While you can optimize the heck out of your Python code with
generator expressions I'm more interested in goofing
around and solving classic programming questions with the
note: For this article, since it's easier to explain things as they happen, I'll be including a lot of inline comments.
Let's start with a simple function that returns a sequence of some of my favorite values:
# yielding.py def pydanny_selected_numbers(): # If you multiple 9 by any other number you can easily play with # numbers to get back to 9. # Ex: 2 * 9 = 18. 1 + 8 = 9 # Ex: 15 * 9 = 135. 1 + 3 + 5 = 9 # See https://en.wikipedia.org/wiki/Digital_root yield 9 # A pretty prime. yield 31 # What's 6 * 7? yield 42 # The string representation of my first date with Audrey Roy yield "2010/02/20"
note: When a function uses the
yield keyword it's now called a
Let's do a test drive in the REPL:
>>> from yielding import pydanny_selected_numbers # import ye aulde code >>> pydanny_selected_numbers() # create the iterator object <generator object pydanny_selected_numbers at 0x1038a03c0> >>> for i in pydanny_selected_numbers(): # iterate through the iterator ... print(i) ... 9 31 42 "2010/02/20" >>> iterator = pydanny_selected_numbers() # create the iterator object >>> for i in iterator: # iterate through the iterator object ... print(i) ... 9 31 42 "2010/02/20"
Of course, if you know anything about generator expressions, you know I could do this more tersely with the following:
>>> iterator = (x for x in [9, 31, 42, "2010/02/20"]) >>> for i in iterator: ... print(i) ... 9 31 42 "2010/02/20"
While that is more terse, it doesn't give us the amount of control we get by defining our own generator function. For example, what if I want to present the Fibonacci sequence in a loop rather than with recursion?
# fun.py def fibonacci(max): result = 0 base = 1 while result <= max: # This yield statement is where the execution leaves the function. yield result # This is where the execution comes back into the function. This is # just whitespace, but that it came back while preserving the state # of the function is pretty awesome. # Fibonacci code to increase the number according to # https://en.wikipedia.org/wiki/Fibonacci_number n = result + base result = base base = n if __name__ == "__main__": for x in fibonacci(144): print(x)
Let's try this out in the REPL:
>>> from fun import fibonacci >>> fibonacci(10) <generator object fibonacci at 0x10d49e460> >>> for x in fibonacci(10): ... print(x) 0 1 1 2 3 5 8 >>> iterator = fibonacci(5) >>> iterator <generator object fibonacci at 0x10d63c550> >>> iterator.next() 0 >>> iterator.next() 1
What's nice about this is so much more than fibonacci logic in a
generator function. Instead, imagine instead of a lightweight
calculation I had done something performance intensive. By using
generator expressions I can readily control the execution calls with the
next() method, saving processor cycles.
I admit it. Like many Python developers, I find using tools like yields and generators to optimize the heck out of performance intensive things a lot of fun.
If you are like me and like this sort of stuff, I recommend the following resources:
In the next article I'll demonstrate how to use the
to create context managers.
Update: Nicholas Tollervey pointed me at wikipedia's Digital root article, so I added it to the comments of the first code sample.
Update: Oddthinking pointed out that I forgot a print statement. In the REPL it's not really needed, but if this is translated to a script then it's necessary.
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