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Potential solutions for pain points when dealing with native code; what needs unifying in the Python packaging space, and how should that be approached? 

Python packaging & workflows - where to next?

Blogpost of working on the PyTorch-Ignite project during internship at Quansight

Sangho's Internship at Quansight with PyTorch-Ignite project

Surbhi Sharma shares her exciting experience working as an intern at Quansight Labs and contributing to condacolab, a tool that lets you deploy a Miniconda installation easily on Google Colab notebooks. This enables you to use conda or mamba to install new packages on any Colab session.

Conda on Colaboratory

Kulsoom Zahra learns about accessibility and fixes a part of the JupyterLab interface (that used to break when zoomed in) during her summer 2022 internship at Quansight Labs.

Zoom zoom zoom! Improving Accessibility in JupyterLab

accessible-pygments hosts curated WCAG-compliant themes for all your syntax highlighting needs.

Making pygments accessible

In this blogpost, I share my experience as a Google Season of Docs 2022 technical writer working on updating the Editor user documentation.

The new Spyder Editor documentation under the spotlights!

Learning from awesome mentors and contributing to pandas open source

Close Encounter with pandas and the Jedis of open source

We are delighted to share details about new grants to support the sustainability of SciPy, conda-forge, and CuPy

Quansight Labs awarded three CZI EOSS Cycle 5 Grants

The Nebari CLI consists of various commands the user needs to run to initialize, deploy, configure, and update Nebari.

Developing a Typer CLI for Nebari

Quansight Labs is delighted to welcome its second cohort of 6 interns, who will work on a variety of open source projects and tasks

Introducing the 2022 Interns Cohort

Announcing the SciPy 2022 Accessibility Awareness Efforts

SciPy 2022 Accessibility Awareness Programs

A non-exhaustive but totally honest checklist for accessibility review

Checking for accessibility: thoughts and a checklist!

The development story of a developer command-line interface (CLI) for the SciPy project, with exmaples

The evolution of the SciPy developer CLI

In our weekly show and tell we got real about "why can writing blog  posts be so hard?" and collaboratively wrote up this blog post about what we learned  from the discussion.

Why is writing blog posts hard?

How we can use the Python Array API Standard with the fundamental libraries in the PyData ecosystem along with CuPy for making GPUs accessible to the users of these libraries

Making GPUs accessible to the PyData Ecosystem via the Array API Standard.

The Chan Zuckerberg Initiative has funded efforts to make the Jupyter ecosystem, starting with JupyterLab, more accessible. As a part of these increased efforts, the team will be providing a periodically updated list of what is currently being worked on and what is coming soon.

Jupyter accessibility efforts have a roadmap!

Grayskull is an automatic conda recipe generator, with a focus on conda-forge.

Conda and Grayskull, the Masters of Software Packaging

This is a companion post from the Official release of IPython 8.0. We hope it will help you apply best practices, and have an easier time maintaining your projects, or helping other.

IPython 8.0, Lessons learned maintaining software

A lot of us showed up for the code, but hung around for the community. We'll continue this post talking about the monthly Jupyter community calls, and how they help all jovyans, Project Jupyter's pet name for their developers and users, stay connected.

A year of Jupyter community calls

In this post, we aim to articulate that vision and suggest a path to making it concrete, focusing on three libraries at the core of the PyData ecosystem: SciPy, scikit-learn and scikit-image. 

A vision for extensibility to GPU & distributed support for SciPy, scikit-learn, scikit-image and beyond

My work was majorly focused on providing performance benchmarks to NumPy in realistic situations. The target was to show the world that NumPy is efficient in handling quasi real-life situations too.

NumPy Benchmarking

In the next lines, I"ll try to capture my experience at Quansight Labs as an intern working on the cuDF implementation of the dataframe interchange protocol. cuDF is a dataframe library very much like pandas which operates on the GPU in order to benefit from its computing power.

Dataframe interchange protocol: cuDF implementation

An exploration of a method to call C++ libarary function from Numba compiled functions, i.e. python function that are decorated with "numba.jit(nopython=True)"

An efficient method of calling C++ functions from numba using clang++/ctypes/rbc

The work I briefly describe in this blog post is the implementation of the dataframe interchange protocol into Vaex which I was working on through the three month period as a Quansight Labs Intern.

Dataframe interchange protocol and Vaex

We are thrilled to announce that the team at Quansight Labs has been awarded five EOSS Cycle 4 grants to work on several projects within the PyData ecosystem. This post will introduce the successful grantees and their objectives for these two-year long grants.

CZI EOSS4 Grants at Quansight Labs

Instead of going through all the complications involved in renting or acquiring dedicated hardware, setting up credentials and monitoring costs of a benchmarkin suite, we hoped we could use the same free cloud resources normally used for CI tests. Ideally, GitHub Actions.

Is GitHub Actions suitable for running benchmarks?

