Understanding map, filter, and zip in Python

The purpose of this article is to understand how Python built-in functions map(), filter(), and zip() work to make you more efficient with processing data in Python!

I will go over how these functions work and provide use-cases with and without the functions to compare.

It is important to understand how these functions work, but there is a better way once you're finished reading this! Its called Comprehensions and I write about them in Beginner Python Comprehensions

Map

Python's map() function is used to apply a function on all elements of a specified iterable and return a map object.

map() will take 2 required positional arguments. 1.) A function to run against iterables. 2.) An iterable (ie. list).

map(func, [...])

The expected output of this would be a map object with a memory position.

Lets take an example that applies the function square() to a list of integers. The objective here is to get a list of squared numbers from our original numbers list.

def square(number):
    return number*number

numbers = [1,2,3,4,5]

Without map()

def square(number):
    return number*number

numbers = [1,2,3,4,5]
squared_numbers = []

for number in numbers:
    squared = square(number)
    squared_numbers.append(squared)

With map()

def square(number):
    return number*number

numbers = [1,2,3,4,5]
squared_numbers = map(square, numbers)

What we observed:

• Using map() we don't need to create an empty list that we append to in a for loop.
• We do not need to use square() with parenthesis as the function parameter, as map() will call the function for us, we just pass the function object.
• map() will run square() for each item in numbers.

Filter

Python's filter() function checks a condition of each element in an iterable and returns a filtered data structure only containing elements who match the given conditions.

filter() will take 2 required positional arguments. 1.) A function to run against iterables. 2.) An iterable (ie. list).

filter(func, [...])

The function passed to filter() must return a boolean (True or False)

The expected output of this would be a filter object with a memory position.

Lets take an example that applies the function even() to a list of integers. The objective here is to get a list that only contains even numbers.

def even(number):
    if (number  % 2) == 0:
        return True
    return False

numbers = [1,2,3,4,5]

Without filter()

def even(number):
    if (number % 2) == 0:
        return True
    return False

numbers = [1,2,3,4,5]

even_numbers = []
for number in numbers:
    if even(number):
        even_numbers.append(number)

With filter()

def even(number):
    if (number % 2) == 0:
        return True
    return False

numbers = [1,2,3,4,5]
even_numbers = filter(even, numbers)

What we observed:

• Using filter() we don't need to create an empty list that we append to in a for loop.
• We do not need to use even() with parenthesis as the function parameter, as filter() will call the function for us, we just pass the function object.
• filter() will run even() for each item in numbers and remove all elements that returned False.

Zip

Python's zip() function combines elements of 2 lists with matching indexes into an interable of tuples.

zip() will take an undefined number of arguments where each argument is an iterable to zip.

zip([...], [...], [...], ...)

The expected output of this would be a zip object containing tuples of the same-index element of each iterable with a memory position..

Let's take the above 2 examples combined to create a list of even numbers with their respective squared results.

def even(number):
    if (number % 2) == 0:
        return  True
    return  False

def square(number):
    return number*number

numbers = [1,2,3,4,5]
even_numbers = filter(even, numbers)
even_numbers_squared = map(square, even_numbers)

Without zip()

...
even_numbers = [2,4]
even_numbers_squared = [4, 8]
combined = []
even_numbers_index = 0
for number  in even_numbers:
    squared = even_numbers_squared[even_numbers_index]
    squared_tuple = (number, squared)
    combined.append(squared_tuple)

With zip()

...
even_numbers = [2,4]
even_numbers_squared = [4, 8]
zipped_result = zip(even_numbers, even_numbers_squared)

What we observed:

• Using zip() we can combine values with matching indexes from multiple lists without creating a buffer list or iterating with a for loop.

Conclusion

So hopefully with this you have a basic grasp on how these functions work, why they're useful, and how they can help you write more efficient and more readable Python code!

It is important to understand how these functions work, but there is a better way once you're finished reading this! Its called Comprehensions and I write about them in Beginner Python Comprehensions