Functional Python Programming

Chapter 8

Using the finite iterators

The itertools module provides a number of functions that we can use to produce
finite sequences of values. We'll look at ten functions in this module, plus some
related built-in functions:

• enumerate(): This function is actually part of the builtins package,
  but it works with an iterator and is very similar to other functions in the
  itertools module.


• accumulate(): This function returns a sequence of reductions of the input
  iterable. It's a higher-order function and can do a variety of clever calculations.


• chain(): This function combines multiple iterables serially.


• groupby(): This function uses a function to decompose a single iterable into
  a sequence of iterables over subsets of the input data.


• zip_longest(): This function combines elements from multiple iterables.
  The built-in zip() function truncates the sequence at the length of the
  shortest iterable. The zip_longest() function pads the shorter iterables
  with the given fillvalue.


• compress(): This function filters one iterable based on a second iterable
  of Boolean values.


• islice(): This function is the equivalent of a slice of a sequence when
  applied to an iterable.


• dropwhile() and takewhile(): Both of these functions use a Boolean
  function to filter items from an iterable. Unlike filter() or filterfalse(),
  these functions rely on a single True or False value to change their filter
  behavior for all subsequent values.


• filterfalse(): This function applies a filter function to an iterable.
  This complements the built-in filter() function.


• starmap(): This function maps a function to an iterable sequence of tuples
  using each iterable as an *args argument to the given function. The map()
  function does a similar thing using multiple parallel iterables.

We've grouped these functions into approximate categories. The categories are
roughly related to concepts of restructuring an iterable, filtering, and mapping.