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 297 articles , 3 standard articles )

Showing results 1 to 20 of 297.
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  1. Rubio-Sánchez, Manuel: Introduction to recursive programming (2018)
  2. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  3. Alexandre Refregier, Lukas Gamper, Adam Amara, Lavinia Heisenberg: PyCosmo: An Integrated Cosmological Boltzmann Solver (2017) arXiv
  4. 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
  5. Bart van Merrienboer, Alexander B. Wiltschko, Dan Moldovan: Tangent: Automatic Differentiation Using Source Code Transformation in Python (2017) arXiv
  6. Benjamin Guedj, Bhargav Srinivasa Desikan: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation (2017) arXiv
  7. Bryan W. Weber, Chih-Jen Sung: UConnRCMPy: Python-based data analysis for rapid compression machines (2017) arXiv
  8. Bugliesi, Michele; Calzavara, Stefano; Focardi, Riccardo: Formal methods for web security (2017)
  9. 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
  10. E. Bachelet, M. Norbury, V. Bozza, R. Street: pyLIMA : an open source package for microlensing modeling. I. presentation of the software and analysis on single lens models (2017) arXiv
  11. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  12. Gordon, Steven I.; Guilfoos, Brian: Introduction to modeling and simulation with MATLAB and Python (2017)
  13. 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)
  14. Herman, Russell L.: An introduction to Fourier analysis (2017)
  15. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  16. 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
  17. Jyh-Miin Lin: Python Non-Uniform Fast Fourier Transform (PyNUFFT): multi-dimensional non-Cartesian image reconstruction package for heterogeneous platforms and applications to MRI (2017) arXiv
  18. Kalisch, Henrik; Moldabayev, Daulet; Verdier, Olivier: A numerical study of nonlinear dispersive wave models with $\mathsfSpecTraVVave$ (2017)
  19. Kim, Daeil; Swanson, Benjamin F.; Hughes, Michael C.; Sudderth, Erik B.: Refinery: an open source topic modeling web platform (2017)
  20. Lema^ıtre, Guillaume; Nogueira, Fernando; Aridas, Christos K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning (2017)

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