This chapter is from the book
This chapter is from the book
This chapter is from the book
Using functions to organize code
Beyond evaluating mathematical functions, the process of calculating an output value as a function of input values is important as a general technique for organizing control flow in any computation. Doing so is a simple example of an extremely important principle that is a prime guiding force for any good programmer: Whenever you can clearly separate tasks within a computation, you should do so.
Functions are natural and universal mechanism for expressing computational tasks. Indeed, the “bird’s-eye view” of a Python program that we began with in SECTION 1.1 was equivalent to a function: we began by thinking of a Python program as a function that transforms command-line arguments into an output string. This view expresses itself at many different levels of computation. In particular, it is generally the case that you can express a long program more naturally in terms of functions instead of as a sequence of Python assignment, conditional, and loop statements. With the ability to define functions, you can better organize your programs by defining functions within them when appropriate.
For example, coupon.py (PROGRAM 2.1.3) on the facing page is an improved version of couponcollector.py (PROGRAM 1.4.2) that better separates the individual components of the computation. If you study PROGRAM 1.4.2, you will identify three separate tasks:
- Given the number of coupon values n, compute a random coupon value.
- Given n, do the coupon collection experiment.
- Get n from the command line, then compute and write the result.
PROGRAM 2.1.3 rearranges the code to reflect the reality that these three activities underlie the computation. The first two are implemented as functions, the third as global code.
With this organization, we could change getCoupon() (for example, we might want to draw the random numbers from a different distribution) or the global code (for example, we might want to take multiple inputs or run multiple experiments) without worrying about the effect of any of these changes on collect().
Using functions isolates the implementation of each component of the collection experiment from others, or encapsulates them. Typically, programs have many independent components, which magnifies the benefits of separating them into different functions. We will discuss these benefits in further detail after we have seen several other examples, but you certainly can appreciate that it is better to express a computation in a program by breaking it up into functions, just as it is better to express an idea in an essay by breaking it up into paragraphs. Whenever you can clearly separate tasks within a computation, you should do so.
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