SciPy now builds with Meson on Linux, and the full test suite passes! This is a pretty exciting milestone, and good news for SciPy maintainers and contributors - they can look forward to much faster builds and a more pleasant development experience.

Moving SciPy to the Meson build system

In this blogpost we present pyflyby, a project and an extension to IPython and JupyterLab, that, among many things, automatically inserts imports and tidies Python files and notebooks.

Pyflyby: Improving Efficiency of Jupyter Interactive Sessions

Today I want to look into a topic that has not evolved much since, and I believe could use an upgrade. Accessing interactive Documentation when in a Jupyter session, and what it could become. At the end I"ll link to my current prototype if you are adventurous.

Rethinking Jupyter Interactive Documentation

Starting from Numba 0.53, Numba will ship with an enhanced version of the @guvectorize decorator. Similar to the @vectorize decorator, @guvectorize now has two modes of operation: Eager, or decoration-time compilation and Lazy, or call-time compilation

Enhancements to Numba's guvectorize decorator

In this blog post we will focus specifically on recent improvements that have been made to SciPy's interpolation functions. A recent NumFOCUS small development grant awarded to the SciPy developers allowed a dedicated effort to fix existing bugs in boundary handling and improve the documentation of the behavior of these modes.

Making SciPy's Image Interpolation Consistent and Well Documented

In the following blog post, we will show you step-by-step how you can develop a custom theme for JupyterLab, distribute it, and take the example of the jupyterlab-theme-winter theme we release today to celebrate the end of 2020.

Develop a JupyterLab Winter Theme

This new grant totaling $140,000 will fund part of our efforts to improve usability and sustainability in both projects and is excellent news for the scientific computing community, which will certainly benefit from this work downstream.

A second CZI grant for NumPy and OpenBLAS

This blog post is a conversation between two contributors, sharing their observations, experiences, and their hope for a stronger collaboration between design and development in open source.

Introduction to Design in Open Source

We'll highlight the ability of Ibis to generically prescribe query expressions across different data storage systems.

Querying multiple backends with Ibis

The basic installation format for users who install packages via pip is the wheel format. But what about Linux? The first manylinux standard, manylinux1, was based on CentOS5 which has been obsolete since March 2017. So what is next for manylinux, and what manylinux should users and package maintainers use?

Manylinux1 is obsolete, manylinux2010 is almost EOL, what is next?

You have probably seen Traitlets in applications, you likely even use it. The package has nearly 5 million downloads on conda-forge alone. But, what is Traitlets? In this post we'll answer this question along with where Traitlets came from, its applications, and a bit of history.

Traitlets - an introduction & use in Jupyter configuration management

Starting with IPython 7.16.1 (released in June 2020), you should be able to recreate the sdist (.tar.gz) and wheel (.whl), and get byte for byte identical result to the wheels published on PyPI. This is a critical step toward being able to trust your computing platforms, and a key component to improve efficiency of build and packaging platforms. It also potentially impacts fast conda environment creation for users. The following goes into some reasons for why you should care.

IPython reproducible builds

Open source is notorious for lack of design presence, enough so that my search to prove this fact has turned up nearly nothing. Once I learned I shouldn’t expect that everyone on my team knows what they want from the design process, here’s what helped me move forward and collaborate with developers successfully.

Designing with and for developers

I recently moved, and my new place has a relatively small footprint. (Yes, I moved during the COVID-19 pandemic. And yes, it was crazy.) I quickly realized that was going to need a wireless router of some sort, or more formally, a wireless access point (WAP).

Learn NixOS by turning a Raspberry Pi into a Wireless Router

A demonstration of a method to create a copy an exact dev environment on
one machine, and transfering it to a another machine.


Creating a Portable Python Environment from Imports

This post is a deep dive into how TensorIterator works, and is an essential part of learning to contribute to the PyTorch codebase since iterations over tensors in the C++ codebase are extremely commonplace. This post is aimed at someone who wants to contribute to PyTorch

PyTorch TensorIterator Internals

Having official high-level documentation written using up-to-date content and techniques will certainly mean more users (and developers/contributors) are involved in the NumPy community. So, if everybody agrees on its importance, why is it so hard to write good documentation?

Documentation as a way to build Community

We're quite excited by the number of students who have shown an interest in participating, and we look forward to seeing excellent applications! What's more exciting, though, are some of the first contributions from people not currently at Quansight, in the true spirit of open-source software!

uarray: GSoC Participation

I'm very pleased to announce that NumPy and OpenBLAS just received a $195,000 grant from the Chan Zuckerberg Initiative, through its Essential Open Source Software for Science (EOSS) program!

A new grant for NumPy and OpenBLAS!

Time flies when you're having fun. Here is an update of some of the highlights of my second month at Quansight Labs.

Labs update and May highlights

It has been an exciting first month for me at Quansight Labs. It's a good time for a summary of what we worked on in April and what is coming next.