IPython

IPython: a system for interactive scientific computing. IPython provides a rich architecture for interactive computing with: A powerful interactive shell. A kernel for Jupyter. Support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing.


References in zbMATH (referenced in 53 articles )

Showing results 1 to 20 of 53.
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  1. Leah Wasser, Maxwell B. Joseph, Joe McGlinchy, Jenny Palomino, Korinek, Nathan, Chris Holdgraf, Tim Head: EarthPy: A Python package that makes it easier toexplore and plot raster and vector data using opensource Python tools (2019) not zbMATH
  2. Linge, Svein; Langtangen, Hans Petter: Programming for computations -- Python. A gentle introduction to numerical simulations with Python 3.6 (2019)
  3. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  4. Rising Odegua: DataSist: A Python-based library for easy data analysis, visualization and modeling (2019) arXiv
  5. Wielemaker, Jan; Riguzzi, Fabrizio; Kowalski, Robert A.; Lager, Torbjörn; Sadri, Fariba; Calejo, Miguel: Using SWISH to realize interactive web-based tutorials for logic-based languages (2019)
  6. Yadu Babuji, Anna Woodard, Zhuozhao Li, Daniel S. Katz, Ben Clifford, Rohan Kumar, Lukasz Lacinski, Ryan Chard, Justin M. Wozniak, Ian Foster, Michael Wilde, Kyle Chard: Parsl: Pervasive Parallel Programming in Python (2019) arXiv
  7. Zhukov, Oleg A.; Kazakova, Tatiana A.; Maksimov, Georgy V.; Brazhe, Alexey R.: Cost of auditory sharpness: model-based estimate of energy use by auditory brainstem “octopus” neurons (2019)
  8. Abreu, Rafael; Su, Zeming; Kamm, Jochen; Gao, Jinghuai: On the accuracy of the complex-step-finite-difference method (2018)
  9. D.M. Straub: flavio: a Python package for flavour and precision phenomenology in the Standard Model and beyond (2018) arXiv
  10. Guedj, Benjamin; Desikan, Bhargav Srinivasa: Pycobra: a Python toolbox for ensemble learning and visualisation (2018)
  11. Henley, A. J.; Wolf, Dave: Learn data analysis with Python. Lessons in coding (2018)
  12. Jason Laura; Kelvin Rodriguez; Adam C. Paquette; Evin Dunn: AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data (2018) not zbMATH
  13. Lynch, Stephen: Dynamical systems with applications using Python (2018)
  14. Minimair, Manfred: MathChat: computational mathematics via a social machine (2018)
  15. Robert Gieseke; Sven N Willner; Matthias Mengel: Pymagicc: A Python wrapper for the simple climate model MAGICC (2018) not zbMATH
  16. Sven N Willner; Corinne Hartin; Robert Gieseke: pyhector: A Python interface for the simple climate model Hector (2018) not zbMATH
  17. Amen, Saeed: Using Python to analyse financial markets (2017)
  18. Benjamin Guedj, Bhargav Srinivasa Desikan: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation (2017) arXiv
  19. Bryan W. Weber, Chih-Jen Sung: UConnRCMPy: Python-based data analysis for rapid compression machines (2017) arXiv
  20. Coelho, L.P.: Jug: Software for Parallel Reproducible Computation in Python (2017) not zbMATH

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