Python was created by Guido van Rossum in the late 1980s and has grown to become one of the most widely used high-level programming languages today. Unlike Perl, which came before it, Python was designed from the ground up to emphasize code readability, simplicity, and explicitness. The motivation behind Python’s design philosophy is summarized in “The Zen of Python” (PEP 20), a collection of 19 aphorisms written by Tim Peters that influences Python’s design, which includes:
- Beautiful is better than ugly
- Readability counts
- Simple is better than complex
Adhering to these principles means Python code aims to be logical, clean, easy to read and understand, even if that means some trade-offs in program performance. This emphasis on readability makes Python a great language for beginners, yet powerful and versatile enough for large-scale software development.
This article will provide Python code examples to demonstrate how these Zen principles translate into writing Pythonic code. We will also reference the Python Enhancement Proposals (PEPs) that have helped shape Python over the years into the versatile, beginner-friendly, and readable language that it is today.
Table of Contents
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Principles of Readability
Readability is at the heart of Python’s design philosophy. As stated in PEP 20, readability counts above everything else in Python code. Some key ways Python code implements readability include:
1. Using Meaningful Names
Python emphasizes using meaningful variable and function names that are self-explanatory:
# Good
student_grade = 9.5
# Avoid
x = 9.5
Long names are preferred over short abbreviations for improved clarity:
# Clearer meaning
student_grade_percentage = 90
# Less clear
st_gr_pct = 90
2. Following Style Conventions
Python has a well-defined style guide called PEP 8 that covers conventions for indentation, whitespace, commenting, naming, etc. This is the official style guide for Python. Adhering to these standards makes Python code more uniformly readable. While PEP 8 recommends 4 spaces for indentation, other styles like 2 spaces or tabs are also commonly used.
# Use 4 spaces for indentation
def calculate_average(grades):
sum = 0
for grade in grades:
sum += grade
return sum / len(grades)
# Spacing after commas, around operators
ages = [23, 18, 21, 78, 56]
average = sum(ages) / len(ages)
3. Using Intuitive Data Structures
Python comes with high-level data structures like lists, dictionaries, and sets that have clear semantics making code more self-explanatory:
# List of students' grades
grades = [9.5, 8, 7.5, 6, 9]
# Dictionary of student names and grades
student_grades = {"Mary": 9.1, "John": 8.5, "Juan": 7.2}
4. Handling Exceptions Gracefully
Python makes it easy to handle errors cleanly with try/except blocks for more readable control flow:
try:
grade_percentage = grade / total_possible
except ZeroDivisionError:
print("Error: Cannot divide by zero")
Simplicity and Minimalism
Python aims for simplicity, avoiding complex syntax and constructs in favor of cleaner, more minimalistic code.
1. Simple and Uncluttered Syntax
Python has a simple and uncluttered syntax with easy-to-read code layouts, using whitespace instead of brackets for blocks:
# Simple if statement
if grade > 90:
print("A")
2. One Obvious Way to Do It
As stated in The Zen of Python, there should ideally be one obvious way to accomplish a task in Python. This avoids confusion and promotes uniformity:
# Obvious way to iterate
for number in [1, 2, 3]:
print(number)
3. Avoiding Complex Language Constructs
Python avoids complex functional or logical notations like currying, pointer arithmetic, etc, in favor of simpler constructs:
# Simple function definition
def increment(x):
return x + 1
# No pointer arithmetic
x = [1, 2, 3]
x[0] = 5
Focus on Practicality
Python is designed to favor practicality over purity and performance. This means preferring “good enough” simple solutions that work for most programs over complex optimizations.
1. Using Built-in Types
Python comes batteries included with robust built-in types like lists, dicts, and strings instead of needing separate libraries or optimizations:
# Convenient built-in
names = ["John", "Juan", "Mary"]
# No need to import special String library
greeting = "Hello World!"
2. Iterators and Generators Over Optimized Loops
Python emphasizes simple iteration using generators and iterators rather than complex, fine-tuned looping constructs:
# More readable than traditional for loop
for name in names:
print("Hello " + name)
3. Flat is Better Than Nested
Deeply nested code is avoided in favor of flatter, more horizontal code where possible:
# Flat structure preferred
def print_student_info(name, grades):
print(name)
print_grades(grades)
def print_grades(grades):
print(grades)
Concise Expressiveness
Python aims to provide high expressiveness in surprisingly few lines of code.
1. List Comprehensions Over Verbose Loops
List comprehensions provide a concise syntax for iteration and filters. They are also generally faster than equivalent for loops:
# Concise list comprehension
even_nums = [x for x in range(20) if x % 2 == 0]
2. Using Containers to Simplify Logic
Python collections like dictionaries, sets, and lists reduce manual logic:
# Concise membership test
if grade in grade_reqs:
print("Passed")
3. Assignment Tricks
Augmented assignment operators like += and |= minimize lines of code:
# Concise summation
total = 0
for x in range(10):
total += x
Pythonic Coding Style
Writing idiomatic, “Pythonic” code that embraces the language’s philosophy and recommended style is key. Here are some examples of Pythonic techniques:
1. Embracing Descriptive Variable Names
# Pythonic
student_grade_percentage = 85
# Unpythonic
percentage = 85
2. Using context managers when possible
# Pythonic
with open("file.txt") as file:
data = file.read()
# Unpythonic
file = open("file.txt")
data = file.read()
file.close()
3. Preferring simple iterations over index manipulation
# Pythonic
for student in students:
print(student)
# Unpythonic
for i in range(len(students)):
print(students[i])
By following Python’s design philosophy and style conventions, we can write code that is easy to understand, debug, and maintain. While there are some trade-offs with performance and flexibility, Python favors working software that embraces simplicity, readability, and practicality.
Conclusion
Python’s design philosophy outlined in “The Zen of Python” (PEP 20) has a major influence on Python’s readability, simplicity, and pragmatic approach. Key principles like favoring plain English naming, flat code structures, simple syntax, and high-level data types result in highly readable and Pythonic code. Adhering to these principles and idioms improves collaboration among developers and maintainability of codebases. By learning and embracing the Zen of Python, developers can write code that is clear, idiomatic, and maintainable for themselves and others. This focus on readability and simplicity has made Python one of the most popular and widely used programming languages today.