{"name":"Numpyscipy","domain":"numpy.org","url":"https://optimly.ai/brand/numpyscipy","is_claimed":false,"categories":["developer-tools"],"headers":[{"header":"Overview","sections":[{"key":"short_description","label":"Tagline","shape":"text","content":"Fundamental package for scientific computing in Python, offering fast and versatile N-dimensional arrays with well-optimized C code for performance, an easy-to-use high-level syntax, and a vibrant, responsive, and diverse open-source community. It is interoperable with a wide range of hardware and computing platforms, playing well with distributed, GPU, and sparse array libraries.","source_type":"1p_website","source_label":"Brand's website","source_url":"https://numpy.org","updated_at":"2026-10-04T00:02:49.782000+00:00"},{"key":"description","label":"Description","shape":"text","content":"Numpyscipy (specifically NumPy based on the provided text) is the fundamental package for scientific computing with Python, offering powerful N-dimensional arrays, comprehensive mathematical functions, random number generators, linear algebra routines, and Fourier transforms. It serves as the core of a rich ecosystem of data science and machine learning libraries.","source_type":"1p_website","source_label":"Brand's website","source_url":"https://numpy.org","updated_at":"2026-10-04T00:02:49.782000+00:00"}]},{"header":"Offerings","sections":[{"key":"core_capabilities","label":"Core capabilities","shape":"text","content":"NumPy: Powerful N-dimensional arrays\ncomprehensive mathematical functions\nrandom number generators\nlinear algebra routines\nand Fourier transforms.","source_type":"1p_website","source_label":"Brand's website","source_url":"https://numpy.org","updated_at":"2026-10-04T00:02:49.782000+00:00"},{"key":"pricing","label":"Pricing","shape":"text","content":"Open source, distributed under a liberal BSD license (free).","source_type":"1p_website","source_label":"Brand's website","source_url":"https://numpy.org","updated_at":"2026-10-04T00:02:49.782000+00:00"}]},{"header":"Audience","sections":[{"key":"target_industry","label":"Target industry","shape":"text","content":"Scientists, data scientists, machine learning engineers, and developers working in Python across various scientific domains including quantum computing, statistical computing, signal processing, image processing, astronomy, bioinformatics, mathematical analysis, chemistry, geoscience, and architecture & engineering.","source_type":"1p_website","source_label":"Brand's website","source_url":"https://numpy.org","updated_at":"2026-10-04T00:02:49.782000+00:00"}]}],"last_refreshed_at":"2026-10-04T00:02:49.782000+00:00","last_changed_at":"2026-10-17T23:46:42.105081+00:00","claim_url":"https://app.optimly.ai/sign-up?claim=e7085495-f202-44e5-8605-503267c4109a"}