You Don't Need range()2020-04-16 #code #python #range
Beginners in Python tend to use
range() for iterating over lists. This is
not really necessary.
When people start programming Python, they tend to use constructions coming from other languages, so they iterate over a list with something like:
a_list = [1, 2, 3, 4] for i in range(len(a_list)): print(a_list[i])
But Python have the concept of "iterable", meaning some things can be iterated over, without the need of accessing each element individually. For example, our previous list can be iterated with:
a_list = [1, 2, 3, 4] for value in a_list: print(value)
"For every element in
a_list, retrieve it and name it
A lot of elements are iterable: Strings are iterable, returning every character in them; dictionaries are iterable, returning every key in them; sets are iterable, returning every element in them; tuples are iterable, returning every value in them; generators are iterable, return the next value they can produce.
But what if you need to iterate over more than one iterable at the same time?
zip() comes in.
zip() allows you to merge iterables:
a_list = [1, 2, 3, 4] a_tuple = ('a', 'b', 'c', 'd') for mixed_tuple in zip(a_list, a_tuple): print(mixed_tuple)
This code prints out:
(1, 'a') (2, 'b') (3, 'c') (4, 'd')
zip() does is create a tuple with the first element of the first
iterable and the first element of the second iterable; then the second element
of the first iterable and the second element of the second iterable; and so
on. You can put as many iterables as you want in
zip() and it will just
create larger tuples for each interaction.
One of the cool things in Python is "destructuring". Destructuring (de-structuring or more like "breaking apart a structure") allows one to extract elements from a iterable directly.
For example, if you have a tuple with two elements:
a_tuple = (1, 2)
... you'd probably take every element of it in separate variables with something like
a = a_tuple b = a_tuple
But with destructuring, you can do this in a single pass with
(a, b) = a_tuple
This code and the one above it will do exactly the same thing.
But why destructuring is important if we are talking about iterating over
for also has the destructuring capabilities:
a_list = [1, 2, 3, 4] a_tuple = ('b', 'c', 'd', 'f') a_string = 'aeio' for (a_number, lowercase_char, uppercase_char) in zip(a_list, a_tuple, a_string): print(a_number) print(lowercase_char) print(uppercase_char) print()
But what happens when one of the iterables is smaller than the other one?
a_short_list = [1, 2] a_long_list [10, 20, 30, 40, 50, 60, 70, 80, 90] for (small, big) in zip(a_short_list, a_long_list): print(small, big)
That will print
1 10 2 20
zip() stops when the shortest iterable have no more elements. To go as far
as the longest iterable, you need
zip_longest(), part of the
itertools module, will transverse the iterables
till every one of them have no more elements. What happens with the shortest
of those is that its value will be replaced with
None. Using our previous
import itertools a_short_list = [1, 2] a_long_list [10, 20, 30, 40, 50, 60, 70, 80, 90] for (small, big) in itertools.zip_longest(a_short_list, a_long_list): print(small, big)
That will print:
1 10 2 20 None 30 None 40 None 50 None 60 None 70 None 80 None 90
Careful with generators
One thing you must be careful when using
generators. Why? Because some of them have no end.
Let's take one example:
cycle(), also part of the itertools
module, is a generator that, on request, returns the next element of an
iterable but, as soon as this iterable is over, it starts over. For example
(and I'm tacking
zip() around this just for the sake of staying on topic,
and you don't need to use
a_list = [10, 20, 30, 40, 50, 60, 70, 80, 90] for (bullet, value) in zip(cycle(['-', '*', '.']), a_list): print(bullet, value)
That will produce:
- 10 * 20 . 30 - 40 * 50 . 60 - 70 * 80 . 90
What happened here is that
zip() took the first value of the first iterable,
cycle(['-', '*', '.']), which was the first value of its iterable,
'-', and the second value of the second iterable,
10; next iteration, the
second value of
'*' and the second value of
30; now, on
the fourth iteration,
cycle() was asked for a value and, with its iterable
exhausted, it returned to the first value, returning
So, what's the problem with generators?
Some generators -- like
cycle() above -- do not have an end. If you replace
zip_longest() on the code above, you'll see that the code will
never stop. It's not every generator the can produce values continuously,
though, so you can mess with them with no issue.
All nice and dandy, but what if I need to show the index itself?
enumerate() to the rescue!
Ok, so we talked about mixing more than one iterable, but what if we need the position? What if we have a list of ordered results and we need to show the position itself?
Again, you may be temped to use
winners = ['first place', 'second place', 'third place', 'fourth place'] for pos in range(len(winners)): print(pos + 1, winners[pos].capitalize())
That will print:
1 First place 2 Second place 3 Third place 4 Fourth place
One may also try to be clever and mix our newly found knowledge about
winners = ['first place', 'second place', 'third place', 'fourth place'] for (pos, name) in zip(range(len(winners)), winners): print(pos + 1, name.capitalize())
... which ,personally, looks even more cumbersome than the first option. But
Python have another generator called
enumerate() that takes one single
iterable, but produces tuples with the index of it and its value:
winners = ['first place', 'second place', 'third place', 'fourth place'] for (pos, name) in enumerate(winners): print(pos + 1, name.capitalize())
enumerate() have an option to define with will be the value of
the first element, so instead of that
pos + 1 in the
print() statement, we
can replace the enumerate to
enumerate(winners, start=1) and remove the
Iterables is one of the powerhouses of Python, as you may have noticed in the beginning with the number of things that can be iterated over. Understanding those will help you write better and more concise Python code, without losing meaning.