Notebooks Coverage

Report on last executions.

60% 2019-02-19

_images/nbcov-2019-02-19.png
index coverage exe time last execution name title success time nb cells nb runs nb valid
0 0% nan   api_rest/rest_api_search_images.ipynb Search engines for images through a REST API   nan 24 0  
1 100% 0.684 2019-02-19 cheat_sheets/chsh_dates.ipynb Cheat Sheet on dates True 2.055 9 9 9
2 100% 10.143 2019-02-12 cheat_sheets/chsh_files.ipynb Cheat Sheet on files True 12.268 21 21 21
3 100% 2.755 2019-02-19 cheat_sheets/chsh_geo.ipynb Cheat sheet on Geocoordinates True 4.052 10 10 10
4 100% 3.451 2019-02-19 cheat_sheets/chsh_graphs.ipynb Cheat Sheet on Graphs True 5.054 8 8 8
5 100% 1.453 2019-02-19 cheat_sheets/chsh_html.ipynb Cheat Sheet on HTML True 3.032 12 12 12
6 100% 1.527 2019-02-19 cheat_sheets/chsh_images.ipynb Images and matrices True 3.052 18 18 18
7 100% 1.736 2019-02-19 cheat_sheets/chsh_pandas.ipynb Uncommon operation with dataframes True 3.037 10 10 10
8 100% 3.531 2019-02-19 cheat_sheets/chsh_pip_install.ipynb Pip install from a notebook True 5.245 8 8 8
9 100% 1.738 2019-02-19 cheat_sheets/image_features.ipynb Image to features True 3.053 5 5 5
10 100% 31.141 2019-02-12 city_bike/bike_chicago.ipynb Chicago True 33.151 18 18 18
11 100% 9.083 2019-02-19 city_bike/bike_seatle.ipynb Seattle True 11.048 16 16 16
12 100% 11.698 2019-02-19 city_bike/business_chicago.ipynb Chicago True 13.050 8 8 8
13 100% 13.577 2019-02-19 city_bike/city_bike_challenge.ipynb City Bike Challenge True 15.598 7 7 7
14 100% 36.910 2019-02-12 city_bike/city_bike_solution.ipynb Ideas on City Bike Challenge True 38.178 25 25 25
15 100% 62.800 2019-02-12 city_bike/city_bike_solution_cluster.ipynb Bike Pattern True 64.266 30 30 30
16 100% 125.328 2019-02-12 city_bike/city_bike_solution_cluster_start.ipynb Bike Pattern 2 True 126.274 36 36 36
17 100% 136.101 2019-02-12 city_bike/city_bike_views.ipynb City Bike Views True 137.259 23 23 23
18 100% 5.703 2019-02-12 city_tour/city_tour_1.ipynb Shortest city tour True 7.086 15 15 15
19 100% 5.034 2019-02-12 city_tour/city_tour_1_solution.ipynb Shortest city tour (solution) True 6.058 7 7 7
20 100% 5.578 2019-02-12 city_tour/city_tour_data_preparation.ipynb Walk through all streets in a city True 7.087 18 18 18
21 100% 33.568 2019-02-12 city_tour/city_tour_long.ipynb Longer city tours True 35.148 6 6 6
22 100% 50.079 2019-02-12 city_tour/city_tour_long_solution.ipynb Longer city tours (solution) True 52.249 12 12 12
23 100% 0.106 2019-02-19 coding_problems/dices_sequence.ipynb Dés en séquences True 2.525 2 2 2
24 0% nan   hackathon_2015/database_schemas.ipynb Database Schemas   nan 58 0  
25 60% 2.032 2019-02-19 hackathon_2015/download_data_azure.ipynb Download data from Azure True 4.080 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  
33 100% 29.713 2019-02-19 knn_kdtree/nearest_neighbours_sparse_features.ipynb Nearest Neighbours and Sparse Features True 31.088 12 12 12
34 100% 5.067 2019-02-12 mlexamples/PCA.ipynb PCA (Principal Component Analysis) True 6.652 31 31 31
35 100% 226.128 2019-02-12 mlexamples/online_news_popylarity.ipynb OnlineNewPopularity (data from UCI) True 227.878 42 42 42
36 0% nan   velib/velib_trajectories.ipynb 2A.ml - Déterminer la vitesse moyenne des vélib   nan 14 0  
_images/nbcov.png