Learning Python - Mark Lutz [269]
Both lambda and def create function objects to be called later. Because lambda is an expression, though, it returns a function object instead of assigning it to a name, and it can be used to nest a function definition in places where a def will not work syntactically. A lambda only allows for a single implicit return value expression, though; because it does not support a block of statements, it is not ideal for larger functions.
lambdas allow us to “inline” small units of executable code, defer its execution, and provide it with state in the form of default arguments and enclosing scope variables. Using a lambda is never required; you can always code a def instead and reference the function by name. lambdas come in handy, though, to embed small pieces of deferred code that are unlikely to be used elsewhere in a program. They commonly appear in callback-based program such as GUIs, and they have a natural affinity with function tools like map and filter that expect a processing function.
These three built-in functions all apply another function to items in a sequence (iterable) object and collect results. map passes each item to the function and collects all results, filter collects items for which the function returns a True value, and reduce computes a single value by applying the function to an accumulator and successive items. Unlike the other two, reduce is available in the functools module in 3.0, not the built-in scope.
Function annotations, available in 3.0 and later, are syntactic embellishments of a function’s arguments and result, which are collected into a dictionary assigned to the function’s __annotations__ attribute. Python places no semantic meaning on these annotations, but simply packages them for potential use by other tools.
Recursive functions call themselves either directly or indirectly in order to loop. They may be used to traverse arbitrarily shaped structures, but they can also be used for iteration in general (though the latter role is often more simply and efficiently coded with looping statements).
Functions should generally be small, as self-contained as possible, have a single unified purpose, and communicate with other components through input arguments and return values. They may use mutable arguments to communicate results too if changes are expected, and some types of programs imply other communication mechanisms.
Chapter 20. Iterations and Comprehensions, Part 2
This chapter continues the advanced function topics theme, with a reprisal of the comprehension and iteration concepts introduced in Chapter 14. Because list comprehensions are as much related to the prior chapter’s functional tools (e.g., map and filter) as they are to for loops, we’ll revisit them in this context here. We’ll also take a second look at iterators in order to study generator functions and their generator expression relatives—user-defined ways to produce results on demand.
Iteration in Python also encompasses user-defined classes, but we’ll defer that final part of this story until Part VI, when we study operator overloading. As this is the last pass we’ll make over built-in iteration tools, though, we will summarize the various tools we’ve met thus far, and time the relative performance of some of them. Finally, because this is the last chapter in the part of the book, we’ll close with the usual sets of “gotchas” and exercises to help you start coding the ideas you’ve read about.
List Comprehensions Revisited: Functional Tools
In the prior chapter, we studied functional programming tools like map and filter, which map operations over sequences and collect results. Because this is such a common task in Python coding, Python eventually sprouted a new expression—the list comprehension—that is even more flexible than the tools we just studied. In short, list comprehensions apply an arbitrary expression to items in an iterable, rather than applying a function. As such, they can be more general tools.
We met list comprehensions in Chapter 14, in conjunction with looping statements. Because