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import boto3
client = boto3.client('textract')

response = client.analyze_document(
    Document={
        'Bytes': b'bytes',
        'S3Object': {
            'Bucket': 'string',
            'Name': 'string',
            'Version': 'string'

sergiolucero

import datetime, requests
import io, glob, os
import PyPDF2
from bs4 import BeautifulSoup

fecha = datetime.datetime.now().strftime('%Y%m%d')
print('FECHA:', fecha)
root='https://www.mercuriovalpo.cl/impresa/%s/%s/%s/' %(fecha[:4], fecha[4:6], fecha[6:])
base = root + 'papel'

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OBJECTID,ID_CIRCUNSCRIPCION,CIRCUNCRIPCION_ELECTORAL,COD_LOCAL,LOCAL_VOTACION,DIRECCION,Latitud,Longitud,ELECTORES,CUT_PROV,PROVINCIA,CUT_REG,CUT_COM,REGION,COMUNA,CUT_COM_ENT,CUT_REG_ENT,ORD_REG,N_MESAS,CASOS_ACTIVOS,GOOGLE_MAPS
1,589,ALTO HOSPICIO,1036,ANEXO DE COLEGIO SAN ANTONIO DE MATILLA,LOS TAMARUGOS ESQUINA LOS KIWIS,-20.2687,-70.1029,3402,011,Iquique,01,01107,Tarapacá,Alto Hospicio,1107,1,2,10,64.0,"<a href=""https://www.google.cl/maps/dir//-20.2687,-70.1029"" target=_blank"">Ir con Google Maps</a>"
2,589,ALTO HOSPICIO,1025,COLEGIO MARISTA HERMANO FERNANDO,AVENIDA RICARDO LAGOS 4201,-20.2864,-70.0823,6386,011,Iquique,01,01107,Tarapacá,Alto Hospicio,1107,1,2,19,0.0,"<a href=""https://www.google.cl/maps/dir//-20.2864,-70.0823"" target=_blank"">Ir con Google Maps</a>"
3,589,ALTO HOSPICIO,1061,COLEGIO METODISTA WILLIAM TAYLOR,AVENIDA LOS ALAMOS 3025,-20.2678,-70.1044,6405,011,Iquique,01,01107,Tarapacá,Alto Hospicio,1107,1,2,19,64.0,"<a href=""https://www.google.cl/maps/dir//-20.2678,-70.1044"" target=_bl

sergiolucero

import matplotlib.pyplot as plt
import seaborn as sns
import altair as alt
# plot.ly, dash...

fig, ax = plt.subplots(1, figsize=(20,12))

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import glob, fitz, pandas as pd

files = glob.glob('folder/*.pdf')
texts = [' '.join([page.get_text() for page in fitz.open(fn)]) 
          for fn in files]

df = pd.DataFrame(dict(file=files, text=texts))
df['cuerpo'] = df.text.apply(lambda t: remove_headandsentence)
df['fallo'] = df.text.apply(lambda t: extract_fallo)
df.to_csv('sentencias.csv', index=False)

sergiolucero

from tqdm import tqdm
import os
import pickle
import pandas as pd

import torch
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler

from transformers import BertTokenizer, BertForSequenceClassification

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# shape source: https://geoine-ine-chile.opendata.arcgis.com/datasets/186b78e22db44f96a016f9254de44dd4_0
import geopandas as gp

gdf = gp.read_file('MAESTRO_CALLES',init={'epsg:3857'})
gdf = gdf.to_crs('epsg:4326')
gdf.columns=[c.lower() for c in gdf.columns]

for comuna, cdf in gdf.groupby('comuna'):
    ddf = cdf[['comuna','nombre_aux','geometry']]
    ddf.to_file(f'CALLES/{comuna.replace(" ","_")}.json', engine='GeoJSON', single_file=True)

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import pandas as pd
import os, time

DOWNER = 'wget https://cdn.servel.cl/padron/A%05d.pdf'
UPPER = 'aws s3 cp A%05d.pdf s3://quantcldata/CLIENTES/SERVEL/2021/'
DELETE = 'rm A%05d.pdf'

df=pd.read_excel('cut_2018_v03.xls')
comunas=df['Código Comuna 2017']

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import pandas as pd
import joypy
import matplotlib.pyplot as plt

df2 = pd.read_excel('EC_series.xlsx')
df2 = df2[df2.mes==11]  # November
odf=df2[df2.agno<=1950]
df2 = df2[df2.agno>=1980]  # Last 40
# df2.valor -= 23.58   # deviation from the historic November mean

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 <script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1" crossorigin="anonymous"></script>
  <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)