sergiolucero

import requests, pandas as pd
from bs4 import BeautifulSoup

url_base = 'https://nuevo.jumbo.cl/%s'

def parse(what):
    print(what)
    bs = BeautifulSoup(requests.get(url_base %what).text, 'lxml')
    producto = [pt.text.split(chr(10))[1] for pt in bs.find_all('div',attrs={'class':'product-item__info'})]
    marca = [pt.text.split(chr(10))[2] for pt in bs.find_all('div',attrs={'class':'product-item__info'})]

sergiolucero

import aiohttp
import asyncio
import time, pandas as pd

def async_http_get(urls, extractor=None, json_response=True):
    tasks = []
    sem = asyncio.Semaphore(32)

    async def fetch(session, url):
        async with session.get(url) as response:

sergiolucero

from datetime import datetime
from pytz import timezone

def chile_time():
    scl = timezone('America/Santiago')
    scl_time = datetime.now(scl)
    return scl_time.strftime('%Y-%m-%d %H-%M-%S')

sergiolucero

import pandas as pd
from token import TOKEN   # token personal: http://www.energiaabierta.cne.cl/

TIPOS = ['calefaccion','vehicular']
SOURCE = 'http://api.cne.cl/v3/combustibles/{}/estaciones?token={}'
COPY_VARS = ['nombre_comuna', 'id_region', 'direccion_calle', 'fecha_hora_actualizacion']
for tipo in TIPOS:
    print(tipo)
    df = pd.read_json(SOURCE.format(tipo, TOKEN))
    out = pd.DataFrame()

sergiolucero

from creds import ID,CODE       # isolines: driving from my house

import folium
import requests

home =[-33.406654,-70.572701]     # 085 LOS MILITARES / ALONSO DE CORDOVA
head = 'https://isoline.route.cit.api.here.com/routing/7.2/calculateisoline.json?'
URL_BASE = '{}app_id={}&app_code={}&mode=shortest;car;traffic:disabled&start=geo!{},{}&range={}&rangetype={}'

def isodata(home, range, type):

sergiolucero

#from google.colab import files;uploaded = files.upload()
#!pip install TPOT
from tpot import TPOTRegressor
from sklearn.model_selection import train_test_split
df=pd.read_csv('32928_chile_autos.csv')
df=df[(df.Año>1980)&(df.Kilometraje>1000)&(df.Precio<100000000)&(df.Precio>1000000)]

data_columns = df.columns[2:-1]   # add some more!
target_columns = df.columns[-1]   # use logs!
X,y = df[data_columns], df[target_columns]

sergiolucero

gdf=pysqldf('SELECT semana,Oficina,AVG(Tiempo_Ate_Min) AS TiempoAtencion, \
             AVG(Tiempo_Esp_Min) AS TiempoEspera,\
             COUNT(DISTINCT(Ejecutivo)) AS Personal FROM df GROUP BY Oficina, semana')
atencion = gdf.pivot("Oficina","semana", "TiempoAtencion")
espera = gdf.pivot("Oficina","semana", "TiempoEspera")
ejecutivos = gdf.pivot("Oficina","semana", "Personal")  # on same plot?

f, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(20, 8),sharey=True)
sns.heatmap(atencion, cmap="rainbow",fmt="d",ax=ax1)
sns.heatmap(espera, cmap="rainbow",fmt="d",ax=ax2)

sergiolucero

#!wget https://www.bcn.cl/siit/obtienearchivo?id=repositorio/10221/10396/1/division_comunal.zip
#!mv obtienearchivo\?id\=repositorio%2F10221%2F10396%2F1%2Fdivision_comunal.zip comunal.zip
#!unzip comunal
#!pip install geopandas
import geopandas as gp
df = gp.read_file('division_comunal.shp')
#df.NOM_REG.unique()
rmdf = df[df.NOM_REG=='Región Metropolitana de Santiago']
print('Procesamos %d comunas en la Región Metropolitana' %len(rmdf))
rmdf.to_pickle('rm.pk')   # 780K! upload to quant.cl for reference (just repeat the above)

sergiolucero

BIKE_URL = 'https://api.citybik.es/v2/networks?fields=id,location,href'
HERE_URL = '''https://route.cit.api.here.com/routing/7.2/calculateroute.json?
            app_id=arVngf4Gl3gIU145UGVB&app_code=Pas2aQELd9IynApEQ4m8jg&
            waypoint0=geo!52.5,13.4&waypoint1=geo!52.5,13.45&
            mode=fastest;bicycle'''    # or car

BACKUP = 'https://github.com/sergiolucero/hereapi/blob/master/DATA/bikesystems.csv'

sergiolucero

'use strict';
module.exports.hello = (event, context, callback) => {
    const response = {
          statusCode: 200,
          body: JSON.stringify({
            message: 'hola Sergio!',
          }),
    };
  callback(null, response);
};