Notebooks Coverage

Report on last executions.

68% 2020-09-27

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%	0.091	2020-09-27	centrale/centrale_201606.ipynb	Big Data, Azure, Machine Learning, Python	True	1.557	2	2	2
2	100%	0.087	2020-09-27	centrale/centrale_201606_sysrec.ipynb	Recommandations sur le web	True	1.044	1	1	1
3	100%	8.167	2020-09-27	ensae/2017_1a_ensae_nocture.ipynb	ENSAE 1A : nocturne	True	9.939	10	10	10
4	0%	nan		ensae/introcode.ipynb	Leçon de code		nan	90	0
5	100%	0.381	2020-09-27	ensae/kaggle_review_2016.ipynb	Revue de compétitions Kaggle (2016)	True	2.327	2	2	2
6	100%	0.369	2020-09-27	ensae/kaggle_review_2017.ipynb	Revue de compétitions Kaggle (2017)	True	1.935	2	2	2
7	0%	nan		ensae_api/sklearn_api.ipynb	API de sciki-learn et modèles customisés		nan	78	0
8	100%	4.293	2020-09-27	meshs/automation_finance_trading.ipynb	Les algorithmes, outils de décision automatique.	True	5.794	10	10	10
9	100%	95.438	2020-09-20	msexp/onnx_deploy.ipynb	Deploy machine learned models with ONNX	True	96.468	82	82	82
10	100%	15.341	2020-09-27	pydata/10_plotting_libraries.ipynb	10 plotting libraries	True	16.338	16	16	16
11	100%	16.155	2020-09-20	pydata/big_datashader.ipynb	datashader	True	19.621	28	28	28
12	80%	0.801	2020-09-20	pydata/gui_geoplotlib.ipynb	geoplotlib	True	1.673	5	4	4
13	100%	1.290	2020-09-27	pydata/im_biopython.ipynb	biopython	True	2.123	6	6	6
14	100%	8.378	2020-09-27	pydata/im_cartopy.ipynb	cartopy	True	9.222	4	4	4
15	100%	4.368	2020-09-20	pydata/im_ete3.ipynb	ete3	True	5.238	11	11	11
16	100%	3.087	2020-09-27	pydata/im_lifelines.ipynb	lifelines	True	3.921	7	7	7
17	90%	1.329	2020-09-27	pydata/im_matplotlib.ipynb	matplotlib	True	2.155	11	10	10
18	100%	4.165	2020-09-27	pydata/im_missingno.ipynb	missingno	True	5.237	13	13	13
19	100%	9.097	2020-09-27	pydata/im_mpl_scatter_density.ipynb	mpl-scatter-density	True	10.031	5	5	5
20	100%	0.825	2020-09-27	pydata/im_networkx.ipynb	networkx	True	2.320	4	4	4
21	100%	7.041	2020-09-27	pydata/im_plotnine.ipynb	plotnine	True	8.205	9	9	9
22	100%	0.540	2020-09-27	pydata/im_reportlab.ipynb	reportlab	True	1.419	4	4	4
23	100%	1.524	2020-09-27	pydata/im_scikit_plot.ipynb	scikit-plot	True	2.416	5	5	5
24	100%	4.073	2020-09-27	pydata/im_seaborn.ipynb	seaborn	True	5.036	5	5	5
25	100%	1.792	2020-09-27	pydata/js_bokeh.ipynb	bokeh	True	3.412	7	7	7
26	50%	0.083	2020-09-27	pydata/js_lightning_python.ipynb	lightning-python	True	1.948	4	2	2
27	0%	nan		pydata/js_mpld3.ipynb	mpld3		nan	17	0
28	100%	6.564	2020-09-27	pydata/js_plotly.ipynb	plotly	True	7.449	9	9	9
29	100%	3.835	2020-09-27	pydata/js_pydy_mass_spring_damper.ipynb	pydy	True	5.593	32	32	32
30	85%	0.702	2020-09-27	pydata/js_pyecharts.ipynb	pyecharts	True	1.552	7	6	6
31	100%	1.249	2020-09-27	pydata/js_pygal.ipynb	pygal	True	2.138	7	7	7
32	100%	1.922	2020-09-27	pydata/js_pythreejs.ipynb	pythreejs	True	2.857	5	5	5
33	100%	0.693	2020-09-27	pydata/js_vega.ipynb	vega	True	1.512	6	6	6
34	100%	0.674	2020-09-27	pydata/jsonly_treant.ipynb	treant-js	True	1.590	7	7	7
35	100%	4.970	2020-09-27	pydata/pyjs_bqplot.ipynb	bqplot	True	6.443	19	19	19
36	100%	1.589	2020-09-27	pydata/pyjs_brython.ipynb	brython, brythonmagic	True	3.566	18	18	18
37	100%	0.951	2020-09-27	pydata/pyjsc_vispy.ipynb	vispy	True	2.547	8	8	8
38	100%	52.269	2020-09-20	pyparis/onnx_deploy_pyparis.ipynb	Deploy machine learned models with ONNX	True	53.376	38	38	38
39	100%	30.587	2020-09-27	sklearn/onnx_sklearn_consortium.ipynb	ONNX, scikit-learn, persistence, deployment	True	32.183	47	47	47
40	100%	2.820	2020-09-27	sklearn/onnx_sklearn_custom.ipynb	Convert custom transformer into ONNX	True	3.690	17	17	17