Iteration and Optimization

If the last section’s for loop looks like the list comprehension expression introduced earlier, it should: both are really general iteration tools. In fact, both will work on any object that follows the iteration protocol—a pervasive idea in Python that essentially means a physically stored sequence in memory, or an object that generates one item at a time in the context of an iteration operation. An object falls into the latter category if it responds to the iter built-in with an object that advances in response to next. The generator comprehension expression we saw earlier is such an object.

I’ll have more to say about the iteration protocol later in this book. For now, keep in mind that every Python tool that scans an object from left to right uses the iteration protocol. This is why the sorted call used in the prior section works on the dictionary directly—we don’t have to call the keys method to get a sequence because dictionaries are iterable objects, with a next that returns successive keys.

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This also means that any list comprehension expression, such as this one, which computes the squares of a list of numbers:

>>> squares = [x ** 2 for x in [1, 2, 3, 4, 5]]
>>> squares
[1, 4, 9, 16, 25]

can always be coded as an equivalent for loop that builds the result list manually by appending as it goes:

>>> squares = []
>>> for x in [1, 2, 3, 4, 5]:          # This is what a list comprehension does
        squares.append(x ** 2)         # Both run the iteration protocol internally

>>> squares
[1, 4, 9, 16, 25]

The list comprehension, though, and related functional programming tools like map and filter, will generally run faster than a for loop today (perhaps even twice as fast)—a property that could matter in your programs for large data sets. Having said that, though, I should point out that performance measures are tricky business in Python because it optimizes so much, and performance can vary from release to release.

A major rule of thumb in Python is to code for simplicity and readability first and worry about performance later, after your program is working, and after you’ve proved that there is a genuine performance concern. More often than not, your code will be quick enough as it is. If you do need to tweak code for performance, though, Python includes tools to help you out, including the time and timeit modules and the profile module. You’ll find more on these later in this book, and in the Python manuals.