Table Of Contents
Table Of Contents

Notebooks Coverage

Report on last executions.

17% 2018-12-11

_images/nbcov-2018-12-11.png
index coverage exe time last execution name title success time nb cells nb runs nb valid
0 100% 0.815 2018-12-11 cheat_sheets/chsh_dates.ipynb Cheat Sheet on dates True 3.091 9 9 9
1 0% nan   cheat_sheets/chsh_files.ipynb Cheat Sheet on files   nan 27 0  
2 100% 6.914 2018-12-11 cheat_sheets/chsh_geo.ipynb Cheat sheet on Geocoordinates True 9.116 10 10 10
3 100% 8.732 2018-12-11 cheat_sheets/chsh_graphs.ipynb Cheat Sheet on Graphs True 12.110 8 8 8
4 100% 2.694 2018-12-11 cheat_sheets/chsh_html.ipynb Cheat Sheet on HTML True 10.174 12 12 12
5 100% 2.187 2018-12-11 cheat_sheets/chsh_images.ipynb Images and matrices True 5.366 18 18 18
6 100% 5.051 2018-12-11 cheat_sheets/chsh_pandas.ipynb Uncommon operation with dataframes True 7.108 10 10 10
7 100% 13.255 2018-12-11 cheat_sheets/chsh_pip_install.ipynb Pip install from a notebook True 16.298 8 8 8
8 100% 5.319 2018-12-11 cheat_sheets/image_features.ipynb Image to features True 8.103 5 5 5
9 0% nan   city_bike/bike_chicago.ipynb Chicago   nan 22 0  
10 100% 46.075 2018-12-11 city_bike/bike_seatle.ipynb Seattle True 49.163 16 16 16
11 100% 20.235 2018-12-11 city_bike/business_chicago.ipynb Chicago True 24.242 8 8 8
12 100% 31.035 2018-12-11 city_bike/city_bike_challenge.ipynb City Bike Challenge True 33.229 7 7 7
13 0% nan   city_bike/city_bike_solution.ipynb Ideas on City Bike Challenge   nan 36 0  
14 0% nan   city_bike/city_bike_solution_cluster.ipynb Bike Pattern   nan 44 0  
15 0% nan   city_bike/city_bike_solution_cluster_start.ipynb Bike Pattern 2   nan 51 0  
16 0% nan   city_bike/city_bike_views.ipynb City Bike Views   nan 31 0  
17 0% nan   city_tour/city_tour_1.ipynb Shortest city tour   nan 27 0  
18 0% nan   city_tour/city_tour_1_solution.ipynb Shortest city tour (solution)   nan 11 0  
19 0% nan   city_tour/city_tour_data_preparation.ipynb Walk through all streets in a city   nan 26 0  
20 0% nan   city_tour/city_tour_long.ipynb Longer city tours   nan 10 0  
21 0% nan   city_tour/city_tour_long_solution.ipynb Longer city tours (solution)   nan 16 0  
22 100% 0.162 2018-12-11 coding_problems/dices_sequence.ipynb Dés en séquences True 3.091 2 2 2
23 0% nan   hackathon_2015/database_schemas.ipynb Database Schemas   nan 58 0  
24 60% 5.911 2018-12-11 hackathon_2015/download_data_azure.ipynb Download data from Azure True 8.152 10 6 6
25 0% nan   hackathon_2015/process_clean_files.ipynb Clean, process dates in text files   nan 13 0  
26 0% nan   hackathon_2015/times_series.ipynb Times Series   nan 27 0  
27 0% nan   hackathon_2015/upload_donnees.ipynb Upload data   nan 23 0  
28 0% nan   hackathon_2018/baseline_images_keras.ipynb Exemple pour reconnaissance des inondations   nan 23 0  
29 0% nan   hackathon_2018/donnees_insee.ipynb Données INSEE   nan 26 0  
30 0% nan   hackathon_2018/images_dups.ipynb Image et doublons   nan 40 0  
31 0% nan   hackathon_2018/images_gets.ipynb Image et doublons   nan 28 0  
32 100% 156.251 2018-12-11 knn_kdtree/nearest_neighbours_sparse_features.ipynb Nearest Neighbours and Sparse Features True 165.724 12 12 12
33 0% nan   mlexamples/PCA.ipynb PCA (Principal Component Analysis)   nan 39 0  
34 0% nan   mlexamples/online_news_popylarity.ipynb OnlineNewPopularity (data from UCI)   nan 52 0  
35 0% nan   velib/velib_trajectories.ipynb 2A.ml - Déterminer la vitesse moyenne des vélib   nan 14 0  
_images/nbcov.png