Notebooks Coverage

Report on last executions.

61% 2023-07-20

index	coverage	exe time	last execution	name	title	success	time	nb cells	nb runs	nb valid
0	100%	21.899	2023-06-23	api_rest/rest_api_search_images.ipynb	Search engines for images through a REST API	True	25.746	14	14	14
1	100%	0.581	2023-07-20	cheat_sheets/chsh_dates.ipynb	Cheat Sheet on dates	True	4.076	9	9	9
2	100%	13.104	2023-06-23	cheat_sheets/chsh_files.ipynb	Cheat Sheet on files	True	16.982	21	21	21
3	100%	4.825	2023-07-20	cheat_sheets/chsh_geo.ipynb	Cheat sheet on Geocoordinates	True	8.345	10	10	10
4	100%	5.269	2023-07-20	cheat_sheets/chsh_graphs.ipynb	Cheat Sheet on Graphs	True	8.803	8	8	8
5	100%	2.033	2023-07-20	cheat_sheets/chsh_html.ipynb	Cheat Sheet on HTML	True	5.541	12	12	12
6	100%	1.893	2023-07-20	cheat_sheets/chsh_images.ipynb	Images and matrices	True	5.424	18	18	18
7	100%	3.647	2023-07-20	cheat_sheets/chsh_pandas.ipynb	Uncommon operation with dataframes	True	7.179	10	10	10
8	100%	23.117	2023-07-20	cheat_sheets/chsh_pip_install.ipynb	Pip install from a notebook	True	26.867	8	8	8
9	100%	2.395	2023-07-20	cheat_sheets/image_features.ipynb	Image to features	True	5.887	5	5	5
10	100%	20.676	2023-06-23	city_bike/bike_chicago.ipynb	Chicago	True	24.240	18	18	18
11	100%	30.738	2023-07-20	city_bike/bike_seatle.ipynb	Seattle	True	34.251	16	16	16
12	100%	110.062	2023-07-20	city_bike/business_chicago.ipynb	Chicago	True	113.601	8	8	8
13	100%	13.059	2023-07-20	city_bike/city_bike_challenge.ipynb	City Bike Challenge	True	16.563	7	7	7
14	100%	18.156	2023-06-23	city_bike/city_bike_solution.ipynb	Ideas on City Bike Challenge	True	21.703	25	25	25
15	100%	19.032	2023-06-23	city_bike/city_bike_solution_cluster.ipynb	Bike Pattern	True	22.606	30	30	30
16	100%	19.829	2023-06-23	city_bike/city_bike_solution_cluster_start.ipynb	Bike Pattern 2	True	23.415	36	36	36
17	100%	27.477	2023-06-23	city_bike/city_bike_views.ipynb	City Bike Views	True	31.223	23	23	23
18	100%	31.208	2023-06-23	city_tour/city_tour_1.ipynb	Shortest city tour	True	34.784	15	15	15
19	100%	30.318	2023-06-23	city_tour/city_tour_1_solution.ipynb	Shortest city tour (solution)	True	33.827	7	7	7
20	100%	33.079	2023-06-23	city_tour/city_tour_data_preparation.ipynb	Walk through all streets in a city	True	37.015	18	18	18
21	100%	38.507	2023-06-23	city_tour/city_tour_long.ipynb	Longer city tours	True	42.236	6	6	6
22	100%	132.139	2023-06-23	city_tour/city_tour_long_solution.ipynb	Longer city tours (solution)	True	135.766	12	12	12
23	100%	0.101	2023-07-20	coding_problems/dices_sequence.ipynb	Dés en séquences	True	3.558	2	2	2
24	0%	nan		hackathon_2015/database_schemas.ipynb	Database Schemas		nan	58	0
25	60%	4.136	2023-07-20	hackathon_2015/download_data_azure.ipynb	Download data from Azure	True	7.630	10	6	6
26	0%	nan		hackathon_2015/process_clean_files.ipynb	Clean, process dates in text files		nan	13	0
27	0%	nan		hackathon_2015/times_series.ipynb	Times Series		nan	27	0
28	0%	nan		hackathon_2015/upload_donnees.ipynb	Upload data		nan	23	0
29	0%	nan		hackathon_2018/baseline_images_keras.ipynb	Exemple pour reconnaissance des inondations		nan	23	0
30	0%	nan		hackathon_2018/donnees_insee.ipynb	Données INSEE		nan	26	0
31	0%	nan		hackathon_2018/images_dups.ipynb	Image et doublons		nan	40	0
32	0%	nan		hackathon_2018/images_gets.ipynb	Récupération d’images avec Bing		nan	28	0
33	0%	nan		hackathon_2022/traitement_du_son.ipynb	Son		nan	25	0
34	100%	68.522	2023-07-20	knn_kdtree/nearest_neighbours_sparse_features.ipynb	Nearest Neighbours and Sparse Features	True	72.006	12	12	12
35	100%	10.999	2023-06-23	mlexamples/PCA.ipynb	PCA (Principal Component Analysis)	True	14.963	31	31	31
36	100%	642.394	2023-06-23	mlexamples/online_news_popylarity.ipynb	OnlineNewPopularity (data from UCI)	True	649.569	44	44	44
37	0%	nan		velib/velib_trajectories.ipynb	2A.ml - Déterminer la vitesse moyenne des vélib		nan	14	0