Notebooks Coverage

Report on last executions.

89% 2019-06-23

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%	0.076	2019-06-16	centrale/centrale_201606.ipynb	Big Data, Azure, Machine Learning, Python	True	2.027	2	2	2
2	100%	0.093	2019-06-16	centrale/centrale_201606_sysrec.ipynb	Recommandations sur le web	True	2.406	1	1	1
3	100%	1.151	2019-06-16	ensae/2017_1a_ensae_nocture.ipynb	ENSAE 1A : nocturne	True	3.030	10	10	10
4	100%	0.316	2019-06-16	ensae/kaggle_review_2016.ipynb	Revue de compétitions Kaggle (2016)	True	2.047	2	2	2
5	100%	0.320	2019-06-16	ensae/kaggle_review_2017.ipynb	Revue de compétitions Kaggle (2017)	True	2.748	2	2	2
6	100%	3.836	2019-06-16	meshs/automation_finance_trading.ipynb	Les algorithmes, outils de décision automatique.	True	6.036	10	10	10
7	100%	136.313	2019-06-16	msexp/onnx_deploy.ipynb	Deploy machine learned models with ONNX	True	138.311	77	77	77
8	100%	5.796	2019-06-16	pydata/10_plotting_libraries.ipynb	10 plotting libraries	True	8.048	16	16	16
9	100%	15.665	2019-06-16	pydata/big_datashader.ipynb	datashader	True	18.495	28	28	28
10	80%	1.348	2019-06-16	pydata/gui_geoplotlib.ipynb	geoplotlib	True	3.067	5	4	4
11	100%	0.666	2019-06-16	pydata/im_biopython.ipynb	biopython	True	3.063	6	6	6
12	100%	0.793	2019-06-16	pydata/im_cartopy.ipynb	cartopy	True	3.070	4	4	4
13	100%	2.803	2019-06-16	pydata/im_ete3.ipynb	ete3	True	5.277	11	11	11
14	100%	2.265	2019-06-16	pydata/im_lifelines.ipynb	lifelines	True	4.057	7	7	7
15	90%	1.167	2019-06-16	pydata/im_matplotlib.ipynb	matplotlib	True	3.064	11	10	10
16	100%	5.187	2019-06-16	pydata/im_missingno.ipynb	missingno	True	7.083	13	13	13
17	100%	10.521	2019-06-16	pydata/im_mpl_scatter_density.ipynb	mpl-scatter-density	True	13.074	6	6	6
18	100%	0.887	2019-06-16	pydata/im_networkx.ipynb	networkx	True	3.592	4	4	4
19	100%	4.829	2019-06-16	pydata/im_plotnine.ipynb	plotnine	True	7.065	9	9	9
20	100%	0.326	2019-06-16	pydata/im_reportlab.ipynb	reportlab	True	2.060	4	4	4
21	100%	1.059	2019-06-16	pydata/im_scikit_plot.ipynb	scikit-plot	True	3.068	5	5	5
22	100%	5.253	2019-06-16	pydata/im_seaborn.ipynb	seaborn	True	7.078	5	5	5
23	100%	1.048	2019-06-16	pydata/js_bokeh.ipynb	bokeh	True	3.352	7	7	7
24	50%	0.199	2019-06-16	pydata/js_lightning_python.ipynb	lightning-python	True	3.229	4	2	2
25	0%	nan		pydata/js_mpld3.ipynb	mpld3		nan	17	0
26	100%	2.876	2019-06-16	pydata/js_plotly.ipynb	plotly	True	5.154	9	9	9
27	100%	3.701	2019-06-16	pydata/js_pydy_mass_spring_damper.ipynb	pydy	True	6.686	32	32	32
28	85%	0.458	2019-06-16	pydata/js_pyecharts.ipynb	pyecharts	True	3.066	7	6	6
29	100%	0.974	2019-06-16	pydata/js_pygal.ipynb	pygal	True	3.077	7	7	7
30	100%	1.743	2019-06-16	pydata/js_pythreejs.ipynb	pythreejs	True	4.073	5	5	5
31	100%	0.634	2019-06-16	pydata/js_vega.ipynb	vega	True	3.050	6	6	6
32	100%	2.554	2019-06-16	pydata/jsonly_treant.ipynb	treant-js	True	5.049	7	7	7
33	100%	4.527	2019-06-16	pydata/pyjs_bqplot.ipynb	bqplot	True	7.042	19	19	19
34	100%	2.406	2019-06-16	pydata/pyjs_brython.ipynb	brython, brythonmagic	True	6.688	18	18	18
35	100%	0.692	2019-06-16	pydata/pyjsc_vispy.ipynb	vispy	True	3.350	8	8	8
36	100%	71.715	2019-06-16	pyparis/onnx_deploy_pyparis.ipynb	Deploy machine learned models with ONNX	True	74.461	38	38	38
37	100%	24.857	2019-06-16	sklearn/onnx_sklearn_consortium.ipynb	ONNX, scikit-learn, persistence, deployment	True	27.662	46	46	46
38	100%	2.695	2019-06-16	sklearn/onnx_sklearn_custom.ipynb	Convert custom transformer into ONNX	True	5.082	17	17	17