4. Map, Filter and Reduce¶
These are three functions which facilitate a functional approach to programming. We will discuss them one by one and understand their use cases.
Map applies a function to all the items in an input_list. Here is
Most of the times we want to pass all the list elements to a function one-by-one and then collect the output. For instance:
items = [1, 2, 3, 4, 5] squared =  for i in items: squared.append(i**2)
Map allows us to implement this in a much simpler and nicer way.
Here you go:
items = [1, 2, 3, 4, 5] squared = list(map(lambda x: x**2, items))
Most of the times we use lambdas with
map so I did the same. Instead
of a list of inputs we can even have a list of functions!
def multiply(x): return (x*x) def add(x): return (x+x) funcs = [multiply, add] for i in range(5): value = list(map(lambda x: x(i), funcs)) print(value) # Output: # [0, 0] # [1, 2] # [4, 4] # [9, 6] # [16, 8]
As the name suggests,
filter creates a list of elements for which a
function returns true. Here is a short and concise example:
number_list = range(-5, 5) less_than_zero = list(filter(lambda x: x < 0, number_list)) print(less_than_zero) # Output: [-5, -4, -3, -2, -1]
The filter resembles a for loop but it is a builtin function and faster.
Note: If map & filter do not appear beautiful to you then you can
Reduce is a really useful function for performing some computation on
a list and returning the result. It applies a rolling computation to sequential
pairs of values in a list. For example, if you wanted to compute the product
of a list of integers.
So the normal way you might go about doing this task in python is using a basic for loop:
product = 1 list = [1, 2, 3, 4] for num in list: product = product * num # product = 24
Now let’s try it with reduce:
from functools import reduce product = reduce((lambda x, y: x * y), [1, 2, 3, 4]) # Output: 24