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import folium
import pandas as pd
import requests

blc = pd.read_json('https://www.bicilascondes.cl/availability_map/getJsonObject')    # BiciLasCondes

js = requests.get('https://api.citybik.es/v2/networks/santiago').json()              # BikeSantiago
bs = pd.DataFrame(js['network']['stations']).rename(columns={'latitude':'lat','longitude':'lon'})        

def get_mobike(lat,lon):

sergiolucero

{
    "nodes": [
        {"id": "J_Pizarro", "group": 1}, 
        {"id": "P_Walker", "group": 1}, 
        {"id": "B_Prokurica", "group": 8}, 
        {"id": "J_Ossandón", "group": 8}, 
        {"id": "R_Lagos", "group": 5}, 
        {"id": "J_García", "group": 8}, 
        {"id": "C_Bianchi", "group": 7}, 
        {"id": "A_Zaldívar", "group": 1}, 

sergiolucero

import folium, pandas as pd
blc = pd.read_json('https://www.bicilascondes.cl/availability_map/getJsonObject')
blc = blc.drop(['address','addressNumber','district','zip'], axis=1)
m = folium.Map(location=[blc.lat.mean(),blc.lon.mean()], zoom_start=14)
bikes = lambda row: 'red' if row['bikes']==0 else 'purple' if row['bikes']<5 else 'green'
for id, station_data in blc.iterrows():
    folium.Marker((station_data['lat'],station_data['lon']), 
                    icon=folium.Icon(color=bikes(station_data), icon='bicycle'),
                    popup=station_data.to_frame().to_html(header=False,justify='center')).add_to(m)
m.save('bicis.html')

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import geopandas
import pandas as pd
from shapely.geometry import Point

gdf = gp.read_file('http://quant.cl/static/DATA/GEO/comunas13.json')
rand = pd.np.random.randn

points=[Point((-70.7+0.1*rand(),-33.4+0.1*rand())) for _ in range(10)]
for point in points:
  for cid,comuna in gdf.iterrows():

sergiolucero

# for every city, we fetch and plot data from all available parameters
import openaq
import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style="white", palette='muted', font_scale=1.35, color_codes=True)
api = openaq.OpenAQ()
def get_city_data(city='Santiago'):
    df = api.measurements(city=city, limit=10000, df=True)
    df = df.query("value >= 0.0")  # clean up the data by removing values below 0

sergiolucero

import requests, sys
from bs4 import BeautifulSoup
from docx import Document

ubs = lambda url:BeautifulSoup(requests.get(url).text,'html5lib')

def get_chords(artist = 'Manu Chao'):
    
    fartist = '_'.join(s.lower() for s in artist.split()) # use map
    url = f'https://acordes.lacuerda.net/{fartist}/'

sergiolucero

import requests
from bs4 import BeautifulSoup
from operator import methodcaller

url_bs = lambda url: BeautifulSoup(requests.get(url).text,'html5lib')  # magic!

def recopila_acordes(artista = 'Manu Chao'):       # "Manu Chao" -> manu_chao
    fartist = '_'.join(map(methodcaller("lower"),artista.split()))   
    url = f'https://acordes.lacuerda.net/{fartist}/'
    

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from docx import Document       

def compilar_cancionero(artista):
    
    data_canciones = recopila_acordes(artista)
    
    document = Document()
    document.add_heading(artista, 0)

    for songname, song_chords in data_canciones.items():

sergiolucero

import pandas as pd
import folium, requests, time

fm=folium.Map([-33.4,-70.65], zoom_start=12)

#LINEAS = ['101','206', '345']

recorridos = eval(requests.get('http://www.transantiago.cl/restservice/rest/getservicios/all').text)

LINEAS = recorridos #[:10]

sergiolucero

import requests
import pandas as pd
from bs4 import BeautifulSoup
# IDEAS: multas, elencos: http://es.teleserieschile.wikia.com/wiki/Categor%C3%ADa:Teleseries_de_Canal_13

bs=BeautifulSoup(requests.get('https://www.cntv.cl/cntv/site/tax/port/all/taxport_16___1.html').text,'html5')
links = bs.find_all('a')
denuncias=[l for l in links if 'Lo más' in l.text]
droot='https://www.cntv.cl'
df=pd.DataFrame()