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 wellfull-py-coccvake-x01t0ecf/full-py/backend.py # -*- coding: utf-8 -*- """ Created on Wed Oct 13 10:44:44 2021 @author: Sathish """ import sqlite3 global year,median,upper,lower year=[] median=[] upper=[] lower=[] conn = sqlite3.connect('tempe.db') print("Opened database successfully") cursor = conn.execute("SELECT YEAR,MEDIAN,UPPER,LOWER from TEMPDATA") for row in cursor:    year.append(row[0])    median.append(row[1])    upper.append(row[2])    lower.append(row[3])         print("YEAR = ", row[0])    print("MEDIAN = ", row[1])    print("UPPER = ", row[2])    print("LOWER = ", row[3], "
") print("Operation done successfully") conn.close() class __ret__():     def __init__(self):         self.year=year         self.median=median         self.upper=upper         self.lower=lower     def getdata(self):              return self.year,self.median,self.upper,self.lower full-py-coccvake-x01t0ecf/full-py/database.py # -*- coding: utf-8 -*- """ Created on Mon Oct 11 14:46:42 2021 @author: Sathish """ import sqlite3 conn = sqlite3.connect('tempe.db') print("Opened database successfully") conn.execute('''CREATE TABLE TEMPDATA          (YEAR INT PRIMARY KEY     NOT NULL,          MEDIAN           FLOAT    NOT NULL,          UPPER            FLOAT     NOT NULL,          LOWER        FLOAT);''') print("Table created successfully") conn.close() y=[] m=[] u=[] lo=[] l1=[] with open('temperature.txt') as f:     lines=f.readlines()    for i in range(1,len(lines)):          l1=lines[i].split()         y.append(int(l1[0]))         m.append(float(l1[1]))         u.append(float(l1[2]))         lo.append(float(l1[3])) import sqlite3 print("Opened database successfully") for i in range(len(lines)-1):     conn = sqlite3.connect('tempe.db')          yy=y[i]     mm=m[i]     uu=u[i]     loo=lo[i]     conn.execute("INSERT INTO TEMPDATA  (YEAR,MEDIAN,UPPER,LOWER) \                     VALUES (?, ?, ?, ?)",(yy, mm, uu, loo))     conn.commit()             conn.close()      print("Records created successfully") full-py-coccvake-x01t0ecf/full-py/front.py from tkinter import * import numpy as np import matplotlib.pyplot as plt import backend from graph import graph from graph import graph1 from graph import graph2 global year,median,upper,lower # Created a class object object1 = backend.__ret__() year,median,upper,lower=object1.getdata() #Create an instance of Tkinter frame win= Tk() #Set the geometry of tkinter frame win.geometry("100x250") def g1():     graph(year,median,upper,lower) def g2():     graph1(year,median,upper,lower) def g3():     graph2(year,median,upper,lower) #Create a button to show the plot Button(win, text= "X-Y Plot", command= g1).pack(pady=20) Button(win, text= "Bar Graph", command= g2).pack(pady=20) Button(win, text= "Linear Regression", command= g3).pack(pady=20) win.mainloop() full-py-coccvake-x01t0ecf/full-py/graph.py # -*- coding: utf-8 -*- """ Created on Wed Oct 13 10:57:25 2021 @author: Sathish """ import numpy as np import matplotlib.pyplot as plt def graph(year,median,upper,lower):    plt.figure()    plt.plot(year, upper, label = "Upper") # plotting the line 2 points    plt.plot(year, median, label = "Median")    plt.plot(year, lower, label = "Lower")    # naming the x axis    plt.xlabel('Years') # naming the y axis    plt.ylabel('Temperature in Celsius') # giving a title to my graph    plt.title('Average Temperature Anamoly,Global!')   # show a legend on the plot    plt.legend()     plt.show() def graph1(year,median,upper,lower):    plt.figure()        plt.bar(year, upper, color ='r')    plt.legend('upper')    plt.bar(year, median, color ='g')    plt.legend('median')    plt.bar(year, lower, color ='b')    plt.legend('lower')    # naming the x axis    plt.xlabel('Years') # naming the y axis    plt.ylabel('Temperature in Celsius') # giving a title to my graph    plt.title('Average Temperature Anamoly,Global!')   # show a legend on the plot     plt.show()     from scipy import stats def graph2(year,median,upper,lower):     global slope,intercept     x=year     y=upper 	     slope, intercept, r, p, std_err = stats.linregress(x, y)     def myfunc(x):         return slope * x + intercept     mymodel = list(map(myfunc,x))     plt.figure()     plt.scatter(x, y,color ='g')     plt.plot(x, mymodel,color ='r')     plt.legend('Upper')        # naming the x axis     plt.xlabel('Years')     # naming the y axis     plt.ylabel('Temperature in Celsius for Upper')     # giving a title to my graph     plt.title('Linear Regression Graph!')     plt.show()     global slope1,intercept1     x=year     y=lower 	     slope1, intercept1, r, p, std_err = stats.linregress(x, y)     def myfunc1(x):         return slope1 * x + intercept1     mymodel1 = list(map(myfunc1,x))     plt.figure()     plt.scatter(x, y,color ='g')     plt.plot(x, mymodel1,color ='r')     plt.legend('Lower')        # naming the x axis     plt.xlabel('Years')     # naming the y axis     plt.ylabel('Temperature in Celsius for Lower')     # giving a title to my graph     plt.title('Linear Regression Graph!')     plt.show() 	     global slope2,intercept2     x=year     y=median 	     slope2, intercept2, r, p, std_err = stats.linregress(x, y)     def myfunc2(x):         return slope2 * x + intercept2     mymodel2 = list(map(myfunc2,x))     plt.figure()     plt.scatter(x, y,color ='g')     plt.plot(x, mymodel2,color ='r')     plt.legend('Median')        # naming the x axis     plt.xlabel('Years')     # naming the y axis     plt.ylabel('Temperature in Celsius for MEdian')     # giving a title to my graph     plt.title('Linear Regression Graph!')     plt.show() 	 full-py-coccvake-x01t0ecf/full-py/Temperature.html 		# Average temperature  		# Relative to 1961-1990  		# Celcius 		Year		Median		Upper		Lower 		1850		-0.373		-0.339		-0.425 		1851		-0.218		-0.184		-0.274 		1852		-0.228		-0.196		-0.28 		1853		-0.269		-0.239		-0.321 		1854		-0.248		-0.218		-0.301 		1855		-0.272		-0.241		-0.324 		1856		-0.358		-0.327		-0.413 		1857		-0.461		-0.431		-0.512 		1858		-0.467		-0.435		-0.521 		1859		-0.284		-0.249		-0.34 		1860		-0.343		-0.31		-0.405 		1861		-0.407		-0.356		-0.484 		1862		-0.524		-0.471		-0.597 		1863		-0.278		-0.226		-0.359 		1864		-0.494		-0.448		-0.564 		1865		-0.279		-0.232		-0.358 		1866		-0.251		-0.197		-0.338 		1867		-0.321		-0.265		-0.406 		1868		-0.238		-0.185		-0.326 		1869		-0.262		-0.221		-0.333 		1870		-0.276		-0.239		-0.34 		1871		-0.335		-0.293		-0.404 		1872		-0.227		-0.187		-0.29 		1873		-0.304		-0.258		-0.364 		1874		-0.368		-0.325		-0.435 		1875		-0.395		-0.352		-0.464 		1876		-0.384		-0.335		-0.453 		1877		-0.075		-0.026		-0.146 		1878		0.035		0.084		-0.026 		1879		-0.23		-0.189		-0.291 		1880		-0.227		-0.182		-0.288 		1881		-0.2		-0.157		-0.259 		1882		-0.213		-0.171		-0.274 		1883		-0.296		-0.251		-0.35 		1884		-0.409		-0.373		-0.457 		1885		-0.389		-0.354		-0.444 		1886		-0.367		-0.334		-0.419 		1887		-0.418		-0.382		-0.476 		1888		-0.307		-0.268		-0.359 		1889		-0.171		-0.129		