Notebooks Coverage

Report on last executions.

60% 2019-06-11

_images/nbcov-2019-06-11.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%

0.601

2019-06-11

cheat_sheets/chsh_dates.ipynb

Cheat Sheet on dates

True

3.049

9

9

9

2

100%

5.225

2019-05-28

cheat_sheets/chsh_files.ipynb

Cheat Sheet on files

True

7.058

21

21

21

3

100%

2.769

2019-06-11

cheat_sheets/chsh_geo.ipynb

Cheat sheet on Geocoordinates

True

5.091

10

10

10

4

100%

2.148

2019-06-11

cheat_sheets/chsh_graphs.ipynb

Cheat Sheet on Graphs

True

5.062

8

8

8

5

100%

1.555

2019-06-11

cheat_sheets/chsh_html.ipynb

Cheat Sheet on HTML

True

4.180

12

12

12

6

100%

1.401

2019-06-11

cheat_sheets/chsh_images.ipynb

Images and matrices

True

4.095

18

18

18

7

100%

1.728

2019-06-11

cheat_sheets/chsh_pandas.ipynb

Uncommon operation with dataframes

True

5.611

10

10

10

8

100%

6.260

2019-06-11

cheat_sheets/chsh_pip_install.ipynb

Pip install from a notebook

True

10.095

8

8

8

9

100%

0.557

2019-06-11

cheat_sheets/image_features.ipynb

Image to features

True

3.044

5

5

5

10

100%

32.144

2019-06-05

city_bike/bike_chicago.ipynb

Chicago

True

34.122

18

18

18

11

100%

12.960

2019-06-11

city_bike/bike_seatle.ipynb

Seattle

True

15.457

16

16

16

12

100%

9.542

2019-06-11

city_bike/business_chicago.ipynb

Chicago

True

13.547

8

8

8

13

100%

35.758

2019-06-11

city_bike/city_bike_challenge.ipynb

City Bike Challenge

True

40.325

7

7

7

14

100%

39.766

2019-06-05

city_bike/city_bike_solution.ipynb

Ideas on City Bike Challenge

True

42.133

25

25

25

15

100%

61.218

2019-06-05

city_bike/city_bike_solution_cluster.ipynb

Bike Pattern

True

64.677

30

30

30

16

100%

122.140

2019-06-05

city_bike/city_bike_solution_cluster_start.ipynb

Bike Pattern 2

True

124.237

36

36

36

17

100%

136.096

2019-06-05

city_bike/city_bike_views.ipynb

City Bike Views

True

138.265

23

23

23

18

100%

6.159

2019-06-04

city_tour/city_tour_1.ipynb

Shortest city tour

True

8.087

15

15

15

19

100%

5.421

2019-06-04

city_tour/city_tour_1_solution.ipynb

Shortest city tour (solution)

True

8.079

7

7

7

20

100%

6.142

2019-06-04

city_tour/city_tour_data_preparation.ipynb

Walk through all streets in a city

True

8.086

18

18

18

21

100%

37.452

2019-06-04

city_tour/city_tour_long.ipynb

Longer city tours

True

40.141

6

6

6

22

100%

53.658

2019-06-04

city_tour/city_tour_long_solution.ipynb

Longer city tours (solution)

True

56.191

12

12

12

23

100%

0.099

2019-06-11

coding_problems/dices_sequence.ipynb

Dés en séquences

True

3.124

2

2

2

24

0%

nan

hackathon_2015/database_schemas.ipynb

Database Schemas

nan

58

0

25

60%

2.206

2019-06-11

hackathon_2015/download_data_azure.ipynb

Download data from Azure

True

5.086

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%

50.111

2019-06-11

knn_kdtree/nearest_neighbours_sparse_features.ipynb

Nearest Neighbours and Sparse Features

True

53.592

12

12

12

34

100%

6.260

2019-06-04

mlexamples/PCA.ipynb

PCA (Principal Component Analysis)

True

8.226

31

31

31

35

100%

258.977

2019-06-05

mlexamples/online_news_popylarity.ipynb

OnlineNewPopularity (data from UCI)

True

261.439

42

42

42

36

0%

nan

velib/velib_trajectories.ipynb

2A.ml - Déterminer la vitesse moyenne des vélib

nan

14

0

_images/nbcov.png