I did not start from np.searchsorted.

The idea came from a broader question I had been exploring in my own infrastructure work:

Has the system already paid for information that we are about to compute again?

In my CoreFoundry project, one recurring principle was simple: before adding more hardware or parallelism, first remove work that never needed to happen.

That same question eventually led me to np.searchsorted.

Reusing information that already exists#

np.searchsorted finds insertion positions in a sorted array.

Thousands of lines of hand-written CUDA C++, now Python. Less code, and it runs faster.

A single collision event in a particle detector holds a variable number of particles, each with a variable number of measurements:

[[1.1, 2.2, 3.3], [], [4.4, 5.5]]

Awkward Array is a Python library for manipulating nested, variable-length (“ragged”) data like this with NumPy-like idioms. It stores that data flat, as one content buffer holding every value contiguously plus an offsets array marking where each sublist begins and ends:

It’s been exactly one year since I started my journey as a NumPy Fellow, and looking back, it has honestly been the best job I’ve ever had. My main goal for 2025 was to push the boundaries of static typing within the Scientific Python ecosystem. I’m happy to report that we didn’t just push the boundaries; we reshaped them.

Here is a high-level look at what we achieved, from making numpy fully type-checked to bridging the gap between scientific computing and the wider Python typing community.