Programmers often need to deal with lists of values, and most languages provide data structures for this purpose (in Python
list is usually used).
However, in functional programming we don't usually deal with lists directly. Instead we use an iterator that fetches values from a list or other sequence. This has two advantages:
- An iterator can read from a list but it can't modify the list. So if you pass an iterator into a function, you can be sure that the function will not change the list. This is important because in functional programming we prefer to use functions that don't have any side effects (and modifying a list passed into the function is a side effect).
- Iterators support lazy evaluation, which we will explore later in this article.
A Python lists, tuples and strings are all types of
iterable object. This means that you can iterate over the object, reading
the values in order.
Iterators and iterables are slightly different things, as you will see in the next article in this series.
The most common way to iterate over an iterable is in a for loop:
k = [10, 20, 30, 40] for x in k: print(x)
The for loop reads the values from the iterable, one at a time, and executes the loop for each value.
Now we will use a slightly more complex example to illustrate what is meant by lazy evaluation. We will use the built-in
that accepts a function and an iterable. It return an iterator that applies the function to each item in the input iterable.
def print_all(it): for x in it: print(x) def square(x): return x*x range_it = range(10) map_it = map(square, range_it) print_all(map_it)
Here we define function
square that calculates the square of
We call the
range function to create a sequence of values 0 ... 9.
range returns an iterable that will produce those values when they
map function applies
square to the values in
range_it. The result of this is an iterator
map_it. The iterator will produce the
sequence 1, 4, 9, 16 ... 81 (the squares of values in the original list).
We then use the previously defined function,
print_all to print the values. It will print 1, 4, 9, 16 ... 81.
Although it is simple enough to understand what the code will do, the order of execution is quite interesting:
1. When we call the
range function, it simply returns an iterable. It doesn't create the values yet.
2. When we call the
map function, it simply returns an iterator. It doesn't do any calculations. The
square function doesn't get
called at this stage.
print_all function accepts the
map_it iterator and loops over its values.
Each time through the for loop in
print_all, the following happens:
- The for loop requests the next value from the
map_ititerator requests the next value from the
map_ititerator applies the
squarefunction to the value, and returns the result.
print_allfunction prints the result.
- Loop back until finished...
What the code has done is to set up a chain of linked iterators from the
print_all for loop right back to the
range function. This
means that each number in the range isn't created until it is needed. The complete list of values is never stored in memory, and the first
value is printed almost immediately.
Lazy evaluation and large sequences
To test this, let's make the range much bigger:
range_it = range(10**1000)
10 to the power of 1000 is a truly huge number - a one followed by 1000 zeros. Your computer can't store a list that big (it is larger than the number of atoms in the universe), and it would never be able to fill the list with numbers (it would almost certainlytake longer than the predicted lifetime of the universe, however fast computers might get).
So if Python actually tried to create this range as a list, the program would never get past this step.
But in fact, all the
range function does is create an iterable that promises to create all those numbers in the future. And that takes
almost no time at all.
Try running the program with this new range. It will start printing values immediately. Just don't expect it to finish any time soon!
- Introduction to Functional Programming
- Pure functions
- Lambda functions
- Iterators vs iterables
- Built-in functions on iterables
- Transforming iterables
- Map/reduce example
- Functional design patterns
- Recursion and the lru_cache in Python
- Partial application
- Failure monad
- List monad
- Maybe monad
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