gabriel19913

from selenium import webdriver
from selenium.webdriver.support.ui import Select
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
import os
import time

def login(browser):
    # Entra com nome de usuário e senha

gabriel19913

[['SOSA4515240', 150, 'https://produto.mercadolivre.com.br/MLB-1104249558-capacete-para-moto-_JM'], ['VEROVEROMOTOS', 254.9, 'https://produto.mercadolivre.com.br/MLB-1103179117-capacete-peels-mirage-storm-_JM'], ['MOTOLOPES.COM.BR', 260, 'https://produto.mercadolivre.com.br/MLB-1010537625-capacete-peels-mirage-storm-preto-rosa-_JM'], ['ROTADOCAPACETE', 264.47, 'https://produto.mercadolivre.com.br/MLB-1135088554-capacete-peels-mirage-original-new-classic-_JM'], ['CENTRALMOTOS17', 266.31, 'https://produto.mercadolivre.com.br/MLB-1114344661-capacete-peels-mirage-storm-preto-vermelho-lancamento-_JM'], ['RACINGMOTOPARTS', 266.31, 'https://produto.mercadolivre.com.br/MLB-1116545168-capacete-peels-mirage-storm-preto-vermelho-lancamento-_JM'], ['MOTOSHRC', 266.31, 'https://produto.mercadolivre.com.br/MLB-1118021626-capacete-peels-mirage-storm-preto-vermelho-lancamento-_JM'], ['WHMOTOS', 266.35, 'https://produto.mercadolivre.com.br/MLB-1030699897-capacete-peels-mirage-storm-preto-e-rosa-feminino-56-58-60-_JM'], ['GALP

gabriel19913

# -*- coding: utf-8 -*-
import scrapy
from netshoes.items import NetshoesItem

class NetshoesspiderSpider(scrapy.Spider):
    name = 'NetshoesSpider'
    allowed_domains = ['netshoes.com.br']
    start_urls = [
        'https://www.netshoes.com.br/busca?nsCat=Natural&q=mirage+storm',
        'https://www.netshoes.com.br/busca?nsCat=Natural&q=mirage+revo',

gabriel19913

# -*- coding: utf-8 -*-
import scrapy
from ponto_frio.items import PontoFrioItem

class PontofrioSpider(scrapy.Spider):
    name = 'PontoFrio'
    allowed_domains = []
    start_urls = ['https://search3.pontofrio.com.br/busca?q=capacete+axxis']

    def parse(self, response):

gabriel19913

rom selenium import webdriver
from bs4 import BeautifulSoup
import csv

browser = webdriver.Chrome('C:\\Users\\100pau\\workspace\\chromedriver.exe')

browser.get('https://www.centauro.com.br/capacete-peels-mirage-storm-preto-e-verde-fosco-m00w4e-mktp.html?cor=34')

html = browser.execute_script("return document.getElementsByTagName('html')[0].innerHTML")
soup = BeautifulSoup(html, 'html.parser')

gabriel19913

%%time
#Importing libraries
import pandas as pd
import json as JSON
from json import load
import numpy as np
import requests
import matplotlib.pyplot as plt
import seaborn as sns
import time

gabriel19913

C1 = np.random.multivariate_normal(np.array([5, 5]), np.array([[1, 0],[0, 1]]), 500)
C2 = np.random.multivariate_normal(np.array([0, 0]), np.array([[1, 0],[0, 1]]), 500)

data1 = pd.DataFrame({'X1':C1[:,0],'X2':C1[:,1]})
data1['Class'] = np.full((500, 1), 'C1')
data2 = pd.DataFrame({'X1':C2[:,0],'X2':C2[:,1]})
data1['Class'] = np.full((500, 1), 'C2')

data = pd.concat([data1, data2], sort=True)

gabriel19913

clear all
clc
x1 = -0.25 + 0.08 * randn(1, 10);
x2 = 0.5 + 0.08 * randn(1, 10);
C1 = [x1;x2];

x1 = 0.7 + 0.08 * randn(1, 10);
x2 = 0.25 + 0.08 * randn(1, 10);
C2 = [x1;x2];

gabriel19913

from sklearn.datasets import load_wine
from sklearn.utils import shuffle
import numpy as np
from sklearn.model_selection import KFold
from sklearn.preprocessing import scale
from sklearn import tree
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score

gabriel19913

# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

conda list -e > requirements.txt #Save all the info about packages to your folder