This chapter introduces a collection of more advanced function-related topics: the
lambda expression, functional programming tools such as
map and list comprehensions, generator functions and expressions, and more. Part of the art of using functions lies in the interfaces between them, so we will also explore some general function design principles here. Because this is the last chapter in Part IV, we’ll close with the usual sets of gotchas and exercises to help you start coding the ideas you’ve read about.
You’ve seen what it takes to write your own basic functions in Python. The next sections deal with a few more advanced function-related ideas. Most of these are optional features, but they can simplify your coding tasks when used well.
def statement, Python also provides an expression form that generates function objects. Because of its similarity to a tool in the LISP language, it’s called
def, this expression creates a function to be called later, but it returns the function instead of assigning it to a name. This is why
lambdas are sometimes known as anonymous (i.e., unnamed) functions. In practice, they are often used as a way to inline a function definition, or to defer execution of a piece of code.
lambda’s general form is the keyword
lambda, followed by one or more arguments (exactly like the arguments list you enclose in parentheses in a
def header), followed ...