Prashant Sharma — August 26, 2021
Beginner Programming Python

This article was published as a part of the Data Science Blogathon

INTRODUCTION

As we know Python is one of the most widely used programming languages in the world. Its widespread popularity can be attributed to its simple and easy-to-understand code. If you’re familiar with the syntax, writing code in Python is a breeze. Its list comprehension feature is one of its key attractions. With just one line of code, you can perform Python comprehension for lists.

The following points will explain what list comprehension is and how it can be applied in various situations. We’ll also go over the differences between list comprehension and for loops, which are often misunderstood. Other related functions and how to use them will also be covered.

 

List comprehension

Python provides several methods for creating lists. The list comprehension feature is one of the most effective of these methods. It allows you to create lists with just one line of code.

It can be used to create new lists from other iterable elements such as arrays, strings, tuples, lists, and so on. It consists of brackets containing the expression. To iterate over all of the elements, the system uses the for loop to execute the expression for each one.

The following is the Python syntax:

our_new_list = [expression for element in our_old_list if condition]

In the above syntax, our_new_list is the new list (or the result). The variable you used for each element in your old list determines the expression. After that, the filter is the “if condition.”

There are many reasons why Python developers favor list comprehension over loops. The main reason is that it is more efficient. When you use list comprehension instead of loops, you can create a list with far less effort and code.

List comprehension saves time because it requires fewer lines of code than loops. It makes the code more user-friendly by keeping it simple. Furthermore, list comprehension covers the incorporation of an iterative statement into a formula.

Examples of List comprehension in Python

It’s best to study them and then implement them in your own program once you’ve figured out how they work. It will assist you in properly understanding how effective tool list comprehension is:

Example 1:

We’ll make a simple list using Python comprehension in this example:

Input:
 a = [i for i in range(8)]
 print a
Output:
 [0, 1, 2, 3, 4,5,6,7]

Example 2:

We’ll make a list with multiples of two in the example below:

Input:
 new_list = [a*2 for a in range(8)]
Output
[0, 2, 4, 6, 8, 10, 12, 14, 16]

Example 3:

We can multiply every item in a list by using list comprehension:

Input
old_list = [1, 2, 3,4,5]
 new_list = [item*2 for item in old_list]
 print new_list
Output
[2, 4, 6,8,10]

Example 4:

List comprehension isn’t just for integers; it can also be applied to strings. In this example, we will use list comprehension to create a list of the first letter of every word in our original list

Input: 
 my_list = [“playing”, “is”, “fun”]
 result = [word[0] for word in my_list]
 print result
Output
[‘p’, ‘i’, ‘f’]

Difference between list comprehension and for loop

The for loop is a common way to iterate through a list. List comprehension, on the other hand, is a more efficient way to iterate through a list because it requires fewer lines of code.

Here is an example to illustrate the difference. We will start with an empty list and modify it to make it a list of even numbers:

Input
# creating the empty list
 old_list = []
 # we will use the for loop to create the new list
 for x in range(10):
 old_list.append(x*2)
 print old_list 
Output: 
 [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

As you can see, the for loop requires you to create a new list using the append function. You also had to make an empty list first so that you could use the loop to change it. You wouldn’t need to use another function if you used list comprehension because the task can be completed with just one line of code:

Input: 
 # creating the set with the help of list comprehension
 old_list = [x*2 for x in range(10)]
 print old_list
Output:
 [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

List comprehension requires less computation power than a for loop because it takes up less space and code. This is useful when working on large programs, as efficiency becomes a major consideration when the code is lengthy.

However, when you are using it, you should avoid using ones that are too long in one line. Otherwise, the code would not be user-friendly, and sharing your work with other developers would be difficult. Keep in mind that while every comprehension can be converted to a for loop, not every for loop can be converted to a list comprehension.

This is one of the many comprehension features available in the Python programming language. Dictionary comprehension is another popular feature of comprehension software.

 

Dictionary comprehension

Dictionary comprehension allows you to create dictionaries in Python, as the name implies. A dictionary comprehension’s syntax is as follows:

#{k:v for (k,v) in iterable}

In the above syntax, k is the key and v is the value.

We’ll create two lists in the following example to generate a dictionary from them using the dictionary understanding:

Input:
 # using Python to showcase dictionary comprehension
 # creation of two lists to represent keys and values
 keys = [1, 2, 3, 4, 5, 6]
 values = [‘a’, ‘b’, ‘c’ ‘d’, ‘e’, ‘f’]
 # implementing dictionary comprehension
 new_dict = { k:v for (k,v) in zip(keys, values)}
 print new_dict
Output:
 {1 : ‘a’, 2 : ‘b’, 3 : ‘c’, 4 : ‘d’, 5 : ‘e’, 6 : ‘f’}

Dictionary comprehension makes creating dictionaries in Python a breeze. However, list comprehension can also be used to create a dictionary. We’ll use list comprehension to make a dictionary in the following example:

Input:
 new_dict = {x: x*3 for x in [6, 5, 4, 3, 2, 1]}
 print new_dict
Output:
 {6 ; 18, 5 : 15, 4 : 12, 3 : 9, 2 : 6, 1 : 3}

Conclusion

Python has a lot of features, and one of them is list comprehension. It’s a powerful and versatile programming language. Furthermore, Python’s syntax is simple to learn and requires little effort to master. Aside from these advantages, Python users have access to a wide range of libraries.

It has a thriving developer and programming community that adds new libraries to its collection on a regular basis. Python’s numerous libraries and functions make it useful in a variety of fields, including software development, data science, machine learning, and others.

You can begin by practicing this on with simple lists before moving on to more complex applications. The flexibility and robustness of Python programming can be seen.

About The Author

Prashant Sharma

Currently, I Am pursuing my Bachelors of Technology( B.Tech) from Vellore Institute of Technology. I am very enthusiastic about programming and its real applications including software development, machine learning, and data science.

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