14.4 Functional Programming Tools

The map function is the simplest representative of a class of Python built-ins used for functional programming—which mostly just means tools that apply functions to sequences. Its relatives filter out items based on a test function (filter), and apply functions to pairs of items and running results (reduce). For example, the following filter call picks out items in a sequence greater than zero:

>>> range(-5, 5)
[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]

>>> filter((lambda x: x > 0), range(-5, 5))
[1, 2, 3, 4]

Items in the sequence for which the function returns true are added to the result list. Like map, it's roughly equivalent to a for loop, but is built-in and fast:

>>> res = [  ]
>>> for x in range(-5, 5):
...     if x > 0:
...         res.append(x)
...
>>> res
[1, 2, 3, 4]

Here are two reduce calls computing the sum and product of items in a list:

>>> reduce((lambda x, y: x + y), [1, 2, 3, 4])
10
>>> reduce((lambda x, y: x * y), [1, 2, 3, 4])
24

At each step, reduce passes the current sum or product, along with the next item from the list, to the passsed in lambda function. By default, the first item in the sequence initializes the starting value. Here's the for loop equivalent to the first of these, with the addition hardcoded inside the loop:

>>> L = [1,2,3,4]
>>> res = L[0]
>>> for x in L[1:]:
...     res = res + x
...
>>> res
10

If this has sparked your interest, also see the built-in operator module, which provides functions that correspond to built-in expressions, and so comes in handy for some uses of functional tools:

>>> import operator
>>> reduce(operator.add, [2, 4, 6])      # function-based +
12
>>> reduce((lambda x, y: x + y), [2, 4, 6])
12

Some observers might also extend the functional programming toolset in Python to include lambda and apply, and list comprehensions (discussed in the next section).