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API design for machine learning software: experiences from the scikit-learn project L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... arXiv preprint arXiv:1309.0238, 2013 | 3569 | 2013 |
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Python data science handbook: Essential tools for working with data J VanderPlas " O'Reilly Media, Inc.", 2016 | 997 | 2016 |
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Statistics, data mining, and machine learning in astronomy: a practical Python guide for the analysis of survey data Ž Ivezić, AJ Connolly, JT VanderPlas, A Gray Princeton University Press, 2014 | 702* | 2014 |
S... Contributors,“SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,” P Virtanen, R Gommers, TE Oliphant, M Haberland, T Reddy, ... Nature methods 17 (3), 261-272, 2020 | 598 | 2020 |
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mwaskom/seaborn: v0. 8.1 (September 2017) M Waskom, O Botvinnik, D O'Kane, P Hobson, S Lukauskas, ... Zenodo, 2017 | 457 | 2017 |
SNANA: A public software package for supernova analysis R Kessler, JP Bernstein, D Cinabro, B Dilday, JA Frieman, S Jha, ... Publications of the Astronomical Society of the Pacific 121 (883), 1028, 2009 | 372 | 2009 |
Lsst science book, version 2.0 PA Abell, J Allison, SF Anderson, JR Andrew, JRP Angel, L Armus, ... | 360 | 2009 |
Periodograms for multiband astronomical time series JT VanderPlas, Ž Ivezic The Astrophysical Journal 812 (1), 18, 2015 | 292 | 2015 |
Altair: interactive statistical visualizations for Python J VanderPlas, B Granger, J Heer, D Moritz, K Wongsuphasawat, ... Journal of open source software 3 (32), 1057, 2018 | 240 | 2018 |
Introduction to astroML: Machine learning for astrophysics J VanderPlas, AJ Connolly, Ž Ivezić, A Gray 2012 conference on intelligent data understanding, 47-54, 2012 | 224 | 2012 |
Scikit-learn: Machine learning in python journal of machine learning research F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... Journal of machine learning research 12, 2825-2830, 2011 | 207 | 2011 |
First-year sloan digital sky survey-II (SDSS-II) supernova results: constraints on nonstandard cosmological models J Sollerman, E Mörtsell, TM Davis, M Blomqvist, B Bassett, AC Becker, ... The Astrophysical Journal 703 (2), 1374, 2009 | 197 | 2009 |