Value Generation in Built-in Types and Classes

Finally, although we’ve focused on coding value generators ourselves in this section, don’t forget that many built-in types behave in similar ways—as we saw in Chapter 14, for example, dictionaries have iterators that produce keys on each iteration:

>>> D = {'a':1, 'b':2, 'c':3}
>>> x = iter(D)
>>> next(x)
'a'
>>> next(x)
'c'

广告：个人专属 VPN，独立 IP，无限流量，多机房切换，还可以屏蔽广告和恶意软件，每月最低仅 5 美元

Like the values produced by handcoded generators, dictionary keys may be iterated over both manually and with automatic iteration tools including for loops, map calls, list comprehensions, and the many other contexts we met in Chapter 14:

>>> for key in D:
...     print(key, D[key])
...
a 1
c 3
b 2

As we’ve also seen, for file iterators, Python simply loads lines from the file on demand:

>>> for line in open('temp.txt'):
...     print(line, end='')
...
Tis but
a flesh wound.

While built-in type iterators are bound to a specific type of value generation, the concept is similar to generators we code with expressions and functions. Iteration contexts like for loops accept any iterable, whether user-defined or built-in.

Although beyond the scope of this chapter, it is also possible to implement arbitrary user-defined generator objects with classes that conform to the iteration protocol. Such classes define a special __iter__ method run by the iter built-in function that returns an object having a __next__ method run by the next built-in function (a __getitem__ indexing method is also available as a fallback option for iteration).

The instance objects created from such a class are considered iterable and may be used in for loops and all other iteration contexts. With classes, though, we have access to richer logic and data structuring options than other generator constructs can offer.

The iterator story won’t really be complete until we’ve seen how it maps to classes, too. For now, we’ll have to settle for postponing its conclusion until we study class-based iterators in Chapter 29.

 

[44] Interestingly, generator functions are also something of a “poor man’s” multithreading device—they interleave a function’s work with that of its caller, by dividing its operation into steps run between yields. Generators are not threads, though: the program is explicitly directed to and from the function within a single thread of control. In one sense, threading is more general (producers can run truly independently and post results to a queue), but generators may be simpler to code. See the second footnote in Chapter 17 for a brief introduction to Python multithreading tools. Note that because control is routed explicitly at yield and next calls, generators are also not backtracking, but are more strongly related to coroutines—formal concepts that are both beyond this chapter’s scope.