This interesting code snippet was brought to my attention by Finxter reader Albrecht.
a, b = 250, 250 for i in range(250, 260): if a is not b: break a += 1 b += 1 print(a) # What's the output of this code snippet?
You’d guess that the for loop goes from
i=259, each time incrementing
b. As Python creates one integer object to which both names refer, the command
a is not b should always be
False. Thus, the result is
Try it yourself in our interactive code shell:
Exercise: Run the code and check the result. Did you expect this?
The result is
If you create an integer object that falls into the range of -5 to 256, Python will only return a reference to this object — which is already cached in memory.
“The current implementation keeps an array of integer objects for all integers between -5 and 256, when you create an int in that range you actually just get back a reference to the existing object.”Python Docs
You can visualize the code execution in this interactive memory visualizer:
Exercise: Click next until you see the result. How many integers are in memory?
Let’s quickly examine the meaning of “is” in Python.
The is operator
The is operator checks if two variable names point to the same object in memory:
>>> a = "hello" >>> b = "hello" >>> a is b True
b point to the string
"hello". Python doesn’t store the same string twice but creates it only once in memory. This saves memory and makes Python faster and more efficient. And it’s not a problem because strings are immutable — so one variable cannot “overshadow” a string object of another variable.
Note that we can use the
id() function to check an integer representation of the memory address:
>>> a = "hello" >>> b = "hello" >>> id(a) 1505840752992 >>> id(b) 1505840752992
They both point to the same location in memory! Therefore, the
is operator returns
Small Integer Caching
Again, if you create an integer object that falls into the range of -5 to 256, Python will only return a reference to this object — which is already cached in memory. But if we create an integer object that does not fall into this range, Python may return a new integer object with the same value.
If we now check
a is not b, Python will give us the correct result
In fact, this leads to the strange behavior of the C implementation of Python 3:
>>> a = 256 >>> b = 256 >>> a is b True >>> a = 257 >>> b = 257 >>> a is b False
Therefore, you should always compare integers by using the
== operator in Python. This ensures that Python performs a semantic comparison, and not a mere memory address comparison:
>>> a = 256 >>> b = 256 >>> a == b True >>> a = 257 >>> b = 257 >>> a == b True
What can you learn from this? Implementation details matter!
Where to Go From Here?
Enough theory, let’s get some practice!
To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?
Practice projects is how you sharpen your saw in coding!
Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?
Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.
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