Python Comprehensions

Types of Comprehensions in Python

List comprehensions in Python

List comprehensions in Python provide a concise way to create lists. They allow you to create a new list by applying an expression to each item in an existing iterable (e.g., a list, tuple, or string), optionally with a condition to filter items.

Syntax:

• new_list: new list being created.
• expression: refers to the expression that would be used to manipulate the elements in the list. It could be a formula or a simple value.
• item: refers to the original element in the list.
• iterable: refers to the original sequence being used to create a new_list.
• condition: It is an optional parameter used to reduce the length of the new_list, making it shorter than the original sequence.

Now, let’s understand this better with some examples.

Example 1:

Considering the example of converting a list of red balls to red. All we do is create a red_list from the existing blue_list by naming each element as red in the new list.

Output:

Example 2:

Considering you have a list of consecutive even numbers and want to create a list of consecutive odd numbers, you can do it by adding 1 to every element of the list. This is achieved by considering the item in even_list as var and then adding 1 to it, as shown in the following code:

Output:

Dictionary comprehensions in Python?

Dictionary comprehensions are used to create a fresh dictionary from an existing sequence of dictionaries or lists by manipulating its elements. They have a similar syntax to list comprehensions and set comprehensions but are used to generate dictionaries.

Syntax:

• new_dict: new dictionary being created.
• key: The expression for the dictionary key.
• value: The expression for the dictionary value.
• item: The variable representing an element in the original iterable.
• iterable: The existing sequence you are iterating over.
• condition (optional): An optional filtering condition. The item is included in the new dictionary only if this condition is true.

Let’s understand this better with some examples.

Example 1:

Consider a list of dictionary objects containing words and their meanings where each dictionary is in the format:

We aim to create a sequence of dictionaries where each dictionary object is in the format:

To do so, we create a new dictionary using dictionary comprehension as follows:

In the above code, d[‘word’] represents the value held by the key, word and the d[‘meaning’] represents the value held by the key meaning.

Output:

Example 2:

Consider the following example of 1 list, one containing the months of a year and another consisting of natural numbers up to 12. We aim to create a dictionary where the key is the number of the month, and the value is the month's name. A dictionary using dictionary comprehension for the given purpose would be created as follows:

Output:

Set comprehensions in Python

In Python, set comprehensions are used to create a new set from an existing sequence (set) by manipulating its elements.They have a similar syntax to list comprehensions, but they generate sets instead.

Syntax:

• new_set: new set being created.
• expression: The operation or value to be included in the new set.
• item: The variable representing an element in the original iterable.
• iterable: The existing sequence you are iterating over.
• condition (optional): An optional filtering condition. The item is included in the new set only if this condition is true.

Let’s understand this better with some examples.

Example 1:

Considering the given example of converting a set of red balls to red. All we do is create a red_set from the existing blue_set by naming each element as red in the new set as follows:

The entire code for this example is given below:

Output:

Example 2:

Consider you have a list of consecutive even numbers and want to create a set of consecutive odd numbers. You can do it by adding 1 to every element of the set. This is done by considering each item in the existing set even_set as var and then adding 1 to var, as shown in the code below:

Output:

Generator comprehensions in Python

This is one of the most rarely used and least known comprehensions in Python. In Python, generator comprehensions are used to create a new generator from an existing list by manipulating its elements. A generator in Python is a function that returns an iterator that holds a sequence of values instead of a single value. Generators are usually memory efficient as they allocate memory on the generation of items rather than at the beginning.

Syntax:

• new_gen: new generator being created.
• expression: The operation or value to be included in the new set.
• item: The variable representing an element in the original iterable.
• iterable: The existing sequence you are iterating over.
• condition (optional): An optional filtering condition. The item is included in the new set only if this condition is true.

Now, let’s understand this better with an example.

Consider you have been given a list of the first 6 even numbers, and you want to generate a list consisting of the first 6 odd numbers from it. You would simply add a 1 to the numbers given in the existing list. The same can be coded as follows:

Output: