Python is known as a popular and widely-used programming language in this fast-emerging world due to its simplicity, large community support, built-in libraries, and support for many features and libraries. In the context of simplicity, Python is known as a programming language that we can easily understand and learn because of its simple syntax and human-friendly nature.
In this article, we will learn about one of the valuable features that Python provides us: List comprehension, along with the definitions, syntax, advantages, some use cases, and how we can nest them, and a similar approach for creating dictionaries: Dictionary comprehension.
What is a list in Python?
A list is a collection of data encapsulated between the square brackets ([ ]), and commas separate these data (,). We can use these lists to store different types of data, such as integers, floats, strings, etc., and lists are dynamically sized, meaning we can extend a list’s length as needed.
What is list comprehension in Python?
In Python, list comprehension is a method or construct that can be used to define and create a list from a string or another existing list. Besides creating lists, we can filter and transform data using list comprehension, which has a more human-readable and concise syntax.
Syntax of list comprehension
[expression for item in iterable conditionals]
expression: expression is like a small program that we run on each item in the old list or any other iterable we use. Basically, It tells us what to do with each item to get the corresponding item in our new list.
Let’s say we want to have the square of each number included in our newly created list. To get this customized value, our expression would be ‘x * 2,’ where ‘x’ represents each item.
item: This represents each item in the iterable. For example, let’s say we have a list with numbers from 1 to 10. So, each number in this list is an item.
iterable: iterable can be a list, set, range, or any other object through which we will be iterating. Simply, It’s our existing list.
conditionals (optional): This is quite important because we can use these conditionals to filter out unwanted values, which normally require a call to ‘filter()’ method.
Let’s say we have a list of numbers from 1 to 10. But our new list must contain only the even numbers from the old list. To filter out the even numbers, we can use conditionals.
How list comprehension works.
First, it will create a new list upon the execution of the defined list comprehension code line.
Then, it iterates through the iterable while evaluating the expression on each element in each iteration.
If the condition is specified and evaluated to True, the result of the expression is added to the new list.
Once the iteration is complete, the new list is returned to us.
Advantages of using list comprehensions in Python.
Simplifies the readability: As we saw earlier, list comprehensions are more concise and readable code lines than traditional loops. They let us create a list in a single line, which can be easier to understand, especially for simple operations.
Simplicity of the code: List comprehensions simplify code by encapsulating the iteration and item creation logic in a single place. Therefore, we can reduce the usage of extra variables and code lines, making the codebase clean.
Enhanced performance: We can often use list comprehensions more efficiently than for loops as they are optimized at the CPython interpreter level. This will enhance the performance, especially for large datasets.
Conclusion
Today, we learned about one of Python’s coolest features: List comprehensions. As we discussed, list comprehensions in Python can be used to create a list from an existing list or any other source, such as a range of numbers or a string. It eases the creation process and avoids the complexities of traditional list-generating methods.
It is always good to have an idea when to use these features and when to not because some developers find it difficult when there is a usage of list comprehensions and similar approaches, which can lead to poor code understanding and reading.
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