Top Python Coding Best Practices for Beginners

In March 2022, Python made headlines in the programming world by becoming the most popular programming language. After years of hovering in the top ranks, it finally surpassed Java and C to reach the number one spot on the TIOBE index.

And why not? After all, it is such a fun and powerful language to work with. It is fast, user-friendly, open-source, and has a community comprising millions of programmers across the globe.

However, no matter how approachable a language is, it is of little worth if a programmer cannot leverage its plus points effectively. And this is especially true in the case of beginner Python programmers who are more often than not confused about what to do and where to head.

In this article, we have put together ten essential Python best practices that are not just beginners, but also experienced programmers can refer to upskill themselves.

Top 10 Python Coding Best Practices for Beginners to Hone Their Skills

Here we list ten of the most commonplace yet necessary Python coding best practices you should know.

1. Improve Code Readability With Naming Conventions

When starting as a Python programmer, or for that matter, a programmer in any language, code readability is something that one should always make sure of. Code readability ensures that a piece of code is not just easy to examine but also states its purpose clearly.

One surefire way to exercise code readability is to follow a language’s naming conventions, and it is no different for Python.

Here we have some fundamental naming conventions for Python that every Python developer should use to write readable code.

  1. Variable names should delineate their purpose and contain only alphabets and numbers, no special characters. In the case of long variable names, you should use underscores. For example, instead of writing temperatureincelsius, you should write temp_in_celsius.

    Also, since variable names are case sensitive, you should not interchange, for instance, th with Th. And do not use Python keywords such as del, class, for, etc., as variables.

  1. When designating names to classes, you should use CamelCase. Also, do not use underscores in class names.

  1. For methods, one should go for lowercase and use underscores for demarcation words in long method names.

  1. Python function names should be in lower case. Use underscores to separate words.

  1. Python file names, packages, object names, and module names should be concise, in lowercase, and use underscores to separate words if required.

  1. Python constants should always be capitalized.

  1. To declare variables as private or protected, add a double underscore or single underscore respectively to their names. For example, _day is a protected variable and __day is a private one.

2. Use Idiomatic Python to Write Code the Python Way

Idiomatic Python technically refers to Python code following specific programming idioms for faster execution and high readability. However, over the years, due to the obsession of Python programmers with code readability (which is good), one more factor – aesthetics – has been included in the definition. Hence, Idiomatic Python or Pythonic code is a Python code that is robust, readable, precise, and of course, beautiful to look at.

Pythonic code is an essential concept for programmers who have shifted to Python from other programming languages ​​but have not let go of their programming idiosyncrasies.

For instance, look at the following piece of Python code:

import random
n = ['cool','hate','love','song','tea']; m = random.choice(n); print (m)

This code has been written to output any random word from an array of five words stored in the variable “n.” Although it does generate the desired result, it looks complex and unsightly.

If we instead write it like this:

import random

n = ['cool','hate','love','song','tea']

m = random.choice(n)

print (m)

It looks neat and conveys its purpose well.

3. Give Importance to Proper Documentation and Commenting

Commenting and documentation are two other aspects of Python coding that beginners should learn and master. Why? Because while these two terms have little to do with the actual coding process, they relay a lot of information about the purpose and logic of a code to the readers.

Commenting:

In Python, comments or code comments are fundamental descriptions of a piece of code (a line or a block). These descriptions come in handy when a user, maintainer, developer, or even the future you are examining the code.

Like many other programming languages, Python comments begin with a # symbol. PEP8, the officially recognized document for Python guidelines, states that comments should not be more than 72 characters long. Thus, if your program requires you to write lengthy comments, use multiple lines to write the comment.

The following example showcases how Python comments work:

# A program to convert temperature values from Fahrenheit to Celsius

fah = float(input("Temperature in Degree Fahrenheit: "))

cel = ((fah-32)*5)/9

print ('Temperature in Degree Celsius: %f' %(cel))

Documentation:

According to Python’s creator Guido van Rossum, code is more often read than written. Hence, it becomes crucial for programmers to maintain the legibility of their code for any potential reader or user. And this is where the importance of documentation comes in.

Documentation refers to precisely and clearly stating the purpose, the specifications, the technical requirements, and any ancillary information of the codebase of your Python project.

In Python, Documentation duties are assigned to docstrings or documentation strings. Docstrings make it easy to relate methods, functions, classes, etc., with their respective documentation.

The following examples explain the functioning of docstrings in Python:

def sleep(name):
    """ An understandable description of the function goes here """
    print(f"Goodnight {name}, I am going to sleep")
    
sleep("Jenny")

As you can see here, docstrings use triple “double” quotes to encapsulate the relevant documentation.

To retrieve a particular code module’s, class’s, function’s, or keyword’s documentation, Python has the built-in help() and __doc__ attributes. The following examples show these attributes in action.

def bye(person):
    """ The Bye function sees off a person """
    print("See You Tomorrow, " + person + "!")
    
person = input("Enter your name: ")

bye(person)
print(bye.__doc__)

In this piece of code, upon execution, the last statement output, “The Bye function sees off a person.”

