I understand the importance of list comprehensions, but do not understand their inner-workings, thus am not able to understand them in simpler terms such as I would a for lo
It's just a shorter way of expressing a list
.
li = [row[index] for row in outer_list]
is equivalent to:
li = []
for row in outer_list:
li.append(row[index])
Once you get used to the syntax, it becomes a tidy way of creating list
s (and other iterables).
Assuming the outer iteration is for the index, this code can be effectively re-written as :
for row in outer_list:
li.append(row[index])
Here you are iterating in the lists inside the outer_list
. So, the first iteration would add the first list inside outer_list
to li
, then the second one is appended and so on.
How could I change this to a for loop?
li = [row[index] for row in outer_list]
li = []
for row in outer_list:
li.append(row[index])
Your question seems to imply that this snippet is already inside of a for loop, like this:
outer_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
for index in xrange(3):
li = [row[index] for row in outer_list]
print li
which would output:
[1, 4, 7]
[2, 5, 8]
[3, 6, 9]
You could convert this to use only conventional for
loops by nesting an inner loop inside of your outer loop:
outer_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
for index in xrange(3):
li = [] # initial empty list
for row in outer_list: # our inner loop
li.append(row[index]) # Build the list "li" iteratively
print li
If you are treating this nested list as a matrix, then what you describe is essentially taking the transpose of the matrix. You can do this entirely via list comprehensions as well:
outer_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
transpose = [[row[i] for row in outer_list] for i in range(len(outer_list[0]))]
print transpose
which yields:
[[1, 4, 7],
[2, 5, 8],
[3, 6, 9]]
As shown in the official Python tutorial here.
I've included the nested list comprehension example by way of completeness but a word to the wise: using nested list comprehensions generally makes code less comprehensible, not more. Just because you can doesn't mean you should.