10.7: Map, filter and reduce

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To add up all the numbers in a list, you can use a loop like this:

def add_all(t):
    total = 0
    for x in t:
        total += x
    return total

total is initialized to 0. Each time through the loop, x gets one element from the list. The += operator provides a short way to update a variable. This augmented assignment statement,

    total += x

is equivalent to

    total = total + x

As the loop runs, total accumulates the sum of the elements; a variable used this way is sometimes called an accumulator.

Adding up the elements of a list is such a common operation that Python provides it as a built-in function, sum:

>>> t = [1, 2, 3]
>>> sum(t)
6

An operation like this that combines a sequence of elements into a single value is sometimes called reduce.

Sometimes you want to traverse one list while building another. For example, the following function takes a list of strings and returns a new list that contains capitalized strings:

def capitalize_all(t):
    res = []
    for s in t:
        res.append(s.capitalize())
    return res

res is initialized with an empty list; each time through the loop, we append the next element. So res is another kind of accumulator.

An operation like capitalize_all is sometimes called a map because it “maps” a function (in this case the method capitalize) onto each of the elements in a sequence.

Another common operation is to select some of the elements from a list and return a sublist. For example, the following function takes a list of strings and returns a list that contains only the uppercase strings:

def only_upper(t):
    res = []
    for s in t:
        if s.isupper():
            res.append(s)
    return res

isupper is a string method that returns True if the string contains only upper case letters.

An operation like only_upper is called a filter because it selects some of the elements and filters out the others.

Most common list operations can be expressed as a combination of map, filter and reduce.