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to see code like for k in D.keys().sort():. This almost works—the keys method builds a keys list, and the sort method orders it—but because the sort method returns None, the loop fails because it is ultimately a loop over None (a nonsequence). This fails even sooner in Python 3.0, because dictionary keys are views, not lists! To code this correctly, either use the newer sorted built-in function, which returns the sorted list, or split the method calls out to statements: Ks = list(D.keys()), then Ks.sort(), and finally, for k in Ks:. This, by the way, is one case where you’ll still want to call the keys method explicitly for looping, instead of relying on the dictionary iterators—iterators do not sort.

Always use parentheses to call a function. You must add parentheses after a function name to call it, whether it takes arguments or not (e.g., use function(), not function). In Part IV, we’ll see that functions are simply objects that have a special operation—a call that you trigger with the parentheses.

In classes, this problem seems to occur most often with files; it’s common to see beginners type file.close to close a file, rather than file.close(). Because it’s legal to reference a function without calling it, the first version with no parentheses succeeds silently, but it does not close the file!

Don’t use extensions or paths in imports and reloads. Omit directory paths and file suffixes in import statements (e.g., say import mod, not import mod.py). (We discussed module basics in Chapter 3 and will continue studying modules in Part V.) Because modules may have other suffixes besides .py (.pyc, for instance), hardcoding a particular suffix is not only illegal syntax, but doesn’t make sense. Any platform-specific directory path syntax comes from module search path settings, not the import statement.

Chapter Summary

This chapter took us on a tour of program documentation—both documentation we write ourselves for our own programs, and documentation available for built-in tools. We met docstrings, explored the online and manual resources for Python reference, and learned how PyDoc’s help function and web page interface provide extra sources of documentation. Because this is the last chapter in this part of the book, we also reviewed common coding mistakes to help you avoid them.

In the next part of this book, we’ll start applying what we already know to larger program constructs: functions. Before moving on, however, be sure to work through the set of lab exercises for this part of the book that appear at the end of this chapter. And even before that, let’s run through this chapter’s quiz.

Test Your Knowledge: Quiz

When should you use documentation strings instead of hash-mark comments?

Name three ways you can view documentation strings.

How can you obtain a list of the available attributes in an object?

How can you get a list of all available modules on your computer?

Which Python book should you purchase after this one?

Test Your Knowledge: Answers

Documentation strings (docstrings) are considered best for larger, functional documentation, describing the use of modules, functions, classes, and methods in your code. Hash-mark comments are today best limited to micro-documentation about arcane expressions or statements. This is partly because docstrings are easier to find in a source file, but also because they can be extracted and displayed by the PyDoc system.

You can see docstrings by printing an object’s __doc__ attribute, by passing it to PyDoc’s help function, and by selecting modules in PyDoc’s GUI search engine in client/server mode. Additionally, PyDoc can be run to save a module’s documentation in an HTML file for later viewing or printing.

The built-in dir(X) function returns a list of all the attributes attached to any object.

Run the PyDoc GUI interface, leave the module name blank, and select “open browser”; this opens a web page containing a link to every module available to your programs.

Mine, of course. (Seriously, the Preface lists a few recommended follow-up books,