-0.235 		1890		-0.416		-0.369		-0.484 		1891		-0.33		-0.276		-0.408 		1892		-0.455		-0.4		-0.526 		1893		-0.473		-0.416		-0.547 		1894		-0.41		-0.352		-0.487 		1895		-0.39		-0.335		-0.46 		1896		-0.186		-0.136		-0.255 		1897		-0.206		-0.153		-0.273 		1898		-0.412		-0.362		-0.479 		1899		-0.289		-0.231		-0.355 		1900		-0.203		-0.147		-0.272 		1901		-0.259		-0.208		-0.326 		1902		-0.402		-0.355		-0.461 		1903		-0.479		-0.431		-0.543 		1904		-0.52		-0.471		-0.58 		1905		-0.377		-0.326		-0.438 		1906		-0.283		-0.232		-0.338 		1907		-0.465		-0.423		-0.523 		1908		-0.511		-0.468		-0.563 		1909		-0.522		-0.476		-0.576 		1910		-0.49		-0.448		-0.547 		1911		-0.544		-0.498		-0.595 		1912		-0.437		-0.392		-0.486 		1913		-0.424		-0.381		-0.471 		1914		-0.244		-0.197		-0.291 		1915		-0.141		-0.085		-0.202 		1916		-0.383		-0.328		-0.437 		1917		-0.468		-0.413		-0.525 		1918		-0.333		-0.27		-0.396 		1919		-0.275		-0.217		-0.327 		1920		-0.247		-0.198		-0.296 		1921		-0.187		-0.149		-0.237 		1922		-0.302		-0.262		-0.346 		1923		-0.276		-0.237		-0.323 		1924		-0.294		-0.259		-0.34 		1925		-0.215		-0.178		-0.265 		1926		-0.108		-0.07		-0.155 		1927		-0.21		-0.177		-0.253 		1928		-0.206		-0.174		-0.253 		1929		-0.35		-0.32		-0.394 		1930		-0.137		-0.104		-0.181 		1931		-0.087		-0.055		-0.133 		1932		-0.137		-0.107		-0.183 		1933		-0.273		-0.24		-0.32 		1934		-0.131		-0.1		-0.177 		1935		-0.178		-0.15		-0.221 		1936		-0.147		-0.12		-0.186 		1937		-0.026		0.001		-0.064 		1938		-0.006		0.017		-0.044 		1939		-0.052		-0.029		-0.088 		1940		0.014		0.045		-0.018 		1941		0.02		0.053		-0.021 		1942		-0.027		0.015		-0.081 		1943		-0.004		0.036		-0.056 		1944		0.144		0.18		0.094 		1945		0.025		0.059		-0.017 		1946		-0.071		-0.042		-0.113 		1947		-0.038		0.001		-0.098 		1948		-0.039		0		-0.101 		1949		-0.074		-0.036		-0.119 		1950		-0.173		-0.135		-0.216 		1951		-0.052		-0.018		-0.091 		1952		0.028		0.063		-0.01 		1953		0.097		0.132		0.06 		1954		-0.129		-0.093		-0.166 		1955		-0.19		-0.16		-0.222 		1956		-0.267		-0.236		-0.297 		1957		-0.007		0.024		-0.035 		1958		0.046		0.075		0.02 		1959		0.017		0.043		-0.008 		1960		-0.049		-0.023		-0.073 		1961		0.038		0.06		0.009 		1962		0.014		0.034		-0.012 		1963		0.048		0.074		0.022 		1964		-0.223		-0.197		-0.246 		1965		-0.14		-0.113		-0.163 		1966		-0.068		-0.042		-0.09 		1967		-0.074		-0.052		-0.093 		1968		-0.113		-0.097		-0.133 		1969		0.032		0.05		0.014 		1970		-0.027		-0.009		-0.048 		1971		-0.186		-0.163		-0.204 		1972		-0.065		-0.04		-0.08 		1973		0.062		0.082		0.051 		1974		-0.214		-0.197		-0.227 		1975		-0.149		-0.134		-0.163 		1976		-0.241		-0.227		-0.255 		1977		0.047		0.064		0.034 		1978		-0.062		-0.044		-0.077 		1979		0.057		0.076		0.041 		1980		0.092		0.107		0.077 		1981		0.14		0.153		0.125 		1982		0.011		0.03		-0.011 		1983		0.194		0.211		0.174 		1984		-0.014		0.006		-0.033 		1985		-0.03		-0.013		-0.048 		1986		0.045		0.067		0.024 		1987		0.192		0.22		0.171 		1988		0.198		0.221		0.175 		1989		0.118		0.147		0.091 		1990		0.296		0.33		0.267 		1991		0.254		0.289		0.22 		1992		0.105		0.139		0.071 		1993		0.148		0.186		0.112 		1994		0.208		0.247		0.174 		1995		0.325		0.361		0.289 		1996		0.183		0.217		0.146 		1997		0.39		0.425		0.356 		1998		0.539		0.578		0.5 		1999		0.306		0.343		0.265 		2000		0

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"...

Answer To: Lab 2 - Web Scraping Backend Layer (refers to data access and manipulation) : Using BeautifulSoup,...

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