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 1114 articles , 3 standard articles )

Showing results 1 to 20 of 1114.
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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. Andrew R. Bennett; Joseph J. Hamman; Bart Nijssen: MetSim: A Python package for estimation and disaggregation of meteorological data (2020) not zbMATH
  4. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  5. Bashier, Eihab B. M.: Practical numerical and scientific computing with MATLAB and Python (2020)
  6. Bell Raj Eapen, Norm Archer, Kamran Sartipi: QRMine: A python package for triangulation in Grounded Theory (2020) arXiv
  7. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  8. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  9. 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
  10. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for pyomo (2020)
  11. Cohen, David A.; Cooper, Martin C.; Kaznatcheev, Artem; Wallace, Mark: Steepest ascent can be exponential in bounded treewidth problems (2020)
  12. Cushing, David; Kamtue, Supanat; Peyerimhoff, Norbert; Watson May, Leyna: Quartic graphs which are Bakry-Émery curvature sharp (2020)
  13. David Torres Sanchez: cspy: A Python package with a collection of algorithms for the (Resource) Constrained Shortest Path problem (2020) not zbMATH
  14. Dhindsa, Kiret; Cook, Oliver; Mudway, Thomas; Khawaja, Areeb; Harwood, Ron; Sonnadara, Ranil: LFSpy: A Python Implementation of Local Feature Selection for Data Classification with scikit-learn Compatibility (2020) not zbMATH
  15. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  16. 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
  17. Eduard Szöcs, Tamás Stirling, Eric R. Scott, Andreas Scharmüller, Ralf B. Schäfer: webchem: An R Package to Retrieve Chemical Information from the Web (2020) not zbMATH
  18. Emerson Boeira; Diego Eckhard: pyvrft: A Python package for the Virtual Reference Feedback Tuning, a direct data-driven control method (2020) not zbMATH
  19. Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck: direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods (2020) arXiv
  20. Fangzhou Xie: Pruned Wasserstein Index Generation Model and wigpy Package (2020) arXiv

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