fmasanori

#feito por https://twitter.com/guilhermeslcs
class Professor:
    def __init__(self, nome, instituto, funcao, salario):
        self.nome = nome
        self.instituto = instituto
        self.funcao = funcao
        self.salario = salario


f = open("salarios.txt", "r")

fmasanori

"""
Autores:
Tiago Henrique da Cruz Pereira
João Felipe de Moraes Borges
"""

import threading
import time
import os
from urllib.request import urlopen

fmasanori

n = 908007000
print (len(str(n)) - len(str(int(str(n)[::-1]))))
print (len(str(n)) - len(str(n).rstrip('0')))
from itertools import takewhile
l = str(n).split('0')
print(len(list(takewhile(lambda x: not x, reversed(l)))))
import re
print(len(re.findall('0*$',str(n))[0]))
f = lambda m: (lambda g: g(m, g))(lambda n, f: 1 + f(n // 10, f) if n % 10 == 0 else 0)
print (f(n))

fmasanori

s1 = 'abacate'
s2 = 'palmeiras'
f1 = len(s1)
f2 = len(s2)
s = ''
for k in range(min(f1, f2)):
  s += s1[k]
  s += s2[k]
k += 1
s += s1[k:] if f1 > f2 else s2[k:]

fmasanori

n = int(input('N: '))
primo = True
for k in range(2, n):
  if n % k == 0:
    primo = False
    break
if primo: print ('Primo')

fmasanori

from uber_rides.session import Session
from pprint import pprint
session = Session(server_token='sua chave da API')

from uber_rides.client import UberRidesClient
client = UberRidesClient(session)

response = client.get_price_estimates(
    start_latitude=-23.202480,
    start_longitude=-45.903285,

fmasanori

import requests
import json

url = 'http://swapi.co/api/species/'
for d in range(1, 5):
  d = '' if d == 1 else '?page='+str(d)
  res = requests.get(url+str(d))
  res = json.loads(res.content.decode('utf-8'))
  for specie in res['results']:
    print (specie['name'], specie['classification'],

fmasanori

#baseado em https://blogs.worldbank.org/opendata/accessing-world-bank-data-apis-python-r-ruby-stata
import wbdata
import matplotlib.pyplot as plt
 
#set up the countries I want
countries = ["CL","UY","BR"]
 
#set up the indicator I want (just build up the dict if you want more than one)
indicators = {'NY.GNP.PCAP.CD':'GNI per Capita'}
 

fmasanori

def enumerações(items):
    n = len(items)
    s = [0]*(n+1)
    k = 0
    while True:
        if s[k] < n:
            s[k+1] = s[k] + 1
            k += 1
        else:
            s[k-1] += 1

fmasanori

from pdfminer.pdfinterp import PDFResourceManager, process_pdf
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams
from io import StringIO
from io import open

from urllib.request import urlopen
from bs4 import BeautifulSoup
import csv