Ready to take the test? Head onto LearnX and get your Python Certification!
Map, Filter, Reduce
Map, Filter, and Reduce are paradigms of functional programming. They allow the programmer (you) to write simpler, shorter code, without neccessarily needing to bother about intricacies like loops and branching.
Essentially, these three functions allow you to apply a function across a number of iterables, in one fell swoop.
filter come built-in with Python (in the
__builtins__ module) and require no importing.
reduce, however, needs to be imported as it resides in the
functools module. Let's get a better understanding of how they all work, starting with
map() function in python has the following syntax:
func is the function on which each element in
iterables (as many as they are) would be applied on. Notice the asterisk(
iterables? It means there can be as many iterables as possible, in so far
func has that exact number as required input arguments. Before we move on to an example, it's important that you note the following:
- In Python 2, the
map()function returns a list. In Python 3, however, the function returns a
map objectwhich is a generator object. To get the result as a list, the built-in
list()function can be called on the map object. i.e.
- The number of arguments to
funcmust be the number of
Let's see how these rules play out with the following examples.
Say I have a list (
iterable) of my favourite pet names, all in lower case and I need them in uppercase. Traditonally, in normal pythoning, I would do something like this:
my_pets = ['alfred', 'tabitha', 'william', 'arla'] uppered_pets =  for pet in my_pets: pet_ = pet.upper() uppered_pets.append(pet_) print(uppered_pets)
Which would then output
['ALFRED', 'TABITHA', 'WILLIAM', 'ARLA']
map() functions, it's not only easier, but it's also much more flexible. I simply do this:
# Python 3 my_pets = ['alfred', 'tabitha', 'william', 'arla'] uppered_pets = list(map(str.upper, my_pets)) print(uppered_pets)
Which would also output the same result. Note that using the defined
map() syntax above,
func in this case is
iterables is the
my_pets list -- just one iterable. Also note that we did not call the
str.upper function (doing this:
str.upper()), as the map function does that for us on each element in the
What's more important to note is that the
str.upper function requires only one argument by definition and so we passed just one iterable to it. So, if the function you're passing requires two, or three, or n arguments, then you need to pass in two, three or n iterables to it. Let me clarify this with another example.
Say I have a list of circle areas that I calculated somewhere, all in five decimal places. And I need to round each element in the list up to its position decimal places, meaning that I have to round up the first element in the list to one decimal place, the second element in the list to two decimal places, the third element in the list to three decimal places, etc. With
map() this is a piece of cake. Let's see how.
Python already blesses us with the
round() built-in function that takes two arguments -- the number to round up and the number of decimal places to round the number up to. So, since the function requires two arguments, we need to pass in two iterables.
# Python 3 circle_areas = [3.56773, 5.57668, 4.00914, 56.24241, 9.01344, 32.00013] result = list(map(round, circle_areas, range(1,7))) print(result)
See the beauty of
map()? Can you imagine the flexibility this evokes?
range(1,7) function acts as the second argument to the
round function (the number of required decimal places per iteration). So as
map iterates through
circle_areas, during the first iteration, the first element of
3.56773 is passed along with the first element of
round, making it effectively become
round(3.56773, 1). During the second iteration, the second element of
5.57668 along with the second element of
2 is passed to
round making it translate to
round(5.57668, 2). This happens until the end of the
circle_areas list is reached.
I'm sure you're wondering: "What if I pass in an iterable less than or more than the length of the first iterable? That is, what if I pass
range(1, 9999) as the second iterable in the above function". And the answer is simple: nothing! Okay, that's not true. "Nothing" happens in the sense that the
map() function will not raise any exception, it will simply iterate over the elements until it can't find a second argument to the function, at which point it simply stops and returns the result.
So, for example, if you evaluate
result = list(map(round, circle_areas, range(1,3))), you won't get any error even as the length of
circle_areas and the length of
range(1,3) differ. Instead, this is what Python does: It takes the first element of
circle_areas and the first element of
range(1,3) and passes it to
round evaluates it then saves the result. Then it goes on to the second iteration, second element of
circle_areas and second element of
round saves it again. Now, in the third iteration (
circle_areas has a third element), Python takes the third element of
circle_areas and then tries to take the third element of
range(1,3) but since
range(1,3) does not have a third element, Python simply stops and returns the result, which in this case would simply be
Go ahead, try it.
# Python 3 circle_areas = [3.56773, 5.57668, 4.00914, 56.24241, 9.01344, 32.00013] result = list(map(round, circle_areas, range(1,3))) print(result)
The same thing happens if
circle_areas is less than the length of the second iterable. Python simply stops when it can't find the next element in one of the iterables.
To consolidate our knowledge of the
map() function, we are going to use it to implement our own custom
zip() function. The
zip() function is a function that takes a number of iterables and then creates a tuple containing each of the elements in the iterables. Like
map(), in Python 3, it returns a generator object, which can be easily converted to a list by calling the built-in
list function on it. Use the below interpreter session to get a grip of
zip() before we create ours with
# Python 3 my_strings = ['a', 'b', 'c', 'd', 'e'] my_numbers = [1,2,3,4,5] results = list(zip(my_strings, my_numbers)) print(results)
As a bonus, can you guess what would happen in the above session if
my_numbers are not of the same length? No? try it! Change the length of one of them.
