Notebooks Coverage

Report on last executions.

16% 2020-06-02

_images/nbcov-2020-06-02.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%

0.562

2020-06-02

cheat_sheets/chsh_dates.ipynb

Cheat Sheet on dates

True

1.469

9

9

9

2

0%

nan

cheat_sheets/chsh_files.ipynb

Cheat Sheet on files

nan

27

0

3

100%

2.309

2020-06-02

cheat_sheets/chsh_geo.ipynb

Cheat sheet on Geocoordinates

True

3.255

10

10

10

4

100%

1.645

2020-06-02

cheat_sheets/chsh_graphs.ipynb

Cheat Sheet on Graphs

True

2.567

8

8

8

5

100%

1.577

2020-06-02

cheat_sheets/chsh_html.ipynb

Cheat Sheet on HTML

True

2.469

12

12

12

6

100%

1.410

2020-06-02

cheat_sheets/chsh_images.ipynb

Images and matrices

True

2.717

18

18

18

7

100%

1.281

2020-06-02

cheat_sheets/chsh_pandas.ipynb

Uncommon operation with dataframes

True

2.126

10

10

10

8

100%

4.934

2020-06-02

cheat_sheets/chsh_pip_install.ipynb

Pip install from a notebook

True

5.929

8

8

8

9

100%

0.604

2020-06-02

cheat_sheets/image_features.ipynb

Image to features

True

1.477

5

5

5

10

0%

nan

city_bike/bike_chicago.ipynb

Chicago

nan

22

0

11

100%

10.034

2020-06-02

city_bike/bike_seatle.ipynb

Seattle

True

10.915

16

16

16

12

100%

15.156

2020-06-02

city_bike/business_chicago.ipynb

Chicago

True

15.991

8

8

8

13

100%

10.815

2020-06-02

city_bike/city_bike_challenge.ipynb

City Bike Challenge

True

11.764

7

7

7

14

0%

nan

city_bike/city_bike_solution.ipynb

Ideas on City Bike Challenge

nan

36

0

15

0%

nan

city_bike/city_bike_solution_cluster.ipynb

Bike Pattern

nan

44

0

16

0%

nan

city_bike/city_bike_solution_cluster_start.ipynb

Bike Pattern 2

nan

51

0

17

0%

nan

city_bike/city_bike_views.ipynb

City Bike Views

nan

31

0

18

0%

nan

city_tour/city_tour_1.ipynb

Shortest city tour

nan

27

0

19

0%

nan

city_tour/city_tour_1_solution.ipynb

Shortest city tour (solution)

nan

11

0

20

0%

nan

city_tour/city_tour_data_preparation.ipynb

Walk through all streets in a city

nan

26

0

21

0%

nan

city_tour/city_tour_long.ipynb

Longer city tours

nan

10

0

22

0%

nan

city_tour/city_tour_long_solution.ipynb

Longer city tours (solution)

nan

16

0

23

100%

0.105

2020-06-02

coding_problems/dices_sequence.ipynb

Dés en séquences

True

0.985

2

2

2

24

0%

nan

hackathon_2015/database_schemas.ipynb

Database Schemas

nan

58

0

25

60%

1.099

2020-06-02

hackathon_2015/download_data_azure.ipynb

Download data from Azure

True

2.074

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%

16.502

2020-06-02

knn_kdtree/nearest_neighbours_sparse_features.ipynb

Nearest Neighbours and Sparse Features

True

17.411

12

12

12

34

0%

nan

mlexamples/PCA.ipynb

PCA (Principal Component Analysis)

nan

39

0

35

0%

nan

mlexamples/online_news_popylarity.ipynb

OnlineNewPopularity (data from UCI)

nan

52

0

36

0%

nan

velib/velib_trajectories.ipynb

2A.ml - Déterminer la vitesse moyenne des vélib

nan

14

0

_images/nbcov.png