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import requests
import re

# Avoir les liens d'une video youtube.com/watch?v=id
def link(id):
    # Regex utilisé parce que le service de liens ne retourne pas un Json valide et du coup c'est plus rapide
    prog= re.compile('"videoId":"(.*?)"', re.MULTILINE)

    # Service qui donne les liens embarqués dans une vidéo
    req = requests.get('https://www.youtube.com/get_endscreen?v=%s'%id)

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35.812409, 127.1471211;Times Square (Seoul);37.517130, 126.903111
2;Suwon World Cup Stadium;37.286278, 127.036889
3;Suwon World Cup Stadium;37.286278, 127.036889
4;Gwacheon National Science Museum;37.4388, 127.0047
5;Gwacheon National Science Museum;37.4388, 127.0047
6;N Seoul Tower;37.551425, 126.988
7;Sejong Center for the Performing Arts;37.5725, 126.9756
8;Seoul Museum of History, Gyeonghui Palace;37.570475, 126.970658
9;Lotte World;37.51, 127.1
10;National Museum of Contemporary Art;37.430952, 127.019811

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print(sum([int(r) for l,r in zip(CHALLENGE_DATA,CHALLENGE_DATA[len(CHALLENGE_DATA)//2:]+CHALLENGE_DATA[:len(CHALLENGE_DATA)//2]) if l==r]))

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from github import Github
import csv

GITHUB_TOKEN=""
ORGANIZATION=''

# or using an access token
g = Github(GITHUB_TOKEN)
with open('git_network.node.csv','w') as nodes:
    writer_node = csv.DictWriter(nodes,fieldnames=['id','label','type'])

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from keras.applications import *
from keras.utils import plot_model
# [..]
# model = ...
# Get your own model here
# [..]
model  = NASNetMobile() #Example with NASNetMobile
plot_model(model,show_shapes=False, to_file='model.dot')

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from bs4 import BeautifulSoup
import requests
import sys
import uuid
import json

data = {
        'url':sys.argv[1],
        'links': [],
        'images': []

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Timeseries data

Majority of indicators will be "timeseries" (Gini, GDP, temperatures, salary, population, light pollution). Need to store (at least on the raw storage part) the date even if the queryable data will be the "last year". Fortunatelly, that's easier to display / compute as a layer when doing some geo but ....

Right data at Right scale

(Let's say that the application makes us start from a world map and we can 'add layer')

Some scale makes the data hard to valuate. E.g having the exact position of hospitals at country level (even France) might be difficult to interpret. Synthethising the data as a heat map for certain zoom level might help. https://www.utc.fr/ic05/resources/EDA-cours2-3-p2010.pdf (Page 17)

And regrouping data within zoom level might be helpful (as you know at which zoom level you are, you can add / remove geo layer)

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37 - https://www.youtube.com/watch?v=QEbcShAoCss - https://www.imdb.com/title/tt0100758/
36 - https://www.youtube.com/watch?v=zJHWmSa6a30 - https://www.imdb.com/title/tt0275277/
35 - https://www.youtube.com/watch?v=ESKg7nbTmUI - https://www.imdb.com/title/tt0093777/
34 - https://www.youtube.com/watch?v=oigoehRD-VE - https://www.imdb.com/title/tt0065955/
33 - https://www.youtube.com/watch?v=mecSHTQw8x4 - https://www.imdb.com/title/tt0103874/
32 - https://www.youtube.com/watch?v=LmHLedAnkfY - https://www.imdb.com/title/tt0395584/
31 - https://www.youtube.com/watch?v=-DhX8gFGALc - https://www.imdb.com/title/tt0062711/
30 - https://www.youtube.com/watch?v=d71jVWrY4eI - https://www.imdb.com/title/tt0091605/
29 - https://www.youtube.com/watch?v=YRXdDq6h0hI - https://www.imdb.com/title/tt0119567/
28 - https://www.youtube.com/watch?v=Uy8tn_JltNs - https://www.imdb.com/title/tt0109592/

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Chongqing Hongyadong
Zhangjiajie
District de Tai'erzhuang, Zaozhuang, Shandong, Chine
Xiaozhai Tiankeng (小寨天坑)
Huading National Forest Park
敦煌
长白山 天池
九寨沟
China, Island Hainan, city Sanya, Dadonghai beach
shanxi xiguan / School that suffle

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Shirakawa