Iteration in Python: for, list, and map
The basics of writing iterations in Python using for loops, list comprehensions, and map.python coding
Iteration in Python can be a little hard to understand. Subtle differences in terminology like iteration, iterator, iterating, and iterable aren’t the most beginner-friendly.
When tackling new concepts, I find concrete examples to be most useful. I’ll share some in this post and discuss appropriate situations for each. (Pun intended.)
First, in pseudocode:
for iterating_variable in iterable: statement(s)
for loops to be the most readable way to iterate in Python. This is especially nice when you’re writing code that someone else needs to read and understand, which is always.
iterating_variable, loosely speaking, is anything you could put in a group. For example: a letter in a string, an item from a list, or an integer in a range of integers.
iterable houses the things you iterate on. This can also take different forms: a string with multiple characters, a range of numbers, a list, and so on.
statement or multiple
statements indicates doing something to the iterating variable. This could be anything from mathematical expressions to simply printing a result.
Here are a couple simple examples that print each
iterating_variable of an
for letter in "Hello world": print(letter) for i in range(10): print(i) breakfast_menu = ["toast", "eggs", "waffles", "coffee"] for choice in breakfast_menu: print(choice)
You can even use a
for loop in a more compact situation, such as this one-liner:
breakfast_buffet = " ".join(str(item) for item in breakfast_menu)
The downside to
for loops is that they can be a bit verbose, depending on how much you’re trying to achieve. Still, for anyone hoping to make their Python code as easily understood as possible,
for loops are the most straightforward choice.
A pseudocode example:
new_list = [statement(s) for iterating_variable in iterable]
List comprehensions are a concise and elegant way to create a new list by iterating on variables. Once you have a grasp of how they work, you can perform efficient iterations with very little code.
List comprehensions will always return a list, which may or may not be appropriate for your situation.
For example, you could use a list comprehension to quickly calculate and print tip percentage on a few bar tabs at once:
tabs = [23.60, 42.10, 17.50] tabs_incl_tip = [round(tab*1.15, 2) for tab in tabs] print(tabs_incl_tip) >>> [27.14, 48.41, 20.12]
In one concise line, we’ve taken each tab amount, added a 15% tip, rounded it to the nearest cent, and made a new list of the tabs plus the tip values.
List comprehensions can be an elegant tool if output to a list is useful to you. Be advised that the more statements you add, the more complicated your list comprehension begins to look, especially once you get into nested list comprehensions. If your code isn’t well annotated, it may become difficult for another reader to figure out.
map, in pseudocode:
Map is pretty compact, for better or worse. It can be harder to read and understand, especially if your line of code has a lot of parentheses.
In terms of efficiency for character count,
map is hard to beat. It applies your
statement to every instance of your
iterable and returns an iterator.
Here’s an example casting each element of
input() (the iterable) from string representation to integer representation. Since
map returns an iterator, you also cast the result to a list representation.
values = list(map(int, input().split())) weights = list(map(int, input().split()))
It’s worth noting that you can also use
for loops, list comprehension, and
map all together:
output = sum([x * x for x in zip(values, weights)]) / sum(weights) print(round(output, 1))
Your iteration toolbox
Each of these methods of iteration in Python have a special place in the code I write every day. I hope these examples have helped you see how to use
for loops, list comprehensions, and
map in your own Python code!
If you like this post, there’s a lot more where that came from! I write about efficient programming for coders and for leading technical teams. Check out the posts below!