Rahul Banerjee

#Day24 - How to scrape tables and other use cases of Beautiful Soup Part2

#Day24 - How to scrape tables and other use cases of Beautiful Soup Part2

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In yesterday's article, we talked about getting started with Beautiful Soup. We discussed the following functions

  • pretiffy()
  • find()
  • find_all()
  • select() Today we will try to scrape the data in the table of the worldometer website Screen Shot 2021-04-13 at 9.20.54 PM.png

The table has an id "main_table_countries_today". We will use the id to get the table element. Let's talk about the structure of the table

           <td> </td>
           <td> </td>
           <td> </td>

Screen Shot 2021-04-13 at 9.38.59 PM.png

"thead" contains the header row ( "Country,Other" , "Total Cases" , "New Cases" .........) . If this seems confusing, let's start actually scraping the elements and see the output

import requests
from bs4 import BeautifulSoup

html = requests.get("https://www.worldometers.info/coronavirus/").text

soup = BeautifulSoup(html, features= 'html.parser')

table = soup.select("#main_table_countries_today")[0]

headers = table.find("thead").get_text()


Screen Shot 2021-04-13 at 9.54.33 PM.png

We can use the split() function to break the string into a list of elements.

headers = headers.split("\n")
headers = [header for header in headers if header]

['#', 'Country,Other', 'TotalCases', 'NewCases', 'TotalDeaths', 
'NewDeaths', 'TotalRecovered', 'NewRecovered', 'ActiveCases',
 'Serious,Critical', 'Tot\xa0Cases/1M pop', 'Deaths/1M pop', 'TotalTests', 'Tests/', 
'1M pop', 'Population', 'Continent', 
'1 Caseevery X ppl1 Deathevery X ppl1 Testevery X ppl']

We split by "/n" and then clean up the data. We remove the empty elements. Now let's try to scrap one of the "tr" elements

num_headers = len(headers)
table_body = table.find("tbody")
rows = table_body.find_all("tr")
for idx,row_element in enumerate(rows[8:]):
  row= row_element.get_text().split("\n")[1:]
  if len(row) != num_headers:
print(" No Errors")
 No Errors
  • We get all the elements
  • We start from element 8 since the row with "USA" is the 8th element in the list.
  • The first element in the row is an empty element and ignore it
  • We put a check to ensure that the length of the row and the headers are the same

Now, we have all the data. The data can be transformed and stored as a list of dictionaries or in a CSV.

How to get attributes of the tags

Let's try to get the href value inside a "a tag".

a_tag = soup.find('a')
print(f"Attributes :  {a_tag.__dict__['attrs']}")

<a class="navbar-brand" href="/"><img border="0" src="/img/worldometers-logo.gif" title="Worldometer"/></a>

Attributes :  {'href': '/', 'class': ['navbar-brand']}

To get the href, we can simply do the following

href = a_tag['href']

Let's try to get the URL of the image inside the "a tag", i.e the value for "src"

img = soup.select("a img")[0]
img_src = img['src']
print(f'Src is {img_src}')

<img border="0" src="/img/worldometers-logo.gif" title="Worldometer"/>
Src is /img/worldometers-logo.gif
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