pip install selenium

pip install bs4

pip install html5lib

soup = bs4.BeautifulSoup("Extremely bold)
tag = soup.b
type(tag)

tag.name

tag[id]

# add the element
tag['id'] = 'verybold'
tag['another-attribute'] = 1
tag
# delete the tag
del tag['id']    

class_is_multi= { '*' : 'class'}
xml_soup = BeautifulSoup('
', 'xml', multi_valued_attributes=class_is_multi)
xml_soup.p['class']
# [u'body', u'strikeout']

tag.string
# u'Extremely bold'
type(tag.string)
# 

tag.string.replace_with("No longer bold")
tag

doc=BeautifulSoup("INSERT FOOTER HEREHere's the footer","xml")
doc.find(text="INSERT FOOTER HERE").replace_with(footer)
print(doc)

?xml version="1.0" encoding="utf-8"?>
# 

#importing the BeautifulSoup Library

importbs4
import requests

#Creating the requests

res = requests.get("https://en.wikipedia.org/wiki/Machine_learning")
print("The object type:",type(res))

# Convert the request object to the Beautiful Soup Object
soup = bs4.BeautifulSoup(res.text,'html5lib')
print("The object type:",type(soup)

The object type 
Convert the object into: 

soup.select('.mw-headline')
for i in soup.select('.mw-headline'):
print(i.text,end = ',')

Overview,Machine learning tasks,History and relationships to other fields,Relation to data mining,
Relation to optimization,Relation to statistics, Theory,Approaches,Types of learning algorithms,
Supervised learning,Unsupervised learning,Reinforcement learning,Self-learning,Feature learning,
Sparse dictionary learning,Anomaly detection,Association rules,Models,Artificial neural networks,
Decision trees,Support vector machines,Regression analysis,Bayesian networks,Genetic algorithms,
Training models,Federated learning,Applications,Limitations,Bias,Model assessments,Ethics,Software,
Free and open-source software,Proprietary software with free and open-source editions,Proprietary 
software,Journals,Conferences,See also,References,Further reading,External links,

following code:
# importing the libraries
from bs4 import BeautifulSoup
import requests

url="https://www.javatpoint.com/"

# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text

# Parse the html content
soup = BeautifulSoup(html_content, "html5lib")
print(soup.prettify()) # print the parsed data of html

