Learning Python - Mark Lutz [213]
Test Your Knowledge: Quiz
How are for loops and iterators related?
How are for loops and list comprehensions related?
Name four iteration contexts in the Python language.
What is the best way to read line by line from a text file today?
What sort of weapons would you expect to see employed by the Spanish Inquisition?
Test Your Knowledge: Answers
The for loop uses the iteration protocol to step through items in the object across which it is iterating. It calls the object’s __next__ method (run by the next built-in) on each iteration and catches the StopIteration exception to determine when to stop looping. Any object that supports this model works in a for loop and in other iteration contexts.
Both are iteration tools. List comprehensions are a concise and efficient way to perform a common for loop task: collecting the results of applying an expression to all items in an iterable object. It’s always possible to translate a list comprehension to a for loop, and part of the list comprehension expression looks like the header of a for loop syntactically.
Iteration contexts in Python include the for loop; list comprehensions; the map built-in function; the in membership test expression; and the built-in functions sorted, sum, any, and all. This category also includes the list and tuple built-ins, string join methods, and sequence assignments, all of which use the iteration protocol (the __next__ method) to step across iterable objects one item at a time.
The best way to read lines from a text file today is to not read it explicitly at all: instead, open the file within an iteration context such as a for loop or list comprehension, and let the iteration tool automatically scan one line at a time by running the file’s next method on each iteration. This approach is generally best in terms of coding simplicity, execution speed, and memory space requirements.
I’ll accept any of the following as correct answers: fear, intimidation, nice red uniforms, a comfy chair, and soft pillows.
Chapter 15. The Documentation Interlude
This part of the book concludes with a look at techniques and tools used for documenting Python code. Although Python code is designed to be readable, a few well-placed human-readable comments can do much to help others understand the workings of your programs. Python includes syntax and tools to make documentation easier.
Although this is something of a tools-related concept, the topic is presented here partly because it involves Python’s syntax model, and partly as a resource for readers struggling to understand Python’s toolset. For the latter purpose, I’ll expand here on documentation pointers first given in Chapter 4. As usual, in addition to the chapter quiz this concluding chapter ends with some warnings about common pitfalls and a set of exercises for this part of the text.
Python Documentation Sources
By this point in the book, you’re probably starting to realize that Python comes with an amazing amount of prebuilt functionality—built-in functions and exceptions, predefined object attributes and methods, standard library modules, and more. And we’ve really only scratched the surface of each of these categories.
One of the first questions that bewildered beginners often ask is: how do I find information on all the built-in tools? This section provides hints on the various documentation sources available in Python. It also presents documentation strings (docstrings) and the PyDoc system that makes use of them. These topics are somewhat peripheral to the core language itself, but they become essential