Notebooks Coverage

Report on last executions.

77% 2019-11-12

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%	0.086	2019-11-12	centrale/centrale_201606.ipynb	Big Data, Azure, Machine Learning, Python	True	1.712	2	2	2
2	100%	0.074	2019-11-12	centrale/centrale_201606_sysrec.ipynb	Recommandations sur le web	True	0.893	1	1	1
3	100%	0.694	2019-11-12	ensae/2017_1a_ensae_nocture.ipynb	ENSAE 1A : nocturne	True	2.532	10	10	10
4	100%	0.363	2019-11-12	ensae/kaggle_review_2016.ipynb	Revue de compétitions Kaggle (2016)	True	2.101	2	2	2
5	100%	0.371	2019-11-12	ensae/kaggle_review_2017.ipynb	Revue de compétitions Kaggle (2017)	True	1.992	2	2	2
6	0%	nan		ensae_api/sklearn_api.ipynb	API de sciki-learn et modèles customisés		nan	78	0
7	100%	3.628	2019-11-12	meshs/automation_finance_trading.ipynb	Les algorithmes, outils de décision automatique.	True	5.238	10	10	10
8	100%	103.371	2019-11-11	msexp/onnx_deploy.ipynb	Deploy machine learned models with ONNX	True	104.256	77	77	77
9	100%	5.027	2019-11-12	pydata/10_plotting_libraries.ipynb	10 plotting libraries	True	5.857	16	16	16
10	100%	23.001	2019-11-11	pydata/big_datashader.ipynb	datashader	True	24.097	28	28	28
11	80%	1.039	2019-11-11	pydata/gui_geoplotlib.ipynb	geoplotlib	True	1.898	5	4	4
12	100%	0.744	2019-11-12	pydata/im_biopython.ipynb	biopython	True	1.553	6	6	6
13	100%	0.841	2019-11-12	pydata/im_cartopy.ipynb	cartopy	True	1.643	4	4	4
14	100%	9.160	2019-11-11	pydata/im_ete3.ipynb	ete3	True	10.143	11	11	11
15	100%	2.032	2019-11-12	pydata/im_lifelines.ipynb	lifelines	True	2.825	7	7	7
16	90%	1.125	2019-11-12	pydata/im_matplotlib.ipynb	matplotlib	True	1.943	11	10	10
17	100%	5.607	2019-11-12	pydata/im_missingno.ipynb	missingno	True	6.618	13	13	13
18	100%	23.525	2019-11-12	pydata/im_mpl_scatter_density.ipynb	mpl-scatter-density	True	24.293	6	6	6
19	100%	0.956	2019-11-12	pydata/im_networkx.ipynb	networkx	True	3.364	4	4	4
20	100%	4.779	2019-11-12	pydata/im_plotnine.ipynb	plotnine	True	5.545	9	9	9
21	100%	0.306	2019-11-12	pydata/im_reportlab.ipynb	reportlab	True	1.101	4	4	4
22	100%	1.442	2019-11-12	pydata/im_scikit_plot.ipynb	scikit-plot	True	2.207	5	5	5
23	100%	5.387	2019-11-12	pydata/im_seaborn.ipynb	seaborn	True	6.169	5	5	5
24	100%	1.150	2019-11-12	pydata/js_bokeh.ipynb	bokeh	True	3.020	7	7	7
25	50%	0.091	2019-11-12	pydata/js_lightning_python.ipynb	lightning-python	True	1.755	4	2	2
26	0%	nan		pydata/js_mpld3.ipynb	mpld3		nan	17	0
27	100%	2.588	2019-11-12	pydata/js_plotly.ipynb	plotly	True	3.384	9	9	9
28	100%	4.161	2019-11-12	pydata/js_pydy_mass_spring_damper.ipynb	pydy	True	5.845	32	32	32
29	85%	0.465	2019-11-12	pydata/js_pyecharts.ipynb	pyecharts	True	1.263	7	6	6
30	100%	1.004	2019-11-12	pydata/js_pygal.ipynb	pygal	True	1.816	7	7	7
31	100%	1.009	2019-11-12	pydata/js_pythreejs.ipynb	pythreejs	True	1.904	5	5	5
32	100%	0.708	2019-11-12	pydata/js_vega.ipynb	vega	True	1.499	6	6	6
33	100%	0.642	2019-11-12	pydata/jsonly_treant.ipynb	treant-js	True	1.564	7	7	7
34	100%	2.321	2019-11-12	pydata/pyjs_bqplot.ipynb	bqplot	True	4.080	19	19	19
35	100%	1.667	2019-11-12	pydata/pyjs_brython.ipynb	brython, brythonmagic	True	3.370	18	18	18
36	100%	1.175	2019-11-12	pydata/pyjsc_vispy.ipynb	vispy	True	2.850	8	8	8
37	100%	54.529	2019-11-11	pyparis/onnx_deploy_pyparis.ipynb	Deploy machine learned models with ONNX	True	56.233	38	38	38
38	100%	56.136	2019-11-12	sklearn/onnx_sklearn_consortium.ipynb	ONNX, scikit-learn, persistence, deployment	True	57.809	47	47	47
39	100%	3.178	2019-11-12	sklearn/onnx_sklearn_custom.ipynb	Convert custom transformer into ONNX	True	3.989	17	17	17