SciPy (pronounced ”Sigh Pie”) is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!

References in zbMATH (referenced in 390 articles )

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  1. Yihong Z. Mauro; Collin J. Wilkinson; John C. Mauro: KineticPy: A tool to calculate long-time kinetics in energy landscapes with broken ergodicity (2020) not zbMATH
  2. Agrell, Christian: Gaussian processes with linear operator inequality constraints (2019)
  3. Ahmed, Elyes; Fumagalli, Alessio; Budiša, Ana: A multiscale flux basis for mortar mixed discretizations of reduced Darcy-Forchheimer fracture models (2019)
  4. Albin, Nathan; Fernando, Nethali; Poggi-Corradini, Pietro: Modulus metrics on networks (2019)
  5. Andreas F. Haselsteiner; Jannik Lehmkuhl; Tobias Pape; Kai-Lukas Windmeier; Klaus-Dieter Thoben: ViroCon: A software to compute multivariate extremes using the environmental contour method (2019) not zbMATH
  6. Andrei V. Novikov: PyClustering: Data Mining Library (2019) not zbMATH
  7. Andrew Abi-Mansour: PyGran: An object-oriented library for DEM simulation and analysis (2019) not zbMATH
  8. Benjamin Bengfort; Rebecca Bilbro: Yellowbrick: Visualizing the Scikit-Learn Model Selection Process (2019) not zbMATH
  9. Benjamin W. L. Margolis, Kenneth R. Lyons: ndsplines: A Python Library for Tensor-Product B-Splines of Arbitrary Dimension (2019) not zbMATH
  10. B. Perret; G. Chierchia; J. Cousty; S. J. F. Guimaraes; Y. Kenmochi; L. Najman: Higra: Hierarchical Graph Analysis (2019) not zbMATH
  11. Brian C. Ferrari: AutoGAMESS: A Python package for automation of GAMESS(US) Raman calculations (2019) not zbMATH
  12. Budanur, Nazmi Burak; Fleury, Marc: State space geometry of the chaotic pilot-wave hydrodynamics (2019)
  13. Busseti, Enzo; Moursi, Walaa M.; Boyd, Stephen: Solution refinement at regular points of conic problems (2019)
  14. Campillo-Funollet, Eduard; Venkataraman, Chandrasekhar; Madzvamuse, Anotida: Bayesian parameter identification for Turing systems on stationary and evolving domains (2019)
  15. Carrillo, José Antonio; Craig, Katy; Patacchini, Francesco S.: A blob method for diffusion (2019)
  16. Chua, Lynn; Kummer, Mario; Sturmfels, Bernd: Schottky algorithms: classical meets tropical (2019)
  17. Cimrman, Robert; Lukeš, Vladimír; Rohan, Eduard: Multiscale finite element calculations in Python using sfepy (2019)
  18. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
  19. Climaco, Joyce S.; Saa, Alberto: Optimal global synchronization of partially forced Kuramoto oscillators (2019)
  20. Cole, S., Donoghue, T., Gao, R., Voytek, B.: NeuroDSP: A package for neural digital signal processing (2019) not zbMATH

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