For anyone wondering what exactly the assignment semantics are, see: https://nedbatchelder.com/text/names1.html and https://www.youtube.com/watch?v=_AEJHKGk9ns
Is that really what's going on here? I'm in way too deep to be sure what's best for beginner programmers, but I feel like Python must surely optimise...
sheep = 1
goats = sheep
sheep = sheep + 10
Now, if we imagine those numbers are way bigger (say 200 digits), Python still just works, whereas the low level languages I spend most time with will object because 200 digit integers don't fit in a machine word. You could imagine that copying isn't cheaper in that case, but I don't think I buy it. The 200 digit integer is a relatively small data structure, still probably cheaper to copy it than mess about with small objects which need garbage collecting.
I don't think it does that. You can try it yourself:
>>> x = 123456
>>> y = x
>>> id(x)
139871830996560
>>> id(y)
139871830996560For these particular numbers, CPython has one optimization I know of: Small integers (from -5 to 256) are pre-initialized and shared.
See https://docs.python.org/3/c-api/long.html#c.PyLong_FromLong
This is generally invisible to the python programmer, but leads to https://wsvincent.com/python-wat-integer-cache/
On my system these "cached" integers seem to each be 32 byte objects, so, over 8kB of RAM is used by CPython to "cache" the integers -5 through 256 in this way.
There's similar craziness over in String town. If I mint the exact same string a dozen times from a constant, those all have the same id, presumably Python has a similar "cache" of such constant strings. But if I assemble the same result string with concatenation, each of the identical strings has a different id (this is in 3.9)
So, my model of what's going on in a Python program was completely wrong. But the simple pedagogic model in the article was also wrong, just not in a way that's going to trip up new Python programmers.
Not really. You're hitting constant folding: https://arpitbhayani.me/blogs/constant-folding-python
This isn't a pre-made list of certain strings that should be cached, this is the compiler noticing that you mentioned the same constant a bunch of times.
Also in general you would see a lot of things with the same id because python uses references all over the place. E.g. assignment never copies.
The behavior that results from your example follows from this rule. First, evaluate `1` and call it `sheep`. Then evaluate whatever `sheep` is, once, to get `1` (the same object in memory as every other literal `1` in Python) and call it `goats`.
The last line is where the rule matters: The statement `sheep = sheep + 10` can be read as, “Evaluate `sheep + 10` and call the result `sheep`.” The statement reassigns the name `sheep` in the local namespace to point to a different object, one created by evaluating `sheep + 10`. The actual memory location that `sheep` referred to previously (containing the `int` object `1`) is not changed at all — assignment to a local will never change the value of any other local.
This is easy to remember if you recall that a local namespace is effectively just a `dict`. Your example is equivalent to:
d = {}
d["sheep"] = 1
d["goats"] = d["sheep"]
d["sheep"] = d["sheep"] + 10