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Map, Filter, Reduce

Code sample link: https://replit.com/@jjoco/python-functional

These functions help the developer take a more functional approach to some problems.

Let's say we have an items list: 

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items = [0 , -1, -5, 3, 5, 6, 2, 1]

map

The map function allows the developer to apply a function to all items in a list, and returns an iterator that contains the output values.

Syntax 

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map_iterator = map(map_func, base_list)

Check out the following: 

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squared = list(map(lambda x: x**2, items))
print(squared) #[0, 1, 25, 9, 25, 36, 4, 1]
Note that list is needed to cast the iterator into a list. In this example, we create multiply each value in items by 2, and put the output results in a new list. FYI, you could also do the above using list comprehensions.

filter

filter creates a new list that contains the values of some base list based on some condition function.

Syntax 

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filtered_iter = filter(condition_func, items)

For example, 

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filtered = list(filter(lambda x: x < 0, items))
print(filtered) # 11
Here, we filter out anything in items that is not negative.

reduce

reduce is used to do some computation on an entire list, instead of each element separately. It performs a rolling computation on sequential pairs in the list in order to accumulate the elements into a single value.

Firstly, we need to import reduce from the functools library and use the following syntax: 

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from functools import reduce
result = reduce(reduce_func, items)

Consider the following: 

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from functools import reduce

total = reduce(lambda acc, nextVal : acc + nextVal, items)
print(total) # 11
In the above example, we use reduce to get the sum of the given items list.