Best Practices in Python and why Python is so popular
Python is a versatile language that has attracted a broad base of people in recent times. Python has become one of the most popular programming languages. The popularity of Python grew exponentially during the last decade. According to an estimate, the previous five years saw more Python developers than the conventional Java/C++ programmers. Now the question is why is Python so popular? The primary reasons for this are its simplicity, speed, and performance.
Why does Python have an edge over the other programming languages? Let’s find out!
- Everything is an object in Python
- Support for Object-Oriented Programming – including multiple inheritances, instance methods, and class methods
- Attribute access customization
- List, dictionary, and set comprehensions
- Generators expressions and generator functions (lazy iteration)
- Standard library support of queues, fixed precisions decimals, rational numbers.
- Wide-ranging standard library including OS access, Internet access, cryptography, and much more.
- Strict nested scoping rules
- Support for modules and packages
- Python is used in the data science field
- Python is used in machine learning and deep learning
- Parallel Programming
As a Python developer, you must know some basic techniques and practices which could help you by providing a free-flowing work environment. Some of the best practices in Python are listed below.
Create Readable Documentation
In python, the best practice is readable documentation. You may find it a little burdensome, but it creates a clean code. For this purpose, you can use Markdown, reStructuredText, Sphinx, or docstrings. reStructuredText and Markdown are markup languages with plain text formatting syntax to make it easy to markup text and convert it into a format like HTML or PDF. Sphinx is a tool to create intelligent and beautiful documentation easily, while reStructuredText lets you create in-line documentation. It also enables you to export documentation in formats like HTML.
Follow Style Guidelines
Python follows a system of community-generated proposals known as Python Enhancement Proposals(abbreviated as PEPs) which attempt to provide the basic set of guidelines and standards for a wide variety of topics for proper Python Development. One of the most widely referenced PEPs ever created is PEP8, which is also termed as the “Python community Bible” for properly styling your code.
Immediately Correct your Code
When creating a python application, it is almost always more beneficial in the long-term to acknowledge quickly and repair broken code. (Join the Xaltius Academy to learn how!)
Give Preferences to PyPI over manual Coding
The above will help in obtaining a clean and elegant code. However, one of the best tools to improve your use of Python is the huge module repository namely The Python Package Index (short for PyPI). Not considering the level and experience of the Python Developer, this repository will be very beneficial for you. Most projects will initially begin by utilizing existing projects on PyPI. The PyPI has over 10,000 projects at the time of writing. There’s undoubtedly some code that will fulfill your project needs.
Watch out for Exceptions
The developer should watch out for exceptions. They creep in from anywhere and are difficult to debug.
Example: One of the most annoying is the KeyError exception. To handle this, a programmer must first check whether or not a key exists in the dictionary.
Write Modular and non-repetitive Code
A class/function should be defined if some operation is required to be performed multiple times. This will shorten your code, also increasing code readability and reducing debugging time.
Use the right data structures
The benefits of different data structures are very well known. This will result in higher working speed, storage space reduction, and higher code efficiency.
These are the good practices in Python that every Python developer must follow for a smooth experience in Python. Python is a growing language and its increased use in the field of Data Analytics and Machine Learning has proved to be very useful for the developers. Python for AI has also gained popularity in recent years. In the upcoming years, Python shall have a very bright future, and the programmers who are proficient in Python will have an advantage.