Notebooks Coverage

Report on last executions.

68% 2020-12-27

index	coverage	exe time	last execution	name	title	success	time	nb cells	nb runs	nb valid
0	0%	nan		centrale/azure_pig.ipynb	HDInsight, PIG		nan	35	0
1	100%	0.104	2020-12-27	centrale/centrale_201606.ipynb	Big Data, Azure, Machine Learning, Python	True	2.291	2	2	2
2	100%	0.086	2020-12-27	centrale/centrale_201606_sysrec.ipynb	Recommandations sur le web	True	0.984	1	1	1
3	100%	0.911	2020-12-27	ensae/2017_1a_ensae_nocture.ipynb	ENSAE 1A : nocturne	True	2.731	10	10	10
4	0%	nan		ensae/introcode.ipynb	Leçon de code		nan	90	0
5	100%	0.372	2020-12-27	ensae/kaggle_review_2016.ipynb	Revue de compétitions Kaggle (2016)	True	2.263	2	2	2
6	100%	0.385	2020-12-27	ensae/kaggle_review_2017.ipynb	Revue de compétitions Kaggle (2017)	True	2.145	2	2	2
7	0%	nan		ensae_api/sklearn_api.ipynb	API de sciki-learn et modèles customisés		nan	78	0
8	100%	4.269	2020-12-27	meshs/automation_finance_trading.ipynb	Les algorithmes, outils de décision automatique.	True	5.974	10	10	10
9	100%	100.241	2020-12-13	msexp/onnx_deploy.ipynb	Deploy machine learned models with ONNX	True	103.776	82	82	82
10	100%	15.436	2020-12-27	pydata/10_plotting_libraries.ipynb	10 plotting libraries	True	16.495	16	16	16
11	100%	17.866	2020-12-20	pydata/big_datashader.ipynb	datashader	True	19.019	28	28	28
12	80%	0.882	2020-12-20	pydata/gui_geoplotlib.ipynb	geoplotlib	True	1.886	5	4	4
13	100%	1.316	2020-12-27	pydata/im_biopython.ipynb	biopython	True	2.237	6	6	6
14	100%	8.522	2020-12-27	pydata/im_cartopy.ipynb	cartopy	True	9.387	4	4	4
15	100%	4.296	2020-12-20	pydata/im_ete3.ipynb	ete3	True	5.435	11	11	11
16	100%	2.821	2020-12-27	pydata/im_lifelines.ipynb	lifelines	True	3.661	7	7	7
17	90%	1.380	2020-12-27	pydata/im_matplotlib.ipynb	matplotlib	True	2.259	11	10	10
18	100%	4.250	2020-12-27	pydata/im_missingno.ipynb	missingno	True	5.087	13	13	13
19	100%	9.480	2020-12-27	pydata/im_mpl_scatter_density.ipynb	mpl-scatter-density	True	10.902	5	5	5
20	100%	0.768	2020-12-27	pydata/im_networkx.ipynb	networkx	True	2.595	4	4	4
21	100%	7.294	2020-12-27	pydata/im_plotnine.ipynb	plotnine	True	8.148	9	9	9
22	100%	0.537	2020-12-27	pydata/im_reportlab.ipynb	reportlab	True	1.394	4	4	4
23	100%	1.570	2020-12-27	pydata/im_scikit_plot.ipynb	scikit-plot	True	2.444	5	5	5
24	100%	3.753	2020-12-27	pydata/im_seaborn.ipynb	seaborn	True	4.615	5	5	5
25	100%	1.422	2020-12-27	pydata/js_bokeh.ipynb	bokeh	True	3.023	7	7	7
26	50%	0.118	2020-12-27	pydata/js_lightning_python.ipynb	lightning-python	True	2.699	4	2	2
27	0%	nan		pydata/js_mpld3.ipynb	mpld3		nan	17	0
28	100%	6.248	2020-12-27	pydata/js_plotly.ipynb	plotly	True	7.136	9	9	9
29	100%	4.623	2020-12-27	pydata/js_pydy_mass_spring_damper.ipynb	pydy	True	6.629	32	32	32
30	85%	0.724	2020-12-27	pydata/js_pyecharts.ipynb	pyecharts	True	1.554	7	6	6
31	100%	1.254	2020-12-27	pydata/js_pygal.ipynb	pygal	True	2.114	7	7	7
32	100%	2.001	2020-12-27	pydata/js_pythreejs.ipynb	pythreejs	True	2.966	5	5	5
33	100%	0.708	2020-12-27	pydata/js_vega.ipynb	vega	True	1.540	6	6	6
34	100%	0.556	2020-12-27	pydata/jsonly_treant.ipynb	treant-js	True	1.668	7	7	7
35	100%	2.911	2020-12-27	pydata/pyjs_bqplot.ipynb	bqplot	True	4.388	19	19	19
36	100%	1.458	2020-12-27	pydata/pyjs_brython.ipynb	brython, brythonmagic	True	4.742	18	18	18
37	100%	1.083	2020-12-27	pydata/pyjsc_vispy.ipynb	vispy	True	2.940	8	8	8
38	100%	62.519	2020-12-13	pyparis/onnx_deploy_pyparis.ipynb	Deploy machine learned models with ONNX	True	67.120	38	38	38
39	100%	46.429	2020-12-27	sklearn/onnx_sklearn_consortium.ipynb	ONNX, scikit-learn, persistence, deployment	True	48.286	47	47	47
40	100%	2.940	2020-12-27	sklearn/onnx_sklearn_custom.ipynb	Convert custom transformer into ONNX	True	3.852	17	17	17