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 868 articles , 3 standard articles )

Showing results 1 to 20 of 868.
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  1. Adam J. Batten: Fruitbat: A Python Package for Estimating Redshifts of Fast Radio Bursts (2019) arXiv
  2. Adrien Leger; Tommaso Leonardi: pycoQC, interactive quality control for Oxford NanoporeSequencing (2019) not zbMATH
  3. A. Kapanowski, A. Krawczyk: Halin graphs are 3-vertex-colorable except even wheels (2019) arXiv
  4. Alexander J. Gates; Yong-Yeol Ahn: CluSim: a python package for calculating clustering similarity (2019) not zbMATH
  5. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  6. Andreas F. Haselsteiner; Jannik Lehmkuhl; Tobias Pape; Kai-Lukas Windmeier; Klaus-Dieter Thoben: ViroCon: A software to compute multivariate extremes using the environmental contour method (2019) not zbMATH
  7. Andreas Nüßing, Maria Carla Piastra, Sophie Schrader, Tuuli Miinalainen, Heinrich Brinck, Carsten H. Wolters, Christian Engwer: duneuro - A software toolbox for forward modeling in neuroscience (2019) arXiv
  8. Andrew Abi-Mansour: PyGran: An object-oriented library for DEM simulation and analysis (2019) not zbMATH
  9. Barbara De Palma, Marco Erba, Luca Mantovani, Nicola Mosco: A Python program for the implementation of the GAMMA-method for Monte Carlo simulations (2019) not zbMATH
  10. Benítez-Peña, S.; Blanquero, R.; Carrizosa, E.; Ramírez-Cobo, P.: Cost-sensitive feature selection for support vector machines (2019)
  11. Benjamin Bengfort; Rebecca Bilbro: Yellowbrick: Visualizing the Scikit-Learn Model Selection Process (2019) not zbMATH
  12. Ben Mather; Robert Delhaye: PyCurious: A Python module for computing the Curie depth from the magnetic anomaly (2019) not zbMATH
  13. Bingham, Eli; Chen, Jonathan P.; Jankowiak, Martin; Obermeyer, Fritz; Pradhan, Neeraj; Karaletsos, Theofanis; Singh, Rohit; Szerlip, Paul; Horsfall, Paul; Goodman, Noah D.: Pyro: deep universal probabilistic programming (2019)
  14. Birkandan, Tolga; Güzelgün, Ceren; Şirin, Elif; Uslu, Mustafa Can: Symbolic and numerical analysis in general relativity with open source computer algebra systems (2019)
  15. Blaise J. Thompson; Kyle F. Sunden; Darien J. Morrow; Daniel D. Kohler; John C. Wright: WrightTools: a Python package for multidimensional spectroscopy (2019) not zbMATH
  16. Burd, Adrian: Mathematical methods in the Earth and environmental sciences (2019)
  17. C. Bane Sullivan; Alexander A. Kaszynski: PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK) (2019) not zbMATH
  18. Christine Harvey; R. S. Weigel: Transplant2Mongo: Python Scripts that Insert Organ Procurement and Transplantation Network (OPTN) Data in MongoDB (2019) not zbMATH
  19. Christopher Hahne; Amar Aggoun: PlenoptiSign: An optical design tool for plenoptic imaging (2019) not zbMATH
  20. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH

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