Python is renowned for its simple and understandable syntax. And oneliners are the icing that elevates Python into a more appealing and well-liked programming language. The oneliners are among the many aspects of the Python language that programmers most cherish. Python is already one of the simpler computer languages to write code in compared to other languages, and utilizing oneliners only makes it more relaxed and accessible. Let's examine these Python one-liners now.

What are Python one-liners?

Python one-liners are short lines of code that carry out a single action. One line of code is all it takes to complete a job, and they frequently use Python's built-in methods, functions, and language features. Oneliners are preferred because they are brief and easy to understand, making them helpful when a simple response is sought.

Python one-liners can be used in various settings, including scripts, command-line tools, and as a component of more extensive programs. They are particularly well-liked for effectively and speedily completing straightforward or repetitive activities.

A few characteristics of Python one-liners are as follows:

Concise: They eliminate needless verbosity and do a precise task in a single line of code.
Readability is a priority, even when they are condensed.
Utilizing built-in functions: The vast Python standard library has several built-in functions that may be used in oneliners.
Oneliners frequently use functional programming structures like lambda functions, list comprehensions, and map/filter/reduce functions.
Problem-specific: Each oneliner concentrates on a single problem and attempts to resolve it simply yet beautifully.

While Python oneliners might be effective and robust in some situations, they might not always be appropriate for challenging or extensive projects. In practical development, readability, maintainability, and adherence to recommended practices should always take precedence.

Python one-liners are handy for several things:

Concision: Oneliners allow you to convey a solution or procedure concisely. This can simplify your code and cut down on needless verbosity.

Efficiency: Shorter code snippets are frequently more effective than larger ones in completing tasks. They can assist you with doing straightforward tasks fast without writing extensive amounts of code.
Rapid prototyping: Oneliners can be particularly helpful for swiftly testing ideas and trying out various strategies while prototyping or exploring ideas. They offer a quick approach to iterate and improve your code.
Code golf is a recreational programming competition in which competitors try to create a minor code to address a particular issue. Code golfing competitions frequently prefer oneliners because they force programmers to develop original, concise solutions.
Command-line utilities: Python one-liners are widely employed in command-line contexts to carry out rapid operations or data manipulations. They can aid in work simplification and automate repetitive actions.
Educating and learning: Oneliners can be used as teaching aids to demonstrate Python's strength and adaptability. They can aid novices in clearly and practically understanding crucial ideas like list comprehensions, lambda functions, and built-in functions.

Oneliners can be helpful in some circumstances, but they may trade readability and maintainability in favor of lengthier, more descriptive code. So it's crucial to balance code clarity and conciseness, especially in larger projects or codebases that demand teamwork or ongoing maintenance.

Here are 10 practical Python one-liners that can help developers write code more quickly in their daily work.

1. Swap Two Variables:

first, second = second, first

This oneliner swaps the values of two variables, a and b, without using a temporary variable by tuple packing and unpacking. The right-hand side swaps the variables a and b as it constructs a tuple (b, a) and assigns it to the left-hand side variables a and b.

2. List Comprehension:

List comprehension is a shortcut for creating a new list by altering the components of an iterable that is already accessible (such as a list, set, tuple, etc.).

nums = [1, 2, 3, 4, 5]

squares = [val*val for val in nums]

      print(squares)

List comprehension with conditionals:

To understand lists, conditionals can also be used. For instance, suppose we need to make an even-numbered list from a given list:

Example:

nums = [1, 2, 3, 4, 5]

even_nums = []

for val in nums:

          if val % 2 == 0:

                    even_nums.append(val)

print(even_nums)

Oneliner for it:

nums = [1, 2, 3, 4, 5]

even_nums = [val for val in nums if val % 2 == 0]

print(even_nums)

Output

[2, 4]

3. Lambda function:

Anonymous functions, or functions without names, are lambda functions. This is because most of them are oneliners or are only needed once.

Syntax:

lambda argument(s): expression

Python uses the lambda keyword to construct an anonymous function. The variables needed to evaluate the expression are arguments. The function's body is its expression. The expression is returned once it has been evaluated.

The lambda function can also be kept as a variable for later usage.

Traditional way:

def sum(a, b):

  return a+b

# function call

sum(1, 5)

Oneliner:

def sum(a, b): return a+b

The lambda function mentioned above returns the total of the two parameters supplied in values. The function is kept in the sum variable.

4. Reverse a String:

This oneliner reverses a string using string slicing with a step of -1 ([::-1]). By specifying a step number of -1, slicing allows you to extract a piece of a string while flipping the characters' order.

reversed_string = 'Hello World'[::-1]

5. Find Duplicates in a list:

duplicates = [item for item, count in collections.Counter(my_list).items() if count > 1]

The collections are used in this oneliner. A counter class that counts how many times each element in the my_list has appeared. The result is an object resembling a dictionary, with the components' counts as the keys. The list comprehension [item for item, count in... if count > 1] loops over the elements and counts, extracting the items (elements) with a count greater than 1, which denotes duplication.

6. Remove duplicates from a list:

deduplicated_list = list(set(my_list))

By transforming my_list to a set, which only contains unique elements, this oneliner automatically removes duplicate entries from my_list. The list function then turns the set back into a list, producing a list with any duplicate members eliminated.

7. Ternary Operator:

Ternary operators, commonly called conditional expressions, offer a simple approach to making conditional statements.

Syntax:

 value_if_true if condition else value_if_false

The ternary operator tests the condition and returns value_if_true or value_if_false depending on whether it is true or false. Here is a thorough description of how ternary operators operate:

x = 5

result = "Even" if x % 2 == 0 else "Odd"

The ternary operator is employed in this instance to assign the value of the result based on the criterion x% 2 == 0. The value "Even" is given to the result if the condition evaluates to true (i.e., x is even). Otherwise, the value "Odd" is added to the result if the condition is false (i.e., x is odd).

8. Value Unpacking:

Value unpacking, also known as iterable unpacking or multiple assignments, is a Python feature that enables you to assign several variables with the values of an iterable (such as a tuple, list, or dictionary) in a single line of code. It offers an easy method to take values from the iterable and assign them to other variables. Here is a thorough description of how to value unpacking functions:

a, b, c = my_tuple