Python

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 726 articles , 3 standard articles )

Showing results 1 to 20 of 726.
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  1. A. Kapanowski, A. Krawczyk: Halin graphs are 3-vertex-colorable except even wheels (2019) arXiv
  2. 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
  3. Andreas Nüßing, Maria Carla Piastra, Sophie Schrader, Tuuli Miinalainen, Heinrich Brinck, Carsten H. Wolters, Christian Engwer: duneuro - A software toolbox for forward modeling in neuroscience (2019) arXiv
  4. Andrew Abi-Mansour: PyGran: An object-oriented library for DEM simulation and analysis (2019) not zbMATH
  5. Barbara De Palma, Marco Erba, Luca Mantovani, Nicola Mosco: A Python program for the implementation of the GAMMA-method for Monte Carlo simulations (2019) not zbMATH
  6. Benítez-Peña, S.; Blanquero, R.; Carrizosa, E.; Ramírez-Cobo, P.: Cost-sensitive feature selection for support vector machines (2019)
  7. Birkandan, Tolga; Güzelgün, Ceren; Şirin, Elif; Uslu, Mustafa Can: Symbolic and numerical analysis in general relativity with open source computer algebra systems (2019)
  8. Christine Harvey; R. S. Weigel: Transplant2Mongo: Python Scripts that Insert Organ Procurement and Transplantation Network (OPTN) Data in MongoDB (2019) not zbMATH
  9. Cotter, Colin; Crisan, Dan; Holm, Darryl D.; Pan, Wei; Shevchenko, Igor: Numerically modeling stochastic Lie transport in fluid dynamics (2019)
  10. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  11. Fitzpatrick, Richard: Oscillations and waves. An introduction (2019)
  12. Hadrien Lorenzo, Jérôme Saracco, Rodolphe Thiébaut: Supervised Learning for Multi-Block Incomplete Data (2019) arXiv
  13. Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Joshua T. Vogelstein: GraSPy: Graph Statistics in Python (2019) arXiv
  14. J. A. Melendez, R. J. Furnstahl, D. R. Phillips, M. T. Pratola, S. Wesolowski: Quantifying Correlated Truncation Errors in Effective Field Theory (2019) arXiv
  15. Joshua S Speagle: dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences (2019) arXiv
  16. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  17. Larose, Chantal D.; Larose, Daniel T.: Data science using Python and R (2019)
  18. Lorenzo Mentaschi; Michalis; Vousdoukas; Giovanni Besioc; Luc Feyen: alphaBetaLab: Automatic estimation of subscale transparencies for the Unresolved Obstacles Source Term in ocean wave modelling (2019) not zbMATH
  19. Maury, Bertrand; Faure, Sylvain: Crowds in equations. An introduction to the microscopic modeling of crowds (2019)
  20. Michael Slugocki , Allison B. Sekuler, Patrick Bennett: BayesFit: A tool for modeling psychophysical data using Bayesian inference (2019) not zbMATH

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