Introduction to Functional Programming in Python

comments

The Map Function

 
While the ability to pass in functions as arguments is not unique to Python, it is a recent development in programming languages. Functions that allow for this type of behavior are called first-class functions. Any language that contains first-class functions can be written in a functional style.

There are a set of important first-class functions that are commonly used within the functional paradigm. These functions take in a Python iterable, and, like sorted(), apply a function for each element in the list. Over the next few sections, we will examine each of these functions, but they all follow the general form of function_name(function_to_apply, iterable_of_elements).

The first function we'll work with is the map() function. The map() function takes in an iterable (ie. list), and creates a new iterable object, a special map object. The new object has the first-class function applied to every element.

# Pseudocode for map.
def map(func, seq):
    # Return `Map` object with
    # the function applied to every
    # element.
    return Map(
        func(x)
        for x in seq
    )

Here's how we could use map to add 10 or 20 to every element in a list:

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

# Note: We convert the returned map object to
# a list data structure.
add_10 = list(map(lambda x: x + 10, values))
add_20 = list(map(lambda x: x + 20, values))

print(add_10)
>> [11, 12, 13, 14, 15]

print(add_20)
>> [21, 22, 23, 24, 25]

Note that it's important to cast the return value from map() as a list object. Using the returned map object is difficult to work with if you're expecting it to function like a list. First, printing it does not show each of its items, and secondly, you can only iterate over it once.

The Filter Function

 
The second function we'll work with is the filter() function. The filter() function takes in an iterable, creates a new iterable object (again, a special map object), and a first-class function that must return a bool value. The new map object is a filtered iterable of all elements that returned True.

# Pseudocode for filter.
def filter(evaluate, seq):
    # Return `Map` object with
    # the evaluate function applied to every
    # element.
    return Map(
        x for x in seq
        if evaluate(x) is True
    )

Here's how we could filter odd or even values from a list:

values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Note: We convert the returned filter object to
# a list data structure.
even = list(filter(lambda x: x % 2 == 0, values))
odd = list(filter(lambda x: x % 2 == 1, values))

print(even)
>> [2, 4, 6, 8, 10]

print(odd)
>> [1, 3, 5, 7, 9]

The Reduce Function

 
The last function we'll look at is the reduce() function from the functools package. The reduce() function takes in an iterable, and then reduces the iterable to a single value. Reduce is different from filter() and map(), because reduce() takes in a function that has two input values.

Here's an example of how we can use reduce() to sum all elements in a list.

from functools import reduce

values = [1, 2, 3, 4]

summed = reduce(lambda a, b: a + b, values)
print(summed)
>> 10

An interesting note to make is that you do not have to operate on the second value in the lambda expression. For example, you can write a function that always returns the first value of an iterable:

from functools import reduce

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

# By convention, we add `_` as a placeholder for an input
# we do not use.
first_value = reduce(lambda a, _: a, values)
print(first_value)
>> 1

Rewriting with list comprehensions

 
Because we eventually convert to lists, we should rewrite the map() and filter() functions using list comprehension instead. This is the more pythonic way of writing them, as we are taking advantage of the Python syntax for making lists. Here's how you could translate the previous examples of map() and filter() to list comprehensions:

values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Map.
add_10 = [x + 10 for x in values]
print(add_10)
>> [11, 12, 13, 14, 15, 16, 17, 18, 19, 20]

# Filter.
even = [x for x in values if x % 2 == 0]
print(even)
>> [2, 4, 6, 8, 10]

From the examples, you can see that we don't need to add the lambda expressions. If you are looking to add map(), or filter() functions to your own code, this is usually the recommended way. However, in the next section, we'll provide a case to still use the map()and filter() functions.

Writing Function Partials

 
Sometimes we want to use the behavior of a function, but decrease the number of arguments it takes. The purpose is to "save" one of the inputs, and create a new function that defaults the behavior using the saved input. Suppose we wanted to write a function that would always add 2 to any number:

def add_two(b):
    return 2 + b 

print(add_two(4))
>> 6

The add_two function is similar to the general function, $f(a,b) = a + b$, only it defaults one of the arguments ($a = 2$). In Python, we can use the partial module from the functoolspackage to set these argument defaults. The partial module takes in a function, and "freezes" any number of args (or kwargs), starting from the first argument, then returns a new function with the default inputs.

from functools import partial

def add(a, b):
    return a + b


add_two = partial(add, 2)
add_ten = partial(add, 10)

print(add_two(4))
>> 6

print(add_ten(4))
>> 14

Partials can take in any function, including ones from the standard library.

# A partial that grabs IP addresses using
# the `map` function from the standard library.
extract_ips = partial(
    map,
    lambda x: x.split(' ')[0]
)
lines = read('example_log.txt')
ip_addresses = list(extract_ip(lines))

Next steps

 
In this post, we introduced the paradigm of functional programming. We learned about the lambda expression in Python, important functional functions, and the concept of partials. Overall, we showed that Python provides a programmer with the tools to easily switch between functional programming and object-oriented programming.

If you've enjoyed functional programming, our newest course: Building a Data Pipeline uses functional programming concepts to build a data pipeline. In this course, you'll find more advanced Python concepts, examples of good API design, and a final project that uses your own data pipeline built from scratch!

 
Bio: Spiro Sideris is a Data Engineer content author at Dataquest.io. A software engineer with a knack for economics and a love for mathematics.

Original. Reposted with permission.

Related:

• Web Scraping Tutorial with Python: Tips and Tricks
• Moving from R to Python: The Libraries You Need to Know
• Data Structures Related to Machine Learning Algorithms