PS Similar to the comments, docstrings also abide by specific documentation conventions stated in PEP257.

def bye(person):

    print("See You Tomorrow, " + person + "!")
    
person = input("Enter your name: ")

bye(person)
print(bye.__doc__) */

4. Employ Virtual Environments

Virtual environments are fundamentally isolated versions of Python, complete with the python interpreter, scripts, and libraries.

Python programmers use virtual environments when they want to work with different versions of Python or on projects with varying dependencies. For instance, when using a third-party Python library, there might be cases when a new update alters the original library functions, rendering it unusable for a project. In such scenarios, firing up a virtual environment and verifying the functionalities of the library beforehand can help tremendously.

Virtual environments are a Python coding aspect that all beginners should familiarize themselves with.

5. Working on Projects That Simulate Real-World Scenarios

When it comes to learning to program in general and not just any specific language, coding projects go a long way in teaching the essentials to total amateurs.

They not only implore learners to employ the skills they have learned so far but offer them a platform to exercise their creativity.

However, choosing any random practice project to work on can prove detrimental to the learning process in the long run. Picking irrelevant projects, and that too in an unorganized manner, can demoralize a learner (if a project is way beyond their current level) or bring them to a dead-end (if they opt for stale projects).

Hence, beginners should only go for coding projects that gradually test their upskilling efforts and provide worthwhile real-world scenarios.

6. Using Enumerate() Over Range()

Another aspect of Python programming that makes a code more Pythonic is its aesthetic appeal. Veteran Python programmers, when writing code, make sure that their code not only reads easily but is also beautiful to look at.

Following this narrative, we bring the enumerate() vs range() dilemma.

The range() Python function comes in handy when users want to loop over integer or string lists. Look at the following example to understand.

for n in range(50):
    k = n+1
    print (k)

#output will be a vertical list of numbers from 1 to 50

However, what would you do if you wanted to check the number of times your function has looped over the iterator or wanted to add an automated index to the output?

You can do something like the following code:

import string

word_list = ["eat", "sleep", "read", "repeat"]
for i in range(len(word_list)):

    words = word_list[i]
    print ("I want to ")
    print(f"{i}: {words}")

The above piece of code works as intended. However, if you show this to any experienced python programmer, they will say it is not pythonic.

Now let us use the enumerate() function to see how it fares in comparison.

import string

word_list = ["eat", "sleep", "read", "repeat"]

for i, words in enumerate(word_list):

    print ("I want to ")
    print(f"{i}: {words}")

Well, what do we see here? The number of lines has decreased while the output is the same as the previous code. Also, if you observe, the code looks much more appealing, clear, and explains its purpose sans any documentation.

Hence, you should use enumerate() if you want to beautify your iteration code while keeping its functionality intact. And also, if you’re going to add an index but do not want to write code separately for it.

7. Hesitate to Create Too Many Global Variables

Global variables are the accidental villains that get more flak than credit. And understandably so, since global variables can be called anywhere in the program. Thus, it is very much possible that these variables can alter the working of many functions by being called unknowingly, introduce hard to trace bugs and, in some extreme cases, lead to spaghetti code.

Thus, programmers should make it a point to reduce the number of global variables and keep a constant check on them so as not to misuse their values.

8. Leverage Modular Code

Python is a highly modular programming language, which means one can divide a Python codebase into several modules of Python code, each containing functions, variables, methods, classes, etc. These modules can then be interlinked to construct a program.

The concept of code modularity in Python enables programmers to simplify the complexity of a large codebase by reducing it into modules. These modules can then be used in conjunction or independently.

Modular coding also increases code reusability as programmers can import a relevant module instead of writing a function from scratch.

Python also sports a repository housing many modules and libraries containing Pythonic code that programmers can use to ease their coding burdens.

import math

print ("Enter a number: ")
x = input()
y  = math.sqrt(int(x))
z  = math.factorial(int(x))
print ("The Square Root of the above number is: ", y)
print (z)

9. Few Return Variables? Return Multiple Values ​​at Once

If your program has a limited number of return values, for instance, three or four, we suggest you try returning multiple values ​​at once. The following program shows how to do so:

import math

def maths():

    x  = math.sqrt(int(25))
    y  = math.factorial(int(5))
    return x,y;
    
x, y = maths()
print (x,y)

However, if the number of return values ​​exceeds four, you should resort to dictionaries or data classes.

10. Deal With Broken Code ASAP!

Broken code, if neglected, can turn from a minor issue to a complex, hard-to-trace, and difficult problem to fix. Hence, programmers should make it a point to regularly check their code for any inconsistencies and debug it for optimal performance.

The best way to deal with broken code is to develop a code checkup routine. Since code can break not just from human errors but also from alterations due to changes in dependencies or environments, programmers must keep a keen eye on their codebases. And when they do detect a patch of incorrect code, they should not defer the remedy until a later date.

We know all this sounds too obvious, but sometimes, even the basic requirements can get neglected and augment into irreparable issues.

Conclusion

Every programming language has its set of peculiarities that its programmers need to get along with. And Python is by no means different. However, certain aspects of Python coding, like writing beautiful code, following a preset code layout, etc., can overwhelm even the most stoic individuals.

The above list of Python best practices covers most if not all the requisites of ideal Python coding. All that is left now is for you to examine and adapt them into your programming routine and grow to be a great Python programmer (which we are sure you will do).

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