Python⇒Speed

Floating point math is often slower than integer math because the compiler is being conservative about how it optimizes your code.

How do you speed up computational Python code?

Imagine you are processing a stream of events, of unknown length.

In a previous post I talked about unit testing for speed, and in particular testing for big-O scalability.

When you’re building a Docker image for your Python application, you’re building on top of an existing image—and there are many possible choices for the resulting container.

If your NumPy code is too slow, what next?

When you implement an algorithm, you also implement tests to make sure the outputs are correct.

In a recent article by David Lattimore, he demonstrates a number of Rust performance tricks, including one that involve writing code that looks like a loop, but which in practice is optimized down to a fixed number of instructions.

Note: The original version of this article was slightly different, e.g. with 500x speedup; I reworked it to make the argument clearer.

You have a large JSON file, and you want to load the data into Pydantic.