SciPy vs Dask: What are the differences?
Developers describe SciPy as "Scientific Computing Tools for Python". Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. On the other hand, Dask is detailed as "A flexible library for parallel computing in Python". It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers. .
SciPy and Dask belong to "Data Science Tools" category of the tech stack.
SciPy is an open source tool with 6.18K GitHub stars and 2.91K GitHub forks. Here's a link to SciPy's open source repository on GitHub.