Notebooks Coverage

Report on last executions.

24% 2023-06-26

index	coverage	exe time	last execution	name	title	success	time	nb cells	nb runs	nb valid
0	0%	nan		actuaires/introprogpy.ipynb	Datascience et programmation		nan	67	0
1	0%	nan		centrale/azure_pig.ipynb	HDInsight, PIG		nan	35	0
2	0%	nan		centrale/centrale_201606.ipynb	Big Data, Azure, Machine Learning, Python		nan	11	0
3	0%	nan		centrale/centrale_201606_sysrec.ipynb	Recommandations sur le web		nan	17	0
4	0%	nan		ensae/2017_1a_ensae_nocture.ipynb	ENSAE 1A : nocturne		nan	35	0
5	0%	nan		ensae/introcode.ipynb	Leçon de code		nan	90	0
6	0%	nan		ensae/kaggle_review_2016.ipynb	Revue de compétitions Kaggle (2016)		nan	21	0
7	0%	nan		ensae/kaggle_review_2017.ipynb	Revue de compétitions Kaggle (2017)		nan	19	0
8	0%	nan		ensae_api/sklearn_api.ipynb	API de sciki-learn et modèles customisés		nan	78	0
9	0%	nan		meshs/automation_finance_trading.ipynb	Les algorithmes, outils de décision automatique.		nan	42	0
10	0%	nan		msexp/onnx_deploy.ipynb	Deploy machine learned models with ONNX		nan	130	0
11	0%	nan		pydata/10_plotting_libraries.ipynb	10 plotting libraries		nan	84	0
12	0%	nan		pydata/big_datashader.ipynb	datashader		nan	35	0
13	0%	nan		pydata/gui_geoplotlib.ipynb	geoplotlib		nan	8	0
14	100%	2.031	2023-06-26	pydata/im_biopython.ipynb	biopython	True	5.758	6	6	6
15	100%	9.464	2023-06-26	pydata/im_cartopy.ipynb	cartopy	True	12.980	4	4	4
16	0%	nan		pydata/im_ete3.ipynb	ete3		nan	14	0
17	100%	14.344	2023-06-26	pydata/im_lifelines.ipynb	lifelines	True	17.892	7	7	7
18	90%	4.449	2023-06-26	pydata/im_matplotlib.ipynb	matplotlib	True	7.964	11	10	10
19	100%	20.346	2023-06-26	pydata/im_missingno.ipynb	missingno	True	23.870	13	13	13
20	100%	13.722	2023-06-26	pydata/im_mpl_scatter_density.ipynb	mpl-scatter-density	True	17.288	5	5	5
21	100%	4.431	2023-06-26	pydata/im_networkx.ipynb	networkx	True	7.951	4	4	4
22	100%	15.096	2023-06-26	pydata/im_plotnine.ipynb	plotnine	True	18.644	9	9	9
23	100%	1.013	2023-06-26	pydata/im_reportlab.ipynb	reportlab	True	4.516	4	4	4
24	100%	5.197	2023-06-26	pydata/im_scikit_plot.ipynb	scikit-plot	True	8.694	5	5	5
25	100%	23.350	2023-06-26	pydata/im_seaborn.ipynb	seaborn	True	26.954	5	5	5
26	100%	9.966	2023-06-26	pydata/js_bokeh.ipynb	bokeh	True	13.491	7	7	7
27	0%	nan		pydata/js_lightning_python.ipynb	lightning-python		nan	9	0
28	0%	nan		pydata/js_mpld3.ipynb	mpld3		nan	17	0
29	100%	11.003	2023-06-26	pydata/js_plotly.ipynb	plotly	True	14.781	9	9	9
30	100%	14.146	2023-06-26	pydata/js_pydy_mass_spring_damper.ipynb	pydy	True	17.712	32	32	32
31	85%	3.655	2023-06-26	pydata/js_pyecharts.ipynb	pyecharts	True	7.270	7	6	6
32	100%	1.518	2023-06-26	pydata/js_pygal.ipynb	pygal	True	5.205	7	7	7
33	100%	3.131	2023-06-26	pydata/js_pythreejs.ipynb	pythreejs	True	7.167	5	5	5
34	100%	1.393	2023-06-26	pydata/js_vega.ipynb	vega	True	4.942	6	6	6
35	100%	0.736	2023-06-26	pydata/jsonly_treant.ipynb	treant-js	True	4.657	7	7	7
36	100%	5.377	2023-06-26	pydata/pyjs_bqplot.ipynb	bqplot	True	11.655	19	19	19
37	100%	1.704	2023-06-26	pydata/pyjs_brython.ipynb	brython, brythonmagic	True	5.282	18	18	18
38	0%	nan		pydata/pyjsc_vispy.ipynb	vispy		nan	11	0
39	0%	nan		pyparis/onnx_deploy_pyparis.ipynb	Deploy machine learned models with ONNX		nan	70	0
40	100%	57.515	2023-06-26	sklearn/onnx_sklearn_consortium.ipynb	ONNX, scikit-learn, persistence, deployment	True	61.211	47	47	47
41	100%	9.230	2023-06-26	sklearn/onnx_sklearn_custom.ipynb	Convert custom transformer into ONNX	True	12.880	17	17	17