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 1007 articles , 3 standard articles )

Showing results 1 to 20 of 1007.
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  1. 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
  2. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  3. Bashier, Eihab B. M.: Practical numerical and scientific computing with MATLAB and Python (to appear) (2020)
  4. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  5. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  6. Cushing, David; Kamtue, Supanat; Peyerimhoff, Norbert; Watson May, Leyna: Quartic graphs which are Bakry-Émery curvature sharp (2020)
  7. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  8. Duncan N. Johnstone, Ben H. Martineau, Phillip Crout, Paul A. Midgley, Alexander S. Eggeman: Density-based clustering of crystal orientations and misorientations and the orix python library (2020) arXiv
  9. Emerson Boeira; Diego Eckhard: pyvrft: A Python package for the Virtual Reference Feedback Tuning, a direct data-driven control method (2020) not zbMATH
  10. Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
  11. Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, Zhenyu Zhao: CausalML: Python Package for Causal Machine Learning (2020) arXiv
  12. Jélvez, Enrique; Morales, Nelson; Askari-Nasab, Hooman: A new model for automated pushback selection (2020)
  13. J. Magnus Rahm; Paul Erhart: WulffPack: A Python package for Wulff constructions (2020) not zbMATH
  14. Joel C. Miller, Tony TIng: EoN (Epidemics on Networks): a fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks (2020) arXiv
  15. Kumar, Abhishek; Pothérat, Alban: Mixed baroclinic convection in a cavity (2020)
  16. Leonardo Uieda; Santiago Rubén Soler; Rémi Rampin; Hugo van Kemenade; Matthew Turk; Daniel Shapero; Anderson Banihirwe; John Leeman: Pooch: A friend to fetch your data files (2020) not zbMATH
  17. Li, Jiao; Ying, Jinyong: A simple and efficient technique to accelerate the computation of a nonlocal dielectric model for electrostatics of biomolecule (2020)
  18. Linge, Svein; Langtangen, Hans Petter: Programming for computations -- Python. A gentle introduction to numerical simulations with Python 3.6 (2020)
  19. Lukas Alber, Valentino Scalera, Vivek Unikandanunni, Daniel Schick, Stefano Bonetti: NTMpy: An open source package for solving coupled parabolic differential equations in the framework of the three-temperature model (2020) arXiv
  20. Lukas Geiger; Plumerai Team: Larq: An Open-Source Library for Training Binarized Neural Networks (2020) not zbMATH

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