You might have heard the word pythonic when people talk or write about Python code and idiomatic Python expressions. The phrase pythonic essentially means the same thing, that you are writing Python code in the way, that the language was intended to be written. It is a way to write clean, easy-to-understand, and efficient code. Utilizing the pythonic coding practices in your code would greatly enhance the productivity of the program and improve your skills. So, in this article, we will cover the details of pythonic styles, including their importance and how to use them in our code, as well as some built-in functions to ensure performance.
Writing Readable and Maintainable Code
1. Importance of Readability in Python
Throughout the development life, code readability and its maintenance are significant. Developers should keep some points clear while coding and leverage the practices for writing efficient and understandable code. The reasons for ensuring readability in Python code are as follows:
- The code should be easier to understand by all, including the developer and tester team. It must have clearly explained the intent of your code. Efficient readable code will reduce the time required to comprehend the logic and purpose of your code.
- When the code is clear and well organized, it is easier to maintain. This well-structured code will reduce the need to promptly fix bugs and make the code error-prone. Therefore, readable code should be easier to maintain and well-structured.
- Dealing with larger-scale projects requires team collaboration. At that time, the intended code should be readable enough to be understandable by all team members. This will lead to a smoother development process and effective team collaboration.
2. Using Meaningful Variable Names
To write readable and maintainable code, use meaningful variable names that clearly describe the purpose. Avoid single-letter variable names, which is the traditional method, and use proper descriptive names with CamelCase to increase code productivity.
#avoid using single letter variable names
a = 100
b = 200
#use descriptive name that defines the variable data
employee_Name = "John Stephen"
employee_Salary = 20000
2. Writing Clear and Concise Comments
Writing clear and concise comments within the code is the best practice for increasing code readability. The comments on each function and variable will clarify the purpose, behaviors, and logic behind the code block. These comments act as notes and explanations that will help future developers with the code logic.
length = 10 #the length of rectangle
width = 20 #the width of the rectangle
#calculating area of rectangle
Area = length * width
3. Following PEP 8 Guidelines
PEP 8 stands for Python Enhancement Proposal. This is the style guide for writing Python code. This guide includes all Python conventions and practices required to write efficient and understandable code. By following the PEP 8 guidelines, you will be able to write maintainable code. In addition, this guide proposes new features based on requirement enhancement. It covers all aspects of Python code such as design, style, and naming convention for the characters per line while adhering to standards including indentation, maximum line Length, blank lines, import libraries, and comments along with additional considerations of error handling and code structure.
Embracing Python's Built-in Data Structures and Idioms
Python provides many built-in data structures that can be used in Pythonic coding. Utilizing Python built-in functions and libraries instead of writing a traditional code style will increase code reusability and productivity.
By following the PEP 8 style guidelines, you can maintain consistency in your codebase.
1. Utilizing List Comprehensions
List comprehension is the Pythonic way of creating a list easily. It consists of a for clause with the condition for the second list and an optional if-clause. This way, you can concisely generate a list by increasing the readability.
even_numbers = [2,4,6,8,10] #list of even number
square = [num ** 2 for num in even_numbers] #list comprehension in single line
print(square)
Output
2. Utilizing Dictionary Comprehensions
Dictionaries are widely used data structures in Python. Similar to list comprehension, we can also define a dictionary easily using the key-pair value. Here, dictionary comprehension is used to generate a dictionary that uses a list or iterable object and applies the expression to create the values for the key-pair in the list.
#dictionary containing author name with their ID as values
author_Name = ['Alice', 'john', 'Stephen'] #list of author names
author_ID = {name: f'A{i+1}' for i, name in enumerate(author_Name)} #assign the id using index number and iterate over author name
print(author_ID) #print ID
Output
3. Utilizing Sets Effectively
Python provides built-in set operations that allow us to perform intersection, union, and difference, which are common math set operations. These operations ensure the calculation of the set.
set_A = {1,2,3,4,5,6,7,8} #values of set a
set_B = {3,5,7,9} #values of set b
union = set_A |set_B #perform union operation
intersection = set_A & set_B #perform intersection operation
difference = set_A - set_B #perform different operation
symetric_diff = set_A ^ set_B #perform symmetric operation
print("Union:", union)
print("Intersection:",intersection)
print("Difference:", difference)
print("Symmetric Difference is:", symetric_diff)
Output
4. Utilizing Generator Expressions
Utilize generator expressions when dealing with larger datasets because lists and dictionaries will load the data in the memory all at once, which is not memory efficient. Therefore, the generator expression is more memory efficient and will load the data when needed.
even_numbers = [2,4,6,8,10] #list of even number
square = (num ** 2 for num in even_numbers) #generator expression in single line
for numbers in square:
print(square)
Output
Remember at terminal, the output shows the show the address space of value within the memory instead of the actual value
5. Enumerate and Zip Functions
In Pythonic coding, the zip() function combines multiple iterable objects and returns the tuple. Whereas, the enumerate() function takes the iterable object and returns the key-pair values as an output similar to a dictionary.
author_Name = ['Alice', 'john', 'Stephen'] #list of author names
book_Name = ['Alchemist', 'Sapiens', 'Nothing'] #list of book names
for author, book in zip(author_Name, book_Name): # iterating using zip function
print(author, ":", book)
Output
Leveraging Python's Language Features
1. Understanding and utilizing Python's dynamic typing
Python is a dynamic-type programming language, irrespective of other programming languages, where the data types for each variable need to be defined. In Python, the variables defined without datatype are deterministic, and the compiler will interprets and assigns the type at run time based on the value of a variable. Therefore, using the dynamic behavior of Python will make your program flexible and concise because the single function can be used for different types of values, for example, int for adding two values and string for joining two strings.
