sergiolucero

from diagrams import Diagram, Cluster
from diagrams.aws.compute import EC2
from diagrams.aws.network import ELB

with Diagram("Simple Website Diagram", direction='TB') as diag: 
    API = ELB("API")
    piton = ELB("Python")
    with Cluster("Webserver Cluster"):
        svc_group = [EC2("Spotify"),EC2("Shazam!"),EC2("YouTube"),EC2("REWIND!")]
    API >> piton >> svc_group

sergiolucero

from bs4 import BeautifulSoup
import spotipy, requests
from spotipy.oauth2 import SpotifyClientCredentials

def get_cuerda(artista, cancion):
  martista = artista.lower().replace(' ','_')
  mcancion = cancion.lower().replace(' ','_')  
  url = f'https://acordes.lacuerda.net/{martista}/{mcancion}'
  
  bs = BeautifulSoup(requests.get(url).text, 'lxml')

sergiolucero

from goblet import Goblet, goblet_entrypoint

app = Goblet(function_name="goblet_example")
goblet_entrypoint(app)

@app.route('/home')
def home():
    return {"hello": "world"}

sergiolucero

from numba import jit

@jit(nopython=True)
def psum(p):
    acc = 0
    for i in range(1,1000000):
        acc += pow(i, -p)
    return acc

import time

sergiolucero

import pickle, requests, time
from bs4 import BeautifulSoup
import pandas as pd

def tit(html):
    tt0=BeautifulSoup(html,'lxml').find_all('meta', attrs={'property': 'og:title'})[0]
    return tt0['content']
    
def apo(html):
    idx = html.index('Cuenta con')

sergiolucero

import numpy as np
from operator import itemgetter

LOCAL = 0.5; EMPATE = 0.3; VISITA = 0.2

pos = {'BRA': 33, 'ARG':25, 'ECU':20, 'CHI':16, 'COL':16, 
       'URU': 16, 'PER':14, 'PAR': 12, 'BOL':12 , 'VEN':7          }
rem = [('URU','ARG'),('ARG','BRA'),                  ### CHECK ORDER OF ARG'BRA
           ('BOL','URU'),('VEN','PER'),('COL','PAR'),('ARG','BRA'),('CHI','ECU'),
           ('CHI','ARG'),('COL','PER'),('ECU','BRA'),('PAR','URU'),('VEN','BOL'),

sergiolucero

import time
t0=time.time()
psum=[(p,sum(pow(ix,-p) for ix in range(1,1000000))) for p in range(2,20)]
print(psum, time.time()-t0)     # on my HP (2-20)x1M takes 4.5s

sergiolucero

import glob
from PyPDF2 import PdfFileMerger

merger = PdfFileMerger()
for fn in glob.glob('*.pdf'):
    merger.append(fn)
merger.write('merged.pdf')
merger.close()

sergiolucero

import os, time, pandas as pd
carpeta_salida = 'geocgr'

comunas = pd.read_excel('s3://corporacionciudades/cut_2018_v03.xls')      # esto debiera vivir en una API
print(f'TOTAL comunas: {len(comunas)}')
#fuente: http://www.subdere.gov.cl/sites/default/files/documentos/cut_2018_v03.xls

url_base='https://www.contraloria.cl/opencgrapp/geocgr/api/comunas/%05d/newobras'
t0 = time.time()
sdf = pd.DataFrame()

sergiolucero

import matplotlib.pyplot as plt

conn=sqlite3.connect('revlocal.db')
sqloc = lambda q: pd.read_sql(q, conn)
odf = sqloc('SELECT * FROM stats_v1 ORDER BY Ocupación_Promedio DESC')
#odf=odf[odf.Ocupación_Minima>0];
odf=odf[odf.Ocupación_Máxima<1.20]

fig = plt.figure(figsize=(20,16));odf['cod_hotel']=odf['cod_hotel'].apply(lambda x: x.rstrip())
plt.hlines(y=odf['cod_hotel'],xmin=0, xmax = 100*odf['Ocupación_Promedio'], lw=8, color='cyan') #, color=my_color,linewidth=3, alpha =0.8)