Jacob Vanderplas
Jacob Vanderplas
在 cs.washington.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Scikit-learn: Machine learning in Python
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
the Journal of machine Learning research 12, 2825-2830, 2011
429192011
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
52862020
The Astropy project: building an open-science project and status of the v2. 0 core package
AM Price-Whelan, BM Sipőcz, HM Günther, PL Lim, SM Crawford, ...
The Astronomical Journal 156 (3), 123, 2018
26542018
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
13512013
Statistics, data mining, and machine learning in astronomy
Ž Ivezić, AJ Connolly, JT VanderPlas, A Gray
Statistics, Data Mining, and Machine Learning in Astronomy, 2014
416*2014
First-year Sloan Digital Sky Survey-II supernova results: Hubble diagram and cosmological parameters
R Kessler, AC Becker, D Cinabro, J Vanderplas, JA Frieman, J Marriner, ...
The Astrophysical Journal Supplement Series 185 (1), 32, 2009
4152009
Understanding the lomb–scargle periodogram
JT VanderPlas
The Astrophysical Journal Supplement Series 236 (1), 16, 2018
3562018
Python data science handbook: Essential tools for working with data
J VanderPlas
" O'Reilly Media, Inc.", 2016
3342016
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
2442009
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
2442009
Lsst science book, version 2.0
PA Abell, J Allison, SF Anderson, JR Andrew, JRP Angel, L Armus, ...
arXiv preprint arXiv:0912.0201, 2009
2402009
Scikit-learn: Machine learning without learning the machinery
G Varoquaux, L Buitinck, G Louppe, O Grisel, F Pedregosa, A Mueller
GetMobile: Mobile Computing and Communications 19 (1), 29-33, 2015
2392015
SciPy 1.0 Contributors
P Virtanen, R Gommers, TE Oliphant, M Haberland, T Reddy, ...
Nat. Methods 17, 261-272, 2020
1872020
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
1832009
Mwaskom/Seaborn: V0. 8.1 (September 2017)
M Waskom, O Botvinnik, D O'Kane, P Hobson, S Lukauskas, ...
Zenodo, 2017
1822017
Periodograms for multiband astronomical time series
JT VanderPlas, Ž Ivezic
The Astrophysical Journal 812 (1), 18, 2015
1602015
Visualizing and measuring the geometry of BERT
A Coenen, E Reif, A Yuan, B Kim, A Pearce, F Viégas, M Wattenberg
arXiv preprint arXiv:1906.02715, 2019
1592019
Introduction to astroML: Machine learning for astrophysics
J VanderPlas, AJ Connolly, Ž Ivezić, A Gray
2012 conference on intelligent data understanding, 47-54, 2012
1562012
Seaborn: statistical data visualization
ML Waskom
Journal of Open Source Software 6 (60), 3021, 2021
1362021
mwaskom/seaborn: v0. 9.0 (July 2018)
M Waskom, O Botvinnik, D O’Kane, P Hobson, J Ostblom, S Lukauskas, ...
DOI: https://doi. org/10.5281/zenodo 1313201, 2018
1282018
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