kunalrustagi08

import pandas as pd
col_dict = {'title':book_title, 'price':product_price, 'rating':star_rating}
book_store = pd.DataFrame(col_dict)

print(book_store.head())
print(book_store.dtypes)

book_store['price'] = book_store['price'].apply(lambda x : float(x[2:]))

number_mapping = {'One':1,'Two':2,'Three':3,'Four':4,'Five':5}

kunalrustagi08

import requests
from requests import get
from bs4 import BeautifulSoup
import numpy as np
import time
import random

pages = np.arange(1,51,1)

book_title = []

kunalrustagi08

book_div = soup.find_all('li', class_='col-xs-6 col-sm-4 col-md-3 col-lg-3')

for container in book_div:
    title = container.article.h3.a['title']
    book_title.append(title)

    price = container.article.find('div', class_='product_price').p.text
    product_price.append(price)

    rating = container.article.p['class'][-1]

kunalrustagi08

url = 'http://books.toscrape.com/catalogue/page-' + str(page) + '.html'
results = requests.get(url)

soup = BeautifulSoup(results.text, 'html.parser')

kunalrustagi08

time.sleep(random.randint(1,10))

kunalrustagi08

for page in pages:

kunalrustagi08

book_title = []
star_rating = []
product_price = []

kunalrustagi08

pages = np.arange(1,51,1)

kunalrustagi08

import requests
from requests import get
from bs4 import BeautifulSoup
import numpy as np
import time
import random

kunalrustagi08

import requests
from requests import get
from bs4 import BeautifulSoup

url = 'http://books.toscrape.com/catalogue/page-1.html'

results = requests.get(url)

soup = BeautifulSoup(results.text, 'html.parser')
print(soup.prettify())