2. Utilizing Context Manager with the "with" Statement
While dealing with files or databases, closing the file or connection after task completion is necessary to manage the system resources properly. Now, python has provided us with an object for managing the system resources effectively i.e. context manager. The context manager is similar to a resource manager who helps you write the setup and teardown actions around the block of code. The most common case of a context manager is opening and closing a file using the with statement.
It consists of two parts: the entry and exit parts. The entry part consists of the expression that we need in the context. The exit part contains the expression when the working in context is complete. In our example, we are opening and closing the file, so there is no need to close the file explicitly. This will be done by with statement on their own.
file_path = "Context.txt"
with open(file_path, 'r') as file:
file_content = file.read() #this will read the file and save the file into file_content object
print("The Content of file is", file_content)
Output
3. Utilizing Decorators for Code Reuse and Readability
Decorators are the key features of Python metaprogramming. It is a powerful feature that allows the coder to modify the existing code without changing the source code. It allows you to add new functionality by extending the existing function. Decorators are typically performed on functions and its syntax uses the '@' symbol with the decorator function name before its code. This approach promotes the reusability and readability of the code by just calling the decorator in the other parts of the part when needed.
def my_Decorator(function): #function for converting the string into upercase
def wrapper(): #function within a function
func = function() #here, we call the decorator function inside this function
upper_Case = func.upper() #converting the string
return upper_Case
return wrapper
@my_Decorator
def get_String(): #getting the string for converting in upper case
return "Whereof one cannot speak, thereof one must be silent"
print(get_String()) #printing the string
Output
Remember that Pythonic code is not just about syntax but also about writing code that is easy to understand for others.
4. Leveraging Generator Functions and Iterators for Memory Efficiency
Mostly, the generator function is used for iterating against the iterable objects of Python i.e. lists, dictionaries, tuples, etc. Using the simple for loop has memory constraints because it will load the whole dataset at once in the memory. This approach is also not memory efficient in the case of large datasets. Therefore, leveraging the generator function will make the code efficient and load the data only when required.
Pythonic Control Flow and Exception Handling
1. Using Pythonic loops
Pythonic loops i.e. for loop and while loop, are used with the iterable objects of Python to iterate over the sequence. Pythonic for loop extracts the element from the iterable object and assigns the value to the new variable. All of this is done in a single expression followed by a for loop containing condition.
odd_Numbers = [1,3,5,7,9] #list of odd numers
square = [num ** 2 for num in odd_Numbers] #for loop with list comprehension
Whereas the syntax of Pythonic while loop is similar to a traditional one. The only difference is that we use descriptive names for the variables. This encourages a clean and concise coding style that is easy to read and understand by adhering to the PEP 8 guidelines.
counter = 0 #used descriptive name of counter instead of i
while counter > 5:
print(counter)
counter = counter + 1
2. Leveraging Tuple Unpacking for Elegant Iteration
With a Pythonic style, we can iterate over the tuples elegantly within the loop. It allows us to unpack the values of tuples and assign them to another variable using the Pythonic for loop. This way, you can easily simplify the code and encourage the readability of the program
book_Details = [('Alice', 'Alchemist', '2018'), ('john', 'Sapiens', '1987')] #list of book names
for author_Name, book_Name, issue_Year in book_Details: # iterating using zip function
print("Name:", author_Name)
print("Book: ", book_Name)
print("Issue Year:", issue_Year)
Output
3. Writing Concise and Clear Conditional Statement
Writing concise and clear conditional statements means adhering to the Python idiomatic style guidelines. The format of traditional conditional statements or Pythonic is similar, but with more readability emphases in Pythonic ones because the descriptive variable names and indentation are properly used.
#take age from user
enter_Age = input("Enter age:" )
age = int(enter_Age) #convert age string to int
if age >= 18: #conditio for checking vote eligibility
print("You can cast the vote, eligible")
else:
print("You cannot cast the vote, uneligible")
Output
4. Pythonic Exception Handling with try-except block
You can also handle the exceptions in your code gracefully before their occurrence. Leveraging Pythonic exception handling includes the concise way of using try and except block where except block defines the exception name with the variable e for better understanding and readability.
def addition(num1, num2): #addition function
try:
num3 = num1 + num2 #this line will raise exception
return num3
except TypeError as e:
print("An Exception occur") #here the exception is handled
print(type(e)) #prints the type of error with name
return None
result = addition(8, 0)
print("Addition of two numbers:" , result)
Output
Q: What does it mean to write Pythonic code?
A: Pythonic code refers to the code that follows the python's style conventions and takes advantage of its features to write clean and readable idiomatic code.
Q: What is PEP 8?
A: This is a document that provides guidelines and best practices on how to write Python code effectively.
Q: Why is writing pythonic code important?
A: It is important because it encourages code reusability, readability, and maintainability and helps you to write Python features effectively.
Q: How can I improve my pythonic skills?
A: As a beginner, you can enhance your skill by studying the Python style guide and trying to implement it in your code.
Wrapping Up
In the end, we discussed the ways of adhering to the Pythonic best practices that would make your code clean, efficient, and more maintainable. These pythonic style guidelines will not only make the program functional but improve its robustness as well. When you are learning Python language, then embracing the practices of PEP 8 will elevate your skill and help you to create better Python applications. I hope this guide is helpful for you. If you have any questions or concerns, then feel free to ask and provide feedback. Thank you for following the guide. Happy Coding!
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