Scipy is a phenomenal library. It's not every day that one can beat its performance by 1-2 orders of magnitude. Today is that day, though!

Must-read post if you care about any of:
- high performance Rust
- numerical methods
- how Rust can make Python faster

Take a look under the hood of the state of the art in grid interpolation in Rust and Python!

Come for the compile-time loop unrolling, stay for the profile-guided optimization!

jlogan.dev/blog/2025/11...

Such a cool resource! Jupyter Everywhere is WebAssembly Jupyter environments with matplotlib, pandas, scikit learn and scipy. Born of a group trying to create environments for HS students to learn stats on Chromebooks, but incredibly widely useful! www.jupytereverywhere.org

Today, napari users can’t fully leverage Xarray’s labeled metadata, as slider names, units & dimensions often get out of sync. That’s changing. At SciPy 2025, napari, Xarray & CellProfiler devs began a collab to build true metadata-aware visualization across sciences.
earthmover.io/blog/scienti...

🚀 Exciting news! The SciPy 2025 Proceedings are officially published:
👉 proceedings.scipy.org/2025

Huge thanks to the Proceedings Committee, @curvenote.com, Jim Weiss, all the authors, and reviewers who made this happen. 🙌

🔥 Julia results on ODE solving benchmark tasks...

For non-stiff problems, Matlab is 3-10x faster than SciPy (Python), but Julia is 100x faster than Matlab

For stiff problems, Matlab and SciPy perform similarly to each other, but Julia is 100-1000x faster

From docs.sciml.ai/SciMLBenchma...

The video of my talk at SciPy on DataMapPlot is up at last. If you make t-SNE or UMAP plots the talk provides some guidance on how to make plots most effective, and introduces a library to help make that easier.

www.youtube.com/watch?v=-iBh...

Es gibt eine Doku über den Erfinder von NumPy und SciPy, die zwei Bibliotheken, die der Programmiersprache Python zum Durchbruch verhalfen. 🤓

Optuna GPSampler (ガウス過程ベースのベイズ最適化手法) の 2.5倍高速化 tech.preferred.jp/ja/blog/spee...

多点探索の複数の最適化処理での獲得関数の評価をバッチ化することで高速化するのですが、コルーチンライブラリ greenlet を使うことで、scipyの最適化関数を複数並行で実行しつつ、目的関数と勾配の評価用のコールバック関数の中で待ち合わせてバッチ評価する、という面白テクニックを使っています。

This post inspired by me just discovering that the Poisson binomial distribution is exactly what I need for this problem, and that it already exists in scipy and is easy & fast to use. Amazing

The creator of the NumPy and SciPy libraries reflects on their supporting role in the story of Python, now the subject of a documentary

go.nature.com/4gHDSGF

"NumPy has been a driving force for my whole life." It was a real pleasure to speak with @numpy.bsky.social developer Travis Oliphant about NumPy, SciPy, and #Python: the Documentary! New @nature.com 🧪🐍 www.nature.com/articles/d41...

It was a real pleasure to speak with @numpy.bsky.social creator Travis Oliphant about NumPy, SciPy, and #Python: the Documentary! New @nature.com 🧪🐍 www.nature.com/articles/d41...

Not sure who to notify @SciPy_team and @SciPyConf but the “Scipy Conference” link off the main <a href="http://scipy.org" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">http://scipy.org homepage points to the old 2023 page. (Well, it points to <a href="http://conference.scipy.org" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">http://conference.scipy.org which hasn’t been updated from the 2023 conference info)

💻 Yazılımda Modelleme
Python'da bir diferansiyel denklemi çözmek için scipy kütüphanesi kullanılır.

I mostly just use the one conda environment that activates by default, and rarely use much more than numpy + scipy + Pillow + matplotlib, and save virtualenvs for when I've got a thing that needs deploying, or for experimenting with a library I don't want to pollute my default env with..

SVD ♥️ ...

Usei pra calcular os eigen vector e eigen values e aplicar nesse projetinho aqui.

github.com/rdenadai/sen...

Por sorte, na época não precisei escrever a decomposição mão pq já tinha no scipy

- sundials의 cvode는 기존 odepack의 알고리즘을 계승, 개선한 버젼임. 아마 잘 모르면 제일 먼저 써볼만한 알고리즘인 듯. 퍼포먼스도 좋고 stiffness도 잘 커버 하는 것으로 보임
- 그런데 sundials를 scipy에 근 시일내에 포함시킬 계획이 없어보임. 따라서 scipy 이용자는 어쩔 수 없이 구시대 lsoda를 눈물을 머금고 써야 함 <-대체 왜?3
- 다른 scikit ode나 pysundials 옵션이 있긴 하지만 역시 불편함은 어쩔 수 없음

SciPy, maybe?

www.danielmsullivan.com/pages/tutori...

Authors published a paper in Biorxiv, they used SciPy in the study. Including #RRIDs will make this less ambiguous.

SciScore made a table with this resource, see “Automated Services” module (download as csv, xml or #jats) #STMpublishing #OpenScience

👕 #SciPy2025 T-Shirts Are Here! 👕

In the spirit of sustainability we're shaking things up. We’re not printing a free t-shirt for each attendee. Instead, you can opt in to buy one at cost. 💚

🎉 Buy yours by June 20
—now with a punny back, because we couldn’t resist. 👀

➡️ ti.to/scipy/scipy2...

Still riding the #SciPy2025 high ⚡

✅ Packaging workshop w/ #Hatch + #UV
💬 60+ at our BoF on sharing code
🚀 30+ PRs in one day—many from first-timers
🎙️ Talks, #Pixi demos, & a SciPy Song shoutout!

More: www.pyopensci.org/blog/pyopens...
Join us: pyopensci.org/events

#OpenScience #Python

look I love pytorch, it's a ridiculous feat of engineering. same deal with the people who've maintained scipy for a gazillion years

keep getting disappointed by how poor jax is on CPU. Same lbfgs optimization on a small dataset takes 0.009s in my numpy/scipy version and 13 seconds (1400x longer!!) in jax

all I wanted was to vmap some stuff but it turns out to be orders of magnitude faster to do so in a naive python for loop :/