History of Python
Late 1980s: Guido van Rossum, working at the National Research Institute for Mathematics and Computer Science in the Netherlands (CWI), starts designing Python as a hobby project. He’s aiming to address shortcomings he sees in ABC, another language he had helped create.
December 1989: Van Rossum begins the actual implementation. He takes inspiration from ABC, the language SETL, and Modula-3, among others.
February 1991: The first public release (version 0.9.0) is made on the alt.sources newsgroup, marking Python’s public birth.
Early 1990s: Python steadily grows in popularity, with a small but dedicated community forming. Key developments include:
Lambdas, map, filter, and reduce are added (1994).
List comprehensions are introduced (1994).
The __future__ module is created to manage transitions between versions (1998).
2000: Version 2.0 arrives with major features:
Full garbage collector (cycle detection).
Unicode support, making internationalization much easier.
2008: Python 3.0 is released, a significant overhaul addressing core design issues but not fully backward compatible. This leads to a period of coexistence between Python 2 and 3.
2010s: Both 2.x and 3.x branches continue development. Python becomes a go-to language for data science, machine learning, web development, and more. Libraries like NumPy, Pandas, Django, and Flask contribute to its immense popularity.
January 1, 2020: Official support for Python 2.7 ends, encouraging the transition to Python 3.
Present day: Python is one of the most beloved programming languages worldwide, consistently ranking among the top 3 in usage surveys. It’s known for its readability, versatility, and vast ecosystem of libraries.
Core Pythonic Principles
While there’s no absolute «Pythonic» standard, these guidelines are widely embraced:
Readability counts:
Use clear variable names and add comments where necessary.
Follow consistent indentation (PEP 8 recommends 4 spaces per level).
Break up complex code into smaller, well-defined functions.
Simple is better than complex:
Strive for straightforward solutions. Avoid over-engineering when possible.
Use Python’s built-in features effectively (e.g., list comprehensions, iterators).
Explicit is better than implicit:
Avoid hiding functionality behind magic or assumptions.
Be clear about what your code does and how it does it.
Flat is better than nested:
Prefer shallow nesting of code blocks (if statements, loops) when reasonable.
Excessive nesting can hinder readability.
Sparse is better than dense:
Break down code into meaningful chunks with whitespace.
Avoid overly long lines or cramming too much into single statements.
Errors should never pass silently:
Handle exceptions gracefully to prevent unexpected crashes.
Provide informative error messages when things go wrong.
There should be one– and preferably only one –obvious way to do it:
Python favors having a single, clear approach to common tasks.
This reduces confusion and makes code more maintainable.
Practicality beats purity:
While rules are important, sometimes breaking them makes sense for practical reasons.
Know when to be pragmatic, but avoid sacrificing clarity unnecessarily.
Additional Tips:
Learn the standard library: It’s packed with useful modules and functions, saving you time and effort.
The Need: In the late 1980s, Guido van Rossum was working at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. He observed a need for a programming language that was both powerful and easy to read and learn.
Influences: Van Rossum drew inspiration from languages like ABC (intended as a less technical option for non-programmers), SETL (influenced exception handling), Modula-3 (clean syntax), and others.
Birth and Evolution: The groundwork for Python began in December 1989. Its first public release (version 0.9.0) came in 1991, demonstrating its emphasis on readability and relatively uncluttered syntax. As Python matured, it garnered a passionate community of developers who championed its elegance and practicality.
Pythonic Principles
Python’s design emphasizes core philosophies that encourage clean, logical, and expressive code. Here’s a breakdown of some key Pythonic tenets:
Readability Matters: «Beautiful is better than ugly.» Python code should strive to be easily understood by humans. This is achieved through:
Meaningful variable and function names
The use of whitespace for indentation (rather than curly braces)
Clear formatting
Simple is Better Than Complex: «Explicit is better than implicit.» Strive for straightforward solutions when possible. Python provides elegant ways to express logic without excess baggage.
Practicality Wins: «There should be one—and preferably only one—obvious way to do it.» Although there may be multiple ways to tackle something, a more «Pythonic» approach often emerges as a clear and accepted best practice.
Batteries Included: «The Zen of Python» hints at this with «practicality beats purity.» Its extensive standard library gives you an amazing set of tools out-of-the-box, streamlining many common tasks.
Putting Pythonic Principles into Practice
Descriptive Naming: Use meaningful names for variables, functions, and classes (e.g., calculate_interest instead of calc_int).
Whitespace: Python’s mandatory indentation promotes organized code. Follow established conventions such as using four spaces per indentation level.
List Comprehensions: Use the elegance of list comprehensions to perform manipulations concisely (e.g., squares = [x*x for x in range(10)] ).
Iterators and Generators: Embrace lazy evaluation with iterators for efficiency. When applicable, write concise generators and generator expressions to manage resources more effectively.
The Zen of Python: To get a fuller picture of the Pythonic mindset, type import this in your Python interpreter and ponder the wisdom found there.
Let’s illustrate with a Simple Example
Non-Pythonic way:
numbers = [1, 5, 3, 8]
r = []
for x in numbers:
if x % 2 == 0:
r.append(x)
print(r)
More Pythonic way:
numbers = [1, 5, 3, 8]
even_numbers = [x for x in numbers if x % 2 == 0]
print(even_numbers)
History of Python: A Journey from ABC to Global Powerhouse
Python’s story begins in the late 1980s with Guido van Rossum, a Dutch programmer working at Centrum Wiskunde & Informatica (CWI) in the Netherlands. Dissatisfied with the limitations of the ABC programming language, he embarked on creating a successor: Python.
Inspiration and Influences:
ABC: Python shares its clear syntax and emphasis on readability with ABC.
SETL: This language influenced Python’s powerful list manipulations and data structures.
Amoeba: As Python aimed to become the scripting language for the Amoeba operating system, it needed robust exception handling and system interaction capabilities.
Key Milestones:
1989: Development starts during Christmas holidays.
1991: Python 0.9.0 is released, marking the language’s public debut.
2000: Python 2.0 arrives with significant features like garbage collection and Unicode support.
2008: Python 3.0 introduces major changes, including improved Unicode handling and removal of backward compatibility issues.
Present Day: Python remains actively developed, boasting widespread adoption across various domains.
Core Pythonic Principles: Embracing Readability and Elegance
While syntax plays a part, Python’s philosophy goes beyond mere words. Here are some key principles to cultivate:
Readability: Code should be clear and self-explanatory, minimizing the need for excessive comments.
Clarity over Cleverness: Prioritize straightforward solutions over overly complex, obfuscated code.
Minimalism: Do more with less. Use built-in functionalities and libraries effectively to avoid reinventing the wheel.
Explicit over Implicit: Be clear about your intentions. Use explicit constructs whenever possible to enhance code understanding.
Generality: Write code that is flexible and adapts to different use cases.
Community: Learn from and contribute to the vibrant Python community to stay updated and share your knowledge.
Implementing Pythonic Principles:
Here are some practical tips to incorporate these principles in your code:
Use meaningful variable and function names.
Adhere to PEP 8 style guidelines for consistent formatting.
Utilize clear indentation to denote code blocks.
Employ list comprehensions and generator expressions for concise data manipulation.
Favor built-in libraries and modules over custom solutions when appropriate.
Write modular and reusable functions.
Leave helpful comments where necessary, but strive for self-explanatory code.
Remember, mastering Pythonic principles is an ongoing journey. Experiment, explore, and learn from the community to enhance your understanding and craft elegant, readable code.
I hope this explanation aids your academic learning journey!