script que usa a Calculadora do Cidadão do BACEN p/ corrigir valores pela inflação

corrige_inflacao.py

import re
import time
import pandas as pd
from random import random
from selenium import webdriver
from selenium.webdriver.chrome.options import Options

# make waits random to make our bot a bit more human-like
def wait(constant = 0.1):
    time.sleep(constant + random())

# make Chrome run in the background
chrome_options = Options()
chrome_options.add_argument("--headless")

# launch Chrome
path_to_chromedriver = '/path/to/chromedriver'
browser = webdriver.Chrome(
    executable_path = path_to_chromedriver, 
    options = chrome_options
    )

# dates and values you want to adjust for inflation
# (more likely you'd get this from some CSV file)
# notice formats: 
#    monthyear, with two-digit months 
#    decimal points included
data = [
    ('011968', 123456), # jan/1968, NCr$ 1234.56
    ('021980', 654321), # feb/1980, Cr$ 6543.21 
    ('041989', 121212), # apr/1984, NCz$ 1212.12
    # ...
]

# final date
t1 = '062020' # i.e., adjust for inflation until jun/2020

# Caculadora do Cidadao
url = 'https://www3.bcb.gov.br/CALCIDADAO/publico/exibirFormCorrecaoValores.do?method=exibirFormCorrecaoValores'

updated_array = []
for tup in data:
    t0, amount = tup

    # go to Calculadora do Cidadao
    browser.get(url)

    # pick an inflation index (here I'm choosing the oldest one)
    dropdown = browser.find_element_by_id('selIndice')
    for option in dropdown.find_elements_by_tag_name('option'):
        if option.text == 'IGP-DI (FGV) - a partir de 02/1944': # change as needed
            option.click()
            break
    wait()

    # enter initial month/year
    browser.find_element_by_name('dataInicial').send_keys(t0)
    wait()

    # enter final month/year
    browser.find_element_by_name('dataFinal').send_keys(t1)
    wait()

    # enter amount to be updated, including two decimal points
    browser.find_element_by_name('valorCorrecao').send_keys(amount)
    wait()

    # run it!
    browser.find_element_by_css_selector("[title^='Corrigir valor']").click()
    wait(2)

    # get inflation-adjusted ibovespa
    updated = browser.find_elements_by_xpath("//*[contains(text(), 'Valor corrigido')]/following-sibling::td")[0].text
    updated = int(re.sub('[^0-9]', '', updated))/100
    updated_array.append((t0, amount/100, updated))
    print(t0, updated)

df = pd.DataFrame(updated_array)
df.columns = ['date', 'amount_t0', 'amount_t1']
df.to_csv('output.csv', index = False)