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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.
4.1. Map¶
Map
applies a function to all the items in an input_list. Here is
the blueprint:
Blueprint
map(function_to_apply, list_of_inputs)
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]
4.2. Filter¶
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
read about list/dict/tuple
comprehensions.
4.3. Reduce¶
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