictions as possible offering sane defaults.
Conclusion
The Zen of Python has served us well guiding journey towards mastery. May it steer your journey onwards towards more beautiful, simple, expressive and Pythonic code bases ahead! Now go forth and explore its wisdoms – coding perfection awaits tireless seekers…
Explore the full Python Enlightenment Guidebook with exercises at www.python-zen.io.
