Lab 2 - Web ScrapingBackend Layer (refers to data access and manipulation) :Using BeautifulSoup,...

Question

Lab 2 - Web ScrapingBackend Layer (refers to data access and manipulation) :Using BeautifulSoup, scrape this website:"https://en.wikipedia.org/wiki/List_of_countries_by_carbon_dioxide_emissions"Scrape the "List of countries by carbon dioxide emissions" for the data. Store the scraped data in an object from your SQLite database class from lab1.Frontend Layer (refers to the user interface):Using the database passed from the backend, sort the data by the Fossil CO2 Emissions 2017 (% of the world) column. Extract the top 10 countries' data, and plot them in a pie-graph using your Graphics module from lab1.I attach lab 1 as well

Vicky · Accepted Answer

# import libraries
from bs4 import BeautifulSoup
import pandas as pd
import requests
# Extract html page content from url
url = "https://en.wikipedia.org/wiki/List_of_countries_by_carbon_dioxide_emissions"
content = requests.get(url)
soup = BeautifulSoup(content.text, "html.parser")
# Create list of all the columns of table
Countries=[]
one990 = []
two005 = []
two017 = []
two017world= []
two017vs1990 = []
perlandarea = []
percapita = []
totalincluding= []
totalexcluding = []
# Find all the elements of table
job = soup.find('table',attrs={'class':'wikitable sortable','style':'text-align:right'})
job_elems = job.find_all("td")
# Add table data to lists
for i in range(len(job_elems)):
    if(i%10==0):
        Countries.append(job_elems[i].text[1:])
    elif(i%10==1):
        one990.

Lab 2 - Web Scraping Backend Layer (refers to data access and manipulation) : Using BeautifulSoup, scrape this website: "https://en.wikipedia.org/wiki/List_of_countries_by_carbon_dioxide_emissions"...

Solution

Answer To This Question Is Available To Download

Related Questions & Answers

Submit New Assignment