Onto our own custom
# Python 3 my_strings = ['a', 'b', 'c', 'd', 'e'] my_numbers = [1,2,3,4,5] results = list(map(lambda x, y: (x, y), my_strings, my_numbers)) print(results)
Just look at that! We have the same result as
Did you also notice that I didn't even need to create a function using the
def my_function() standard way? That's how flexible
map(), and Python in general, is! I simply used a
lambda function. This is not to say that using the standard function definition method (of
def function_name()) isn't allowed, it still is. I simply preferred to write less code (be "Pythonic").
That's all about map. Onto
map() passes each element in the iterable through a function and returns the result of all elements having passed through the function,
filter(), first of all, requires the function to return boolean values (true or false) and then passes each element in the iterable through the function, "filtering" away those that are false. It has the following syntax:
The following points are to be noted regarding
map(), only one iterable is required.
funcargument is required to return a boolean type. If it doesn't,
filtersimply returns the
iterablepassed to it. Also, as only one iterable is required, it's implicit that
funcmust only take one argument.
filterpasses each element in the iterable through
funcand returns only the ones that evaluate to true. I mean, it's right there in the name -- a "filter".
Let's see some examples
The following is a list (
iterable) of the scores of 10 students in a Chemistry exam. Let's filter out those who passed with scores more than 75...using
# Python 3 scores = [66, 90, 68, 59, 76, 60, 88, 74, 81, 65] def is_A_student(score): return score > 75 over_75 = list(filter(is_A_student, scores)) print(over_75)
The next example will be a palindrome detector. A "palindrome" is a word, phrase, or sequence that reads the same backwards as forwards. Let's filter out words that are palindromes from a tuple (
iterable) of suspected palindromes.
# Python 3 dromes = ("demigod", "rewire", "madam", "freer", "anutforajaroftuna", "kiosk") palindromes = list(filter(lambda word: word == word[::-1], dromes)) print(palindromes)
Which should output
Pretty neat huh? Finally,
reduce applies a function of two arguments cumulatively to the elements of an iterable, optionally starting with an initial argument. It has the following syntax:
reduce(func, iterable[, initial])
func is the function on which each element in the
iterable gets cumulatively applied to, and
initial is the optional value that gets placed before the elements of the iterable in the calculation, and serves as a default when the iterable is empty. The following should be noted about
func requires two arguments, the first of which is the first element in
initial is not supplied) and the second element in
initial is supplied, then it becomes the first argument to
func and the first element in
iterable becomes the second element.
reduce "reduces" (I know, forgive me)
iterable into a single value.
As usual, let's see some examples.
Let's create our own version of Python's built-in
sum() function. The
sum() function returns the sum of all the items in the iterable passed to it.
# Python 3 from functools import reduce numbers = [3, 4, 6, 9, 34, 12] def custom_sum(first, second): return first + second result = reduce(custom_sum, numbers) print(result)
The result, as you'll expect is
So, what happened?
As usual, it's all about iterations:
reduce takes the first and second elements in
numbers and passes them to
custom_sum computes their sum and returns it to
reduce then takes that result and applies it as the first element to
custom_sum and takes the next element (third) in
numbers as the second element to
custom_sum. It does this continuously (cumulatively) until
numbers is exhausted.
Let's see what happens when I use the optional
# Python 3 from functools import reduce numbers = [3, 4, 6, 9, 34, 12] def custom_sum(first, second): return first + second result = reduce(custom_sum, numbers, 10) print(result)
The result, as you'll expect, is
reduce, initially, uses
10 as the first argument to
That's all about Python's Map, Reduce, and Filter. Try on the below exercises to help ascertain your understanding of each function.
In this exercise, you'll use each of
reduce to fix broken code.
from functools import reduce # Use map to print the square of each numbers rounded # to three decimal places my_floats = [4.35, 6.09, 3.25, 9.77, 2.16, 8.88, 4.59] # Use filter to print only the names that are less than # or equal to seven letters my_names = ["olumide", "akinremi", "josiah", "temidayo", "omoseun"] # Use reduce to print the product of these numbers my_numbers = [4, 6, 9, 23, 5] # Fix all three respectively. map_result = list(map(lambda x: x, my_floats)) filter_result = list(filter(lambda name: name, my_names, my_names)) reduce_result = reduce(lambda num1, num2: num1 * num2, my_numbers, 0) print(map_result) print(filter_result) print(reduce_result)
#### Map from functools import reduce my_floats = [4.35, 6.09, 3.25, 9.77, 2.16, 8.88, 4.59] my_names = ["olumide", "akinremi", "josiah", "temidayo", "omoseun"] my_numbers = [4, 6, 9, 23, 5] map_result = list(map(lambda x: round(x ** 2, 3), my_floats)) filter_result = list(filter(lambda name: len(name) <= 7, my_names)) reduce_result = reduce(lambda num1, num2: num1 * num2, my_numbers) print(map_result) print(filter_result) print(reduce_result)
test_output_contains("[18.922, 37.088, 10.562, 95.453, 4.666, 78.854, 21.068]") test_output_contains("['olumide', 'josiah', 'omoseun']") test_output_contains("24840") success_msg("Congrats! Nice work.")
This site is generously supported by DataCamp. DataCamp offers online interactive Python Tutorials for Data Science. Join over a million other learners and get started learning Python for data science today!
Ready to take the test? Head onto LearnX and get your Python Certification!Previous Tutorial Next Tutorial Take the Test