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=250 to i=259, each time incrementing a and 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 a=259, right?
WRONG!!! $%&&%$
Try it yourself in our interactive code shell:
Exercise: Run the code and check the result. Did you expect this?
The result is a=257.
The reason is an implementation detail of the CPython implementation called “Small Integer Caching” — the internal cache of integers in Python.
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
Both variables a and 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 True!
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 True.
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!
Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.
To become more successful in coding, solve more real problems for real people. 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?
You build high-value coding skills by working on practical coding projects!
Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?
🚀 If your answer is YES!, consider becoming 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.
If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.