"We share several other articles and projects from the Python community, including a collection of recent releases and PEPs, a profiler for targeting individual functions, a quiz to test your Django knowledge, when to use each of the eight versions of UUID, the hard-to-swallow truths about being a software engineer, an offline reverse geocoding library, and a library for auto-generating CLIs from any Python object."
"Christopher digs into an article about building tests to make sure your software is fast, or at least doesn't get slower as it scales. The piece focuses on testing Big-O scaling and its implications for algorithms. We also discuss another article covering the top features in pandas 3.0, including the new dedicated string dtype, a cleaner way to perform column-based operations, and more predictable default copying behavior with Copy-on-Write."
Create automated tests that assert expected algorithmic scaling and detect performance regressions as input sizes increase. Generate datasets of varying sizes, measure runtimes or operation counts, and compare growth against expected Big-O behavior rather than single-threshold timings. Integrate baseline measurements into CI and flag deviations that indicate degraded scaling. pandas 3.0 introduces a dedicated string dtype, clearer column-based operations, and more predictable default copying via Copy-on-Write. Additional tooling highlights include a targeting profiler for focusing optimization on individual functions, recent Python and Django releases, several PEPs, reverse geocoding tooling, and utilities for auto-generating CLIs and learning resources.
Read at Realpython
Unable to calculate read time
Collection
[
|
...
]