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Day 08 - Comprehensions

The Python Comprehensions allow us in a clear and simple way, create sequences from other sequences, these work in a similar way to the filter and map functions.

Types of compressions:

  • list comprehensions
  • dictionary comprehensions
  • set comprehensions

List Comprehensions

We can create lists in a clear way, the list comprehension structure is:

new_list = [output for i in sequence if condition]

Almost all the comprehensions work in a similar way depending on the type of structure we need to obtain.

  • new_list: The variable which will contain the list.
  • output: The output of every element in the list.
  • i: Value in the sequence.
  • condition: (optional) Comprehensions can have conditions to decide which value it will keep.

Common Example

totals = []
for number in range(1, 6):
    totals.append(number * 2)

print(totals)

We can perform this same action with the map function as we saw before, however this time we will use comprehension:

totals = [number * 2 for number in range(1, 6)]
print(totals)

# Output:
# [2, 4, 6, 8, 10]

It works exactly the same, now how about filtering out just odd numbers?

Remember that we can add a conditional, if it is satisfied, the value will be kept in the new sequence

totals = [number * 2 for number in range(1, 6) if number % 2 != 0]
print(totals)

# Output:
# [2, 6, 10] -> 1, 3, 5 (odd numbers)

Dict Comprehensions

They work in a similar way, we just have to take into account, the type of braces that are used and the format of the dictionaries key: value

new_dict = {output_key: output_value for i in sequence if condition}

We will use a list of numbers and create a dictionary in which its key will be the real number and its value will be the number multiplied by itself.

new_dict = {}
numbers = [5, 8, 2, 6, 3]
for number in numbers:
    new_dict[number] = number * number

print(new_dict)

# Output:
# {5: 25, 8: 64, 2: 4, 6: 36, 3: 9}

Comprehension

numbers = [5, 8, 2, 6, 3]
new_dict = {number: number * number for number in numbers}
print(new_dict)

# Output:
# {5: 25, 8: 64, 2: 4, 6: 36, 3: 9}

In this example we can also use conditionals, the same way we can work with other types of data such as sets or generators.

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