Dennis Munderu

In [3]: from bs4 import BeautifulSoup import requests In [12]: url = "https://en.wikipedia.org/wiki/List_of_largest_companies_by_revenue" In [211… pages = requests.get(url) print(pages) In [96]: soup = BeautifulSoup(pages.text,'html') In [210… print(soup.prettify()) Extracting column details In [212… our_table = soup.find_all('table')[0] print(our_table) In [213… col_names = our_table.find('tr') print(col_names) In [214… col_name = col_names.find_all('th') print(col_name) In [136… column_names = [col.text.strip() for col in col_name] print(column_names) ['Rank', 'Name', 'Industry', 'Revenue', 'Profit', 'Employees', 'Headquarters[note 1]', 'State-owned', 'Ref.', 'Revenue per worker'] In [ ]: In [ ]: setting our DataFrame In [121… import pandas as pd In [203… df = pd.DataFrame(columns = column_names) df Rank Out[203]: Name Industry Revenue Profit Employees Headquarters[note 1] State-owned Ref. Revenue per worker Scraping for Data to fill our df.columns() In [204… df.drop('Rank', axis= 1, inplace= True) df Name Out[204]: Industry Revenue Profit Employees #will add Rank later as it has diff input Headquarters[note 1] In [215… our_data = our_table.find_all('tr')[2:] print(our_data) In [205… for trs in our_data: raw_data = trs.find_all('td') data_names = [tds.text.strip() for tds in raw_data] length= len(df) df.loc[length] = data_names In [206… df.shape Out[206]: State-owned Ref. Revenue per worker (50, 9) In [190… # Getting the Ranks In [216… rank_data = our_table.find_all('th')[11:] print(rank_data) In [197… data_list = [dat.text.strip() for dat in rank_data] print(data_list) ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '3 2', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50'] In [217… df.insert(0, 'Rank', data_list) df.head() Rank Name Industry Revenue Profit Employees Headquarters[note 1] 0 1 Walmart Retail $611,289 $11,680 2,100,000 1 2 Saudi Aramco Oil and gas $603,651 $159,069 2 3 Amazon Retail $574,785 3 4 State Grid Corporation of China Electricity 4 5 Vitol Commodities Out[217]: Ref. Revenue per worker United States [1] $291,090.00 70,496 Saudi Arabia [4] $8,562,911.37 $30,425 1,525,000 United States [5] $376,908 $530,009 $8,192 870,287 China [6] $609,004.85 $505,000 $15,000 1,560 Switzerland [7][8] $323,717,948.72 Exporting our scrapped data as a .csv file In [209… In [ ]: df.to_csv('scrapped DataFrame.csv', index= False) State-owned