List Comprehension Basics

We met the list comprehension briefly in Chapter 4. Syntactically, its syntax is derived from a construct in set theory notation that applies an operation to each item in a set, but you don’t have to know set theory to use this tool. In Python, most people find that a list comprehension simply looks like a backward for loop.

To get a handle on the syntax, let’s dissect the prior section’s example in more detail:

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>>> L = [x + 10 for x in L]

List comprehensions are written in square brackets because they are ultimately a way to construct a new list. They begin with an arbitrary expression that we make up, which uses a loop variable that we make up (x + 10). That is followed by what you should now recognize as the header of a for loop, which names the loop variable, and an iterable object (for x in L).

To run the expression, Python executes an iteration across L inside the interpreter, assigning x to each item in turn, and collects the results of running the items through the expression on the left side. The result list we get back is exactly what the list comprehension says—a new list containing x + 10, for every x in L.

Technically speaking, list comprehensions are never really required because we can always build up a list of expression results manually with for loops that append results as we go:

>>> res = []
>>> for x in L:
...     res.append(x + 10)
...
>>> res
[21, 22, 23, 24, 25]

In fact, this is exactly what the list comprehension does internally.

However, list comprehensions are more concise to write, and because this code pattern of building up result lists is so common in Python work, they turn out to be very handy in many contexts. Moreover, list comprehensions can run much faster than manual for loop statements (often roughly twice as fast) because their iterations are performed at C language speed inside the interpreter, rather than with manual Python code; especially for larger data sets, there is a major performance advantage to using them.