Python is a widely used high-level, general-purpose, interpreted, dynamic programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java. The language provides constructs intended to enable clear programs on both a small and large scale. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library. Python interpreters are available for installation on many operating systems, allowing Python code execution on a wide variety of systems. Using third-party tools, such as Py2exe or Pyinstaller, Python code can be packaged into stand-alone executable programs for some of the most popular operating systems, allowing the distribution of Python-based software for use on those environments without requiring the installation of a Python interpreter. (wikipedia)

References in zbMATH (referenced in 259 articles , 3 standard articles )

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  1. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  2. Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning: HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data (2017) arXiv
  3. Carmen Moret-Tatay, Daniel Gamermann, Esperanza Navarro-Pardo, Pedro Fernandez de Cordoba: ExGUtils: A python package for statistical analysis with the ex-gaussian probability density (2017) arXiv
  4. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  5. Gordon, Steven I.; Guilfoos, Brian: Introduction to modeling and simulation with MATLAB and Python (2017)
  6. Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)
  7. Herman, Russell L.: An introduction to Fourier analysis (2017)
  8. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  9. Julian Schneider, Jan Hamaekers, Samuel T. Chill, Soren Smidstrup, Johannes Bulin, Ralph Thesen, Anders Blom, Kurt Stokbro: ATK-Classical: A New Generation Molecular Dynamics Software Package (2017) arXiv
  10. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  11. Abali, B.Emek; Wu, Cheng-Chieh; Müller, Wolfgang H.: An energy-based method to determine material constants in nonlinear rheology with applications (2016)
  12. Andersen, Jakob L.; Flamm, Christoph; Merkle, Daniel; Stadler, Peter F.: A software package for chemically inspired graph transformation (2016)
  13. Anisimov, A.V.; Hodovaniuk, M.I.; Kuliabko, P.P.: Parallel programming in computer networks on the base of PARCS-technology (basic language is Python) (2016)
  14. Arai, Ryoya; Sato, Shigeyuki; Iwasaki, Hideya: A debugger-cooperative higher-order contract system in Python (2016)
  15. Bailes, Jeffrey Steven: Orbispaces, configurations and quasi-fibrations. (Abstract of thesis) (2016)
  16. Brzeziński, Dariusz W.; Ostalczyk, Piotr: Numerical calculations accuracy comparison of the inverse Laplace transform algorithms for solutions of fractional order differential equations (2016)
  17. Caprara, Alberto; Carvalho, Margarida; Lodi, Andrea; Woeginger, Gerhard J.: Bilevel knapsack with interdiction constraints (2016)
  18. Coelho, Rodrigo C.V.; Neumann, Rodrigo F.: Fluid dynamics in porous media with Sailfish (2016)
  19. Cooper, Christopher D.; Barba, Lorena A.: Poisson-Boltzmann model for protein-surface electrostatic interactions and grid-convergence study using the PyGBe code (2016)
  20. Davis, Jon H.: Methods of applied mathematics with a software overview (2016)

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