Notebooks Coverage#

Report on last executions.

16% 2022-12-01

_images/nbcov-2022-12-01.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%

0.697

2022-12-01

cheat_sheets/chsh_dates.ipynb

Cheat Sheet on dates

True

4.746

9

9

9

2

0%

nan

cheat_sheets/chsh_files.ipynb

Cheat Sheet on files

nan

27

0

3

100%

10.601

2022-12-01

cheat_sheets/chsh_geo.ipynb

Cheat sheet on Geocoordinates

True

14.684

10

10

10

4

100%

5.234

2022-12-01

cheat_sheets/chsh_graphs.ipynb

Cheat Sheet on Graphs

True

9.312

8

8

8

5

100%

2.032

2022-12-01

cheat_sheets/chsh_html.ipynb

Cheat Sheet on HTML

True

6.092

12

12

12

6

100%

1.993

2022-12-01

cheat_sheets/chsh_images.ipynb

Images and matrices

True

6.032

18

18

18

7

100%

3.619

2022-12-01

cheat_sheets/chsh_pandas.ipynb

Uncommon operation with dataframes

True

7.652

10

10

10

8

100%

19.908

2022-12-01

cheat_sheets/chsh_pip_install.ipynb

Pip install from a notebook

True

24.172

8

8

8

9

100%

2.332

2022-12-01

cheat_sheets/image_features.ipynb

Image to features

True

6.383

5

5

5

10

0%

nan

city_bike/bike_chicago.ipynb

Chicago

nan

22

0

11

100%

28.821

2022-12-01

city_bike/bike_seatle.ipynb

Seattle

True

32.899

16

16

16

12

100%

28.645

2022-12-01

city_bike/business_chicago.ipynb

Chicago

True

32.595

8

8

8

13

100%

12.768

2022-12-01

city_bike/city_bike_challenge.ipynb

City Bike Challenge

True

16.872

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%

0.104

2022-12-01

coding_problems/dices_sequence.ipynb

Dés en séquences

True

4.106

2

2

2

24

0%

nan

hackathon_2015/database_schemas.ipynb

Database Schemas

nan

58

0

25

60%

10.055

2022-12-01

hackathon_2015/download_data_azure.ipynb

Download data from Azure

True

14.072

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

0%

nan

hackathon_2022/traitement_du_son.ipynb

Son

nan

25

0

34

100%

25.765

2022-12-01

knn_kdtree/nearest_neighbours_sparse_features.ipynb

Nearest Neighbours and Sparse Features

True

29.834

12

12

12

35

0%

nan

mlexamples/PCA.ipynb

PCA (Principal Component Analysis)

nan

39

0

36

0%

nan

mlexamples/online_news_popylarity.ipynb

OnlineNewPopularity (data from UCI)

nan

56

0

37

0%

nan

velib/velib_trajectories.ipynb

2A.ml - Déterminer la vitesse moyenne des vélib

nan

14

0

_images/nbcov.png