3 Facts of the Integer Caching in Python

Understand some confusing results made by Python

Python has a special feature called integer caching, or integer interning. Sometimes, it may make us confused. For example, if we run the following code on a Python shell, the results are surprising:

Why 256 is 256 but 257 is not 257? Anything wrong?

And this is not the weirdest part yet, if we put the same code into a file and run it as a Python script, the results will surprise us again!

We put all the code into the above script called “test.py”. Then open a terminal and run the script:

😱 What? The statement x is y becomes True at this time.

If you are confused about these as well, no worries at all. This article will demystify how the integer caching feature works and summary it into three facts. After reading, you will not be confused about it anymore.

Actually, in order to save time and memory costs, Python always pre-loads all the small integers in the range of [-5, 256]. When a new integer variable in this range is declared, Python just references the cached integer to it and won’t create any new object.

In other words, all the integers in [-5, 256] are singletons and they’ve been already saved in the memory. We cannot create a new integer object in this range.

Therefore, the explanations of the first example are obvious:

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