# Comprehensions

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Python Comprehensions constitute an elegant and concise way to create new lists, dictionaries, and sets from existing iterables. They are fast, efficient, and require fewer lines of code. In this chapter, we will dive deep into Python Comprehensions, how they work, and their application with practical code snippets.

Let's start with the simplest and most commonly used comprehension, "List Comprehension".

**List Comprehension**

A list comprehension provides a compact way of mapping a list into another list by applying an operation to each of the elements of the existing list. The resulting list comprehension consists of outputs of the operation for each element in the existing list.

Here's a simple example:

```
numbers = [1, 2, 3, 4, 5]
squares = [n**2 for n in numbers]
print(squares) # Output: [1, 4, 9, 16, 25]
```

In the above example, `n**2`

is the operation, `n`

is the variable representing members of the `numbers`

list.

You can also apply conditions within a list comprehension. For example, if you wanted only the squares of the even numbers, you'd do:

```
numbers = [1, 2, 3, 4, 5]
even_squares = [n**2 for n in numbers if n%2==0]
print(even_squares) # Output: [4, 16]
```

**Dictionary Comprehension**

Just like lists, dictionary comprehensions allow you to express the creation of dictionaries at runtime in a clear and concise manner.

Here's how you create a dictionary using dictionary comprehension:

```
numbers = [1, 2, 3, 4, 5]
squares = {n: n**2 for n in numbers}
print(squares) # Output: {1: 1, 2: 4, 3: 9, 4: 16, 5: 25}
```

In this example, `n: n**2`

is a key-value pair, and `n`

is the member of `numbers`

list.

**Set Comprehension**

Set comprehension is very similar to list and dictionary comprehension, but it produces a set, which is an unordered collection of unique elements.

Here is an example:

```
numbers = [1, 2, 2, 3, 4, 4, 5, 5]
unique_squares = {n**2 for n in numbers}
print(unique_squares) # Output: {1, 4, 9, 16, 25}
```

In this example, we started with a list that had duplicate values. But our set comprehension automatically removed the duplicate values in the result set because sets only allow unique elements.

In conclusion, Python comprehensions offer developers a powerful tool to create and manipulate data structures in a readable and efficient manner. As we've seen, they can be applied in various ways to solve different problems, making them a useful feature to understand and apply in your Python coding journey.

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