Notebooks Coverage

Report on last executions.

89% 2019-08-19

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%	0.100	2019-08-19	centrale/centrale_201606.ipynb	Big Data, Azure, Machine Learning, Python	True	1.018	2	2	2
2	100%	0.099	2019-08-19	centrale/centrale_201606_sysrec.ipynb	Recommandations sur le web	True	0.985	1	1	1
3	100%	1.131	2019-08-19	ensae/2017_1a_ensae_nocture.ipynb	ENSAE 1A : nocturne	True	2.101	10	10	10
4	100%	0.357	2019-08-19	ensae/kaggle_review_2016.ipynb	Revue de compétitions Kaggle (2016)	True	1.285	2	2	2
5	100%	0.382	2019-08-19	ensae/kaggle_review_2017.ipynb	Revue de compétitions Kaggle (2017)	True	1.303	2	2	2
6	100%	4.056	2019-08-19	meshs/automation_finance_trading.ipynb	Les algorithmes, outils de décision automatique.	True	4.894	10	10	10
7	100%	110.667	2019-08-12	msexp/onnx_deploy.ipynb	Deploy machine learned models with ONNX	True	111.725	77	77	77
8	100%	7.296	2019-08-19	pydata/10_plotting_libraries.ipynb	10 plotting libraries	True	8.366	16	16	16
9	100%	15.439	2019-08-12	pydata/big_datashader.ipynb	datashader	True	16.544	28	28	28
10	80%	9.241	2019-08-12	pydata/gui_geoplotlib.ipynb	geoplotlib	True	10.164	5	4	4
11	100%	1.226	2019-08-19	pydata/im_biopython.ipynb	biopython	True	2.100	6	6	6
12	100%	0.879	2019-08-19	pydata/im_cartopy.ipynb	cartopy	True	1.744	4	4	4
13	100%	3.735	2019-08-12	pydata/im_ete3.ipynb	ete3	True	4.660	11	11	11
14	100%	2.827	2019-08-19	pydata/im_lifelines.ipynb	lifelines	True	3.677	7	7	7
15	90%	1.270	2019-08-19	pydata/im_matplotlib.ipynb	matplotlib	True	2.180	11	10	10
16	100%	5.388	2019-08-19	pydata/im_missingno.ipynb	missingno	True	6.302	13	13	13
17	100%	10.839	2019-08-19	pydata/im_mpl_scatter_density.ipynb	mpl-scatter-density	True	11.753	6	6	6
18	100%	0.914	2019-08-19	pydata/im_networkx.ipynb	networkx	True	1.851	4	4	4
19	100%	5.907	2019-08-19	pydata/im_plotnine.ipynb	plotnine	True	6.820	9	9	9
20	100%	0.915	2019-08-19	pydata/im_reportlab.ipynb	reportlab	True	1.781	4	4	4
21	100%	1.294	2019-08-19	pydata/im_scikit_plot.ipynb	scikit-plot	True	2.185	5	5	5
22	100%	4.996	2019-08-19	pydata/im_seaborn.ipynb	seaborn	True	5.840	5	5	5
23	100%	1.380	2019-08-19	pydata/js_bokeh.ipynb	bokeh	True	2.516	7	7	7
24	50%	0.100	2019-08-19	pydata/js_lightning_python.ipynb	lightning-python	True	1.014	4	2	2
25	0%	nan		pydata/js_mpld3.ipynb	mpld3		nan	17	0
26	100%	6.688	2019-08-19	pydata/js_plotly.ipynb	plotly	True	7.684	9	9	9
27	100%	4.019	2019-08-19	pydata/js_pydy_mass_spring_damper.ipynb	pydy	True	4.932	32	32	32
28	85%	0.876	2019-08-19	pydata/js_pyecharts.ipynb	pyecharts	True	1.739	7	6	6
29	100%	1.256	2019-08-19	pydata/js_pygal.ipynb	pygal	True	2.198	7	7	7
30	100%	2.278	2019-08-19	pydata/js_pythreejs.ipynb	pythreejs	True	3.291	5	5	5
31	100%	0.830	2019-08-19	pydata/js_vega.ipynb	vega	True	1.741	6	6	6
32	100%	0.529	2019-08-19	pydata/jsonly_treant.ipynb	treant-js	True	1.704	7	7	7
33	100%	2.386	2019-08-19	pydata/pyjs_bqplot.ipynb	bqplot	True	3.841	19	19	19
34	100%	2.120	2019-08-19	pydata/pyjs_brython.ipynb	brython, brythonmagic	True	2.977	18	18	18
35	100%	1.025	2019-08-19	pydata/pyjsc_vispy.ipynb	vispy	True	1.875	8	8	8
36	100%	52.536	2019-08-12	pyparis/onnx_deploy_pyparis.ipynb	Deploy machine learned models with ONNX	True	53.485	38	38	38
37	100%	40.451	2019-08-19	sklearn/onnx_sklearn_consortium.ipynb	ONNX, scikit-learn, persistence, deployment	True	41.344	47	47	47
38	100%	2.709	2019-08-19	sklearn/onnx_sklearn_custom.ipynb	Convert custom transformer into ONNX	True	3.619	17	17	17