sergiolucero

 <script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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  <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js" integrity="sha384-JjSmVgyd0p3pXB1rRibZUAYoIIy6OrQ6VrjIEaFf/nJGzIxFDsf4x0xIM+B07jRM" crossorigin="anonymous"></script>
  <script type="text/javascript">
    function getAgeFromRUT(rut) {
      var today_date = new Date();
      var slope = 3.3363697569700348e-06
      var intercept = 1932.2573852507373
      var birth_date = rut * slope + intercept
      var birth_date_year = Math.floor(birth_date)

sergiolucero

import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
flights = sns.load_dataset("flights");flights = flights.pivot("month", "year", "passengers")
fig, ax = plt.subplots(1, figsize=(16,8))         
sns.heatmap(flights, cbar=False, annot=True, fmt='.0f', ax=ax)
for ix in range(12):   # one per height
    x = range(12); y = ix+1-flights.iloc[ix]/flights.iloc[ix].max()
    h = plt.plot(x,y, lw=4)
plt.show()

sergiolucero

#/bin/bash
sudo apt-get update
sudo apt-get install -y docker.io
sudo apt-get install -y docker-compose
sudo systemctl start docker
sudo systemctl enable docker
docker --version

sergiolucero

import folium, pandas as pd
from folium.plugins import MarkerCluster
pdf = pd.read_json('https://tinyurl.com/covid19-github')
pdf = pdf[pdf.data==pdf.data.max()]
pdf = pdf[pdf.totale_casi>0]

centroid = pdf.describe()[['lat','long']].loc['50%'].values
fm = folium.Map(location=centroid, zoom_start=6, tiles='stamentoner',
                width=800, height=600)
mc = MarkerCluster()

sergiolucero

import bonobo
from sqlib import get_CAM_data, summarize, to_excel

def Extracción():
    data = get_CAM_data()
    print([(key,len(kdata)) for key, kdata in data.items()])
    yield data
    
def Transformación():
    data['summary'] = summarize(data)   # compute 2019/2020 stats

sergiolucero

from sqlib import sql     # acceso a BD local
import seaborn as sns
import matplotlib.pyplot as plt

def heatmap(some_counts, title, value_var='N', annot_fontsize=24):
    fig, ax = plt.subplots(1, figsize=(24,12))
    print(some_counts.columns)
    pivot = some_counts.pivot_table(index='producto', columns='AñoMes', values=value_var)
    p=sns.heatmap(pivot, annot=True, annot_kws={'size': annot_fontsize, 
                                                'weight': 'bold'}, fmt='.0f', cmap='jet')

sergiolucero

#import os 
#ticket = os.getenv('TOKEN_MP')
from creds import ticket
WAIT = 2;   # segundos entre una y otra solicitud

def get_OC(oc):
    BASE_MP = 'http://api.mercadopublico.cl/servicios/v1/publico/'
    url = f'{BASE_MP}/ordenesdecompra.json?codigo={oc}&ticket={ticket}'
    js = requests.get(url).json()['Listado']    
    return js

sergiolucero

import pandas as pd
import requests, time, os

TOKEN = os.getenv('TOKEN_MP')
WAIT = 2   # this seems reasonable, 1 second does not work

url = 's3://quantcldata/CLIENTS/ObservatorioFiscal/abril2020.csv'
df = pd.read_csv(url)

get_prod = lambda d: 'camilla' if 'camilla' in d else 'mascarilla' if 'mascarilla' in d else 'alcohol' if 'alcohol gel' in d else 'Otros'

sergiolucero

import json, pandas as pd
df = pd.read_json('twitter_premium_api_Lemebel.json', orient = 'records', lines = True)
df.head()

sergiolucero

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv('mobility.csv', encoding='latin-1')   # this comes from Google Takeout in JSON format
fig, ax = plt.subplots(1, figsize=(24,18))
p = sns.heatmap(df[df.año>2013].pivot_table(index='año', columns='mes', values='distancia'),
           annot=True, annot_kws = {'size':14, 'weight': 'bold'}, fmt='.1f', cmap='brg')
_ = p.set_title('Kilómetros recorridos por Sergio Lucero al mes (según Google Maps)', size=24)
plt.savefig('mobility.png', dpi=720)