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 2049 articles , 4 standard articles )

Showing results 1 to 20 of 2049.
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  1. Agresti, Alan; Kateri, Maria: Foundations of statistics for data scientists. With R and Python (2022)
  2. Akimitsu Ishii, Ryunosuke Kamijyo, Akinori Yamanaka, Akiyasu Yamamoto: BOXVIA: Bayesian optimization executable and visualizable application (2022) not zbMATH
  3. Alex Tritt, Joshua Morris, Joel Hochstetter, R. P. Anderson, James Saunderson, L. D. Turner: Spinsim: a GPU optimized python package for simulating spin-half and spin-one quantum systems (2022) arXiv
  4. Alkhaleel, Basem A.; Liao, Haitao; Sullivan, Kelly M.: Risk and resilience-based optimal post-disruption restoration for critical infrastructures under uncertainty (2022)
  5. Alvaro J. Garcia-Tejedor, Alberto Nogales: GEMA: An open-source Python library for self-organizing-maps (2022) arXiv
  6. Antonio Serrano-Muñoz, Nestor Arana-Arexolaleiba, Dimitrios Chrysostomou, Simon Bøgh: skrl: Modular and Flexible Library for Reinforcement Learning (2022) arXiv
  7. Araujo, Gui; Moura, Rafael Rios: Individual specialization and generalization in predator-prey dynamics: the determinant role of predation efficiency and prey reproductive rates (2022)
  8. Ausas, Roberto Federico; Gebhardt, Cristian Guillermo; Buscaglia, Gustavo Carlos: A finite element method for simulating soft active non-shearable rods immersed in generalized Newtonian fluids (2022)
  9. Baijiong Lin, Yu Zhang: LibMTL: A Python Library for Multi-Task Learning (2022) arXiv
  10. Besse, Christophe; Duboscq, Romain; Le Coz, Stefan: Numerical simulations on nonlinear quantum graphs with the GraFiDi library (2022)
  11. Bevilacqua, Moreno; Caamaño-Carrillo, Christian; Porcu, Emilio: Unifying compactly supported and Matérn covariance functions in spatial statistics (2022)
  12. Bilokon, Paul Alexander: Python, data science and machine learning. From scratch to productivity (to appear) (2022)
  13. Bjørdalsbakke, Nikolai L.; Sturdy, Jacob T.; Hose, David R.; Hellevik, Leif R.: Parameter estimation for closed-loop lumped parameter models of the systemic circulation using synthetic data (2022)
  14. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: On sparse optimal regression trees (2022)
  15. Boussaada, Islam; Mazanti, Guilherme; Niculescu, Silviu-Iulian: Some remarks on the location of non-asymptotic zeros of Whittaker and Kummer hypergeometric functions (2022)
  16. Cai, Shang-Rong; Hwang, Feng-Nan: A hybrid-line-and-curve search globalization technique for inexact Newton methods (2022)
  17. Chen, Cathy Yi-Hsuan; Fengler, Matthias R.; Härdle, Wolfgang Karl; Liu, Yanchu: Media-expressed tone, option characteristics, and stock return predictability (2022)
  18. Chloe Brimicombe, Claudia Di Napoli, Tiago Quintino, Florian Pappenberger, Rosalind Cornforth, Hannah L. Cloke: Thermofeel: A python thermal comfort indices library (2022) not zbMATH
  19. Christoph W. Wagner; Sebastian Semper; Jan Kirchhof: fastmat: Efficient linear transforms in Python (2022) not zbMATH
  20. Ciaran Welsh, Jin Xu, Lucian Smith, Matthias König, Kiri Choi, Herbert M. Sauro: libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis Library (2022) arXiv

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