Python is a resourceful programming language that brings innovations to the world of artificial intelligence and data science. Therefore, it is essential to learn how does Python ensure clean code.
In this article, we will learn about what clean code is and how to code in Python, that too by maintaining transparency.
So, let’s get a wing to it all.
What is clean code?
As C++ inventor, Bjarne Stroustrup says:
“I like my code to be elegant and efficient. The logic should be straightforward to make it hard for bugs to hide, the dependencies minimal to ease maintenance, error handling complete according to an articulated strategy, and performance close to optimal so as not to tempt people to make the code messy with unprincipled optimizations. Clean code does one thing well.”
Hence, for clean coding, Python developers make sure to focus on these factors:
- Easy to read – The author of object-oriented Analysis and design with applications, Grady Booch makes it clear that clean code in Python is being read like prose.
- Focused codes for Python developers – Each function, module, or class is well-written and totally explained.
- Easy to maintain – It means the Python developers can flexibly read or enhance the lines and promote clean code in Python.
- Easy to debug – No more fussy indulgence is required in order to find errors in the codesets.
- It is highly performant – What does Python code look like? Simple and facile, it increases the performance of your built systems.
How to write code in Python? Patterns for clean code in Python
If you are curious to know the answers to questions like:
- How do you ensure clean code?
- How do you maintain code quality in Python?
Here’s the clue!
We have suggested certain patterns to incorporate transparency which will enhance the usage of clean code in Python. For instance, some of them are:
What does Python code look like when it comes to visual representation? Gingerly, it depends on how you include naming conventions in the codesets.
Some relevant tips are discussed below:
1. Use easy-to-read long descriptive names
How to write code in Python? Interestingly, the long descriptive names can help you embed clean coding in Python. One good way is shown below:
|# Not recommended# The au variable is the number of active usersau = 105|
# Recommended total_active_users = 105
2. Put Descriptive intention revealing names
The codes for Python developers should be completely comprehensive and easy to reason about. Therefore, it is imperative for them to figure out which variable stores from what name, etc.
|# Not recommendedc = [“UK”, “USA”, “UAE”]|
for x in c:print(x)
# Recommendedcities = [“UK”, “USA”, “UAE”] for city in cities: print(city)
3. Dodge the usage of ambiguous shorthand
Instead of using short perplexing titles or names in the code set, to clean up code and to make it readable for everyone, it is advised to use long descriptive names. The ambiguity should be cast aside in the first place.
|# Not recommendedfn = ‘John’Ln = ‘Doe’cre_tmstp = 1621535852|
# Recommendedfirst_name = ‘John’Las_name = ‘Doe’creation_timestamp = 1621535852
4. Consistent use of the same vocabulary
How to write a code in Python language? Developers at vteams suggest not to use inconsistent naming conventions, that can further overdo your codesets, making them complex and undefined in the later sets of codes.
To enhance the probability of clean code in Python, just be sure about maintaining the appropriate naming conventions.
Note: this rule applies to naming variables, functions, files, and structures.
5. Track codebase issues in the editor
How to code in Python? Allow the Python developer to track bugs and issues in the codesets efficiently (on their own).
Thereby, what else can they do to encourage incorporating clean code in Python? Here are some interesting practices:
- Resolve issues related to technical debt continuously.
- They can write the context for each codebase diligently by removing issues.
- With the Python developer’s help, the context switching is reduced.
- They will provide full visibility on technical debt.
6. Avoid using magic numbers
Let’s take a pledge not to use magic numbers ever in the codesets. Well, for clean coding in Python, it is important to know what the magic number is.
It is a number with special hardcoded semantics that you can encounter in the code but is undefined. If you must know these numbers are actually used as literals in more than one location in the code.
How to code in Python? Well, to understand it completely, you need to get your hands on these amazing functional patterns:
7. Do one thing, and that too, properly
It is the rule of thumb that for clean coding, Python developers have to jot down simple and short functions in order to perform a single task. Let’s suppose your function name contains the “and” word, then you have to split the names into two function names.
8. Consistent function naming convention
As it is said above, to clean up code, Python experts would have to be consistent with the naming conventions. Undoubtedly, different naming conventions can confuse them for sure.
9. Stop using flags or boolean flags
Remember that boolean flags are in fact variables that deal with boolean values like true or false. Alongside this, these flags are first passed on to a function and then, are used by the functions in order to determine or analyze its performance.
What does Python code look like? If you ask us, it all depends on the classes we utilize in the development cycle. So, for clean code in Python, we need to learn one thing and that is
10. No more addition of redundant context
It is due to the ambiguous action that could be performed. For instance, when Python developers work with classes, they add inappropriate or unwanted variables to variable names which in turn can create bugs in the software. So, avoid adding redundant contexts.
No doubt, the transparent development cycle is always admired and gets all the hype. Thus, when it comes to describing how to code in Python, promoting clean code in Python is a must.
This is why these patterns are elucidated above to ensure the perfection and accurate functionality of the codesets.