# Functional Programming: Reduce() Function in Python

Reducing sequence to a single output value! Functional Programming: Reduce() Function in Python. In the previous string of articles, I have talked about the importance of immutable data structures in functional programming. I have also covered the filter() function in the last article. Let’s start from where we left. In this article, I will be talking about the reduce() function.

In the previous string of articles, I have talked about the importance of immutable data structures in functional programming. I have also covered the filter() function in the last article.

Let’s start from where we left. In this article, I will be talking about the reduce() function.

## Working with reduce() function

Just like the filter() function, reduce is another FP aka functional programming primitive that you can use in your Python code. The documentation defined reduce() function as:

“Apply function of two arguments cumulatively to the items of sequence, from left to right, so as to reduce the sequence to a single value.”

reduce(functioniterable[, _initializer_]): Looks like reduce() takes a function and an iterable (sequence of stuff). Although an initializer can also be given as a parameter and is placed before the items of the sequence in the calculation.

For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5). The left argument, x, is the accumulated value and the right argument, y, is the update value from the sequence.

It might seem a lot to grab. For a beginner, reduce() function gives us a descriptive value (eg. sum). It reduces a sequence to a single output value.

``````reduce(lambda x, y: x+y, [1, 2, 3, 4, 5])
[Output]: 15``````

The above line of code took a lambda function and calculated the sum of the sequence given as a parameter. “x” is the accumulated value from each iteration and “y” is the new value. In the end, it gave us the sum of the sequence.

Now that you have got the gist of the reduce() function. It’s time to bring our last problem in the picture and work with it only. Our dataset consists of GOT characters — name, profession, and experience.

``````got_chars=(
got_char("name":"John","profession":"King's Hand","experience_in_year":3),
got_char("name":"Arya Stark","profession":"No One","experience_in_year":4),
got_char("name":"Sansa Stark","profession":"Queen Winterfell","experience_in_year":1),
got_char("name":"Danaerys","profession":"Queen","experience_in_year":3)
)``````

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