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(Alex Zinovev) Moving computationally intensive scientific algorithms from desktop applications to cloud-based services presents ...

Speaker - Michael Zargham SciPy 2026 - www.scipy2026.scipy.org/ Graduate textbooks in applied mathematics are notoriously ...

[DESCRIPTION]: In Part 4.1, we take a step back and look at where Genome Toolkit is going next. We introduce refactoring ...

Title: (Re)-connecting foundational libraries with their communities: Successes, failures, and surprises in building the napari ...

EuroPython 2026 - S3A on 2026-07-16] *An Introduction to Writing Fast GPU Code in Python by Abhik Sarkar* ...

A session featuring updates and roadmaps from maintainers of core Scientific Python libraries and tools.

Many scientific workflows rely on derivatives of complex models: Fisher forecasts, sensitivity analysis, gradient-based inference, ...

Speaker: Bhupendra Raut SciPy 2026 - www.scipy2026.scipy.org/ _Adapt v0.1_ is a real-time, reproducible data-analysis ...

Synthetic Aperture Radar (SAR) is transforming how we observe our planet. It sees through clouds, smoke, and darkness, ...

scipy.stats.ttest_ind: two-sample t-test without Excel equal_var=False is Welch. p-value is not effect size. scipy.stats.ttest_ind ...

After 3 years, Pixi is widely adopted in the scientific Python ecosystem. At SciPy 2026, we want to show why. Scientific Python has ...

In 2004, Matthew Brett asked me a provocative question born of frustration with existing fMRI tools: "Why don't we rewrite them in ...

scipy.fft.fft: discrete Fourier transform Complex output. fftfreq labels bins. rfft for real input. scipy.fft.fft — one API, about 75 seconds.

scipy.stats.spearmanr: rank correlation when shapes are weird Monotonic, not linear. Ties matter. Still not causation.

scipy.linalg.lstsq: least squares when A is rectangular Under/over-determined systems. Residuals tell you fit quality.

scipy.optimize.minimize: find a minimum with a method you choose Pass fun, x0, method=. jac= helps. Check success before ...

scipy.signal.find_peaks: pick peaks with prominence height, distance, prominence beat a raw argmax loop.

scipy.interpolate.make_interp_spline: modern 1-D splines Returns a callable BSpline. k=3 is cubic. Extrapolation is a choice.

scipy.spatial.distance.cdist: pairwise distances between two sets metric="euclidean" default. Don't reinvent broadcasting loops.

scipy.interpolate.interp1d: fill between samples (and why it's legacy) kind="linear" is safe. Prefer make_interp_spline for new code.