Back to the basics: the Matplotlib histogram#

We are all excited about exploring and developing new fancy plotting tools, but I want to take some time to revisit one of the basic plotting functions: Matplotlib’s histogram hist(), which relies on the NumPy histogram() function. Histograms are convenient to quickly inspect a dataset and get a feeling for the distribution of values that they contain.

I am often not satisfied with the default histograms produced by Matplotlib, requiring me to do repetitive manual adjustments. Here I will show how the histograms can be enhanced while avoiding time-consuming manual tweaking. I hope that these suggestions will be useful to others and ultimately that these will be adopted as the default plotting style for histograms in newer versions of Matplotlib.

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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? Read more...

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. Read more...

PyPalettes is a new Python library designed to simplify the use of color palettes in Python charts. Read more...

The NumPy team is excited to announce the appointment of Joren Hammudoglu (@jorenham) as the second NumPy Developer in Residence. For the second time, the project is in a position to use its project funds to pay for a full year of maintainer time through the NumPy Fellowship Program.

Joren has been the driving force behind the improvements in NumPy’s support for static typing since he started contributing in mid-2024. He has authored a lot of the improvements — from the annotations themselves to CI support and working towards fundamental design improvements like ndarray shape typing — and helps guide and integrate the work of other NumPy contributors in this area, and engages with upstream projects like MyPy and Pyright and the typing standards/PEP process to help move static typing support for the ecosystem as a whole forward. He also contributes widely to static typing support in the ecosystem, as the author of scipy-stubs, numtype and more.

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The 2024 Scientific Python Developer summit was held 3–5 June in Seattle. Here’s a summary of what we did. Read more...

TL;DR: If you have GPU code in your project, setup a GitHub hosted GPU runner today. It is fairly quick to do and will free you from having to run tests manually.

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We are happy to announce that we have organized the necessary infrastructure and processes to allow volunteers to start translating multiple project websites. In this blog post, we will discuss how we set up manage translations for Scientific Python projects, and how you can participate in the translation and localization effort. Read more...