We explore how to speed up binary search by batching independent searches with NumPy’s vectorized operations. We then reformulate the algorithm so all searches progress together with only $O(1)$ additional memory, port it to C++, and achieve up to a 25× speedup over NumPy 2.4’s implementation.
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We explore how to speed up binary search by batching independent searches with NumPy’s vectorized operations. We then reformulate the algorithm so all searches progress together with only $O(1)$ additional memory, port it to C++, and achieve up to a 25× speedup over NumPy 2.4’s implementation.
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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:
mplhep is the matplotlib companion library for high-energy physics. It plots pre-binned histograms (and helped land plt.stairs upstream), builds ratio/pull comparison panels the way collider analyses expect them, and ships the official styles of ATLAS, CMS, LHCb, ALICE and DUNE.
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In this post, we explore the risks around and impact of AI contributions on our ecosystem, and how we, as a community, may agree upon common cultural norms and standards for integrating such technologies into contributor pathways and workflows.
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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.
Many real-world shortest path problems include constraints that classic algorithms don’t directly handle. NetworkX provides robust, optimized implementations of algorithms like Dijkstra’s, Bellman-Ford, and A*. But what if your problem doesn’t fit the classic shortest path formulation?
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This blog introduces PyTrees — nested Python data structures (such as lists, dicts, and tuples) with numerical leaf values — designed to simplify working with complex, hierarchically organized data.
While such structures are often cumbersome to manipulate, PyTrees make them more manageable by allowing them to be flattened into a list of leaves along with a reusable structure blueprint in a generic way.
This enables flexible, generic operations like mapping and reducing from functional programming.
By bringing those functional paradigms to structured data, PyTrees let you focus on what transformations to apply, not how to traverse the structure — no matter how deeply nested or complex it is.
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PyPalettes is a new Python library designed to simplify the use of color palettes in Python charts.
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