Notebooks Coverage

Report on last executions.

60% 2019-04-16

_images/nbcov-2019-04-16.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%

0.786

2019-04-16

cheat_sheets/chsh_dates.ipynb

Cheat Sheet on dates

True

3.066

9

9

9

2

100%

5.621

2019-04-09

cheat_sheets/chsh_files.ipynb

Cheat Sheet on files

True

8.097

21

21

21

3

100%

2.535

2019-04-16

cheat_sheets/chsh_geo.ipynb

Cheat sheet on Geocoordinates

True

5.077

10

10

10

4

100%

1.897

2019-04-16

cheat_sheets/chsh_graphs.ipynb

Cheat Sheet on Graphs

True

4.082

8

8

8

5

100%

1.723

2019-04-16

cheat_sheets/chsh_html.ipynb

Cheat Sheet on HTML

True

4.108

12

12

12

6

100%

1.756

2019-04-16

cheat_sheets/chsh_images.ipynb

Images and matrices

True

4.076

18

18

18

7

100%

1.940

2019-04-16

cheat_sheets/chsh_pandas.ipynb

Uncommon operation with dataframes

True

4.091

10

10

10

8

100%

4.466

2019-04-16

cheat_sheets/chsh_pip_install.ipynb

Pip install from a notebook

True

7.129

8

8

8

9

100%

0.569

2019-04-16

cheat_sheets/image_features.ipynb

Image to features

True

3.079

5

5

5

10

100%

32.844

2019-04-09

city_bike/bike_chicago.ipynb

Chicago

True

35.158

18

18

18

11

100%

9.682

2019-04-16

city_bike/bike_seatle.ipynb

Seattle

True

12.636

16

16

16

12

100%

11.625

2019-04-16

city_bike/business_chicago.ipynb

Chicago

True

14.549

8

8

8

13

100%

10.580

2019-04-16

city_bike/city_bike_challenge.ipynb

City Bike Challenge

True

13.509

7

7

7

14

100%

36.986

2019-04-09

city_bike/city_bike_solution.ipynb

Ideas on City Bike Challenge

True

39.185

25

25

25

15

100%

58.703

2019-04-09

city_bike/city_bike_solution_cluster.ipynb

Bike Pattern

True

61.490

30

30

30

16

100%

120.815

2019-04-09

city_bike/city_bike_solution_cluster_start.ipynb

Bike Pattern 2

True

123.279

36

36

36

17

100%

133.619

2019-04-09

city_bike/city_bike_views.ipynb

City Bike Views

True

136.265

23

23

23

18

100%

6.181

2019-04-09

city_tour/city_tour_1.ipynb

Shortest city tour

True

8.111

15

15

15

19

100%

5.332

2019-04-09

city_tour/city_tour_1_solution.ipynb

Shortest city tour (solution)

True

7.075

7

7

7

20

100%

6.240

2019-04-09

city_tour/city_tour_data_preparation.ipynb

Walk through all streets in a city

True

8.089

18

18

18

21

100%

37.583

2019-04-09

city_tour/city_tour_long.ipynb

Longer city tours

True

40.132

6

6

6

22

100%

51.948

2019-04-09

city_tour/city_tour_long_solution.ipynb

Longer city tours (solution)

True

54.166

12

12

12

23

100%

0.108

2019-04-16

coding_problems/dices_sequence.ipynb

Dés en séquences

True

2.294

2

2

2

24

0%

nan

hackathon_2015/database_schemas.ipynb

Database Schemas

nan

58

0

25

60%

1.424

2019-04-16

hackathon_2015/download_data_azure.ipynb

Download data from Azure

True

4.087

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%

28.280

2019-04-16

knn_kdtree/nearest_neighbours_sparse_features.ipynb

Nearest Neighbours and Sparse Features

True

30.203

12

12

12

34

100%

5.591

2019-04-09

mlexamples/PCA.ipynb

PCA (Principal Component Analysis)

True

8.333

31

31

31

35

100%

225.338

2019-04-09

mlexamples/online_news_popylarity.ipynb

OnlineNewPopularity (data from UCI)

True

227.747

42

42

42

36

0%

nan

velib/velib_trajectories.ipynb

2A.ml - Déterminer la vitesse moyenne des vélib

nan

14

0

_images/nbcov.png