{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"Computes numerical derivatives, gradients, Jacobians, Hessians, and higher-order partial derivatives of scalar and vector functions using adaptive finite differences and Richardson extrapolation.","skillfed_tags":["numerical-methods","optimization","scientific-computing"],"use_cases":["Compute gradients for optimization algorithms when analytical derivatives are unavailable or expensive to derive.","Calculate Jacobians for nonlinear least-squares fitting and parameter estimation problems.","Evaluate Hessian matrices for second-order optimization methods and sensitivity analysis.","Verify hand-coded derivatives by comparing them against numerical estimates.","Compute directional derivatives and higher-order partial derivatives for scientific analysis."],"what_it_does":"Numdifftools is a numerical differentiation library that computes derivatives of any order for scalar and vector functions without requiring symbolic expressions or hand-coded gradients. It uses adaptive finite differences coupled with Richardson extrapolation to achieve high accuracy, and lets you choose between complex-step, central, forward, or backward difference schemes. The library provides high-level interfaces for common tasks like computing Jacobians, Hessians, and directional derivatives, along with error estimates on all results.\n\nThe package is built on numpy and scipy and is designed for scientific computing workflows where you need to compute derivatives of functions that are only available as Python callables. It also provides an interface to AlgoPy for algorithmic differentiation when needed. It supports Python 3.10 and later and is actively maintained.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, installs cleanly with minimal dependencies, and solves a well-defined problem in numerical computing. It is a good fit if you need to compute derivatives without symbolic math or hand-coding gradients. The permissive BSD license poses no restrictions."},"id":"numdifftools","links":{"html":"https://skillfed.io/packages/numdifftools","md":"https://skillfed.io/packages/numdifftools.md","pypi":"https://pypi.org/project/numdifftools/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":null,"license_treatment":"permissive","name":"numdifftools","python_support":"supports_current","summary":"Solves automatic numerical differentiation problems in one or more variables."},"popularity":{"monthly_downloads":477954,"position":6440,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.10.1"}