Powerful Python Numbers Every Programmer Should Know

There are numbers every Python programmer should know. For example, how fast or slow is it to add an item to a list in Python? What about opening a file? Is that less than a millisecond? Is there something that makes that slower than you might have guessed? If you have a performance sensitive algorithm, which data structure should you use? How much memory does a floating point number use? What about a single character or the empty string? How fast is FastAPI compared to Django?

I wanted to take a moment and write down performance numbers specifically focused on Python developers. Below you will find an extensive table of such values. They are grouped by category. And I provided a couple of graphs for the more significant analysis below the table.

This article is posted without any code. I encourage you to dig into the benchmarks. The code is available on GitHub at:

https://github.com/mikeckennedy/python-numbers-everyone-should-know

The benchmarks were run on the sytem described in this table. While yours may be faster or slower, the most important thing to consider is relative comparisons.

More analysis and graphs by category below the table.

Understanding how much memory different Python objects consume.

The rule of thumb for strings is the core string object takes 41 bytes. Each additional character is 1 byte.

Numbers are surprisingly large in Python. They have to derive from CPython’s PyObject and are subject to reference counting for garabage collection, they exceed our typical mental model many of:

Collections are amazing in Python. Dynamically growing lists. Ultra high-perf dictionaries and sets. Here is the empty and “full” overhead of each.

Source: HackerNews