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 1229 articles , 3 standard articles )

Showing results 1 to 20 of 1229.
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  1. Adam Pluta; Ontje Lunsdorf: esy-osmfilter - A Python Library to Efficiently Extract OpenStreetMap Data (2020) not zbMATH
  2. Albert Steppi; Benjamin M. Gyori; John A. Bachman: Adeft: Acromine-based Disambiguation of Entities from Text with applications to the biomedical literature (2020) not zbMATH
  3. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  4. Andrew R. Bennett; Joseph J. Hamman; Bart Nijssen: MetSim: A Python package for estimation and disaggregation of meteorological data (2020) not zbMATH
  5. Andrew R. McCluskey; Tim Snow: uravu: Making Bayesian modelling easy(er) (2020) not zbMATH
  6. Anjalika Nande, Andrew Ferdowsian, Eric Lubin, Erez Yoeli, Martin Nowak: DyPy: A Python Library for Simulating Matrix-Form Games (2020) arXiv
  7. António Ramires, Gilberto Bernardes, Matthew E. P. Davies, Xavier Serra: TIV.lib: an open-source library for the tonal description of musical audio (2020) arXiv
  8. Arora, Rajat; Zhang, Xiaohan; Acharya, Amit: Finite element approximation of finite deformation dislocation mechanics (2020)
  9. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  10. Auger, Pierre; Pironneau, Olivier: Parameter identification by statistical learning of a stochastic dynamical system modelling a fishery with price variation (2020)
  11. Bartosz J. Bartmanski; Ruth E. Baker: StoSpa2: A C++ software package for stochastic simulations of spatially extended systems (2020) not zbMATH
  12. Bashier, Eihab B. M.: Practical numerical and scientific computing with MATLAB and Python (2020)
  13. Baudin, Gérard: Statistique. Estimation des incertitudes. Cours et applications en langage Python (2020)
  14. Bell Raj Eapen, Norm Archer, Kamran Sartipi: QRMine: A python package for triangulation in Grounded Theory (2020) arXiv
  15. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  16. Benjamin H Savitzky, Lauren A Hughes, Steven E Zeltmann, Hamish G Brown, Shiteng Zhao, Philipp M Pelz, Edward S Barnard, Jennifer Donohue, Luis Rangel DaCosta, Thomas C. Pekin, Ellis Kennedy, Matthew T Janish, Matthew M Schneider, Patrick Herring, Chirranjeevi Gopal, Abraham Anapolsky, Peter Ercius, Mary Scott, Jim Ciston, Andrew M Minor, Colin Ophus: py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets (2020) arXiv
  17. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  18. Blondeau Da Silva, Stéphane: Benford or not Benford: a systematic but not always well-founded use of an elegant law in experimental fields (2020)
  19. Brian de Silva; Kathleen Champion; Markus Quade; Jean-Christophe Loiseau; J. Nathan Kutz; Steven L. Brunton: PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data (2020) not zbMATH
  20. Brugière, Pierre: Quantitative portfolio management. With applications in Python (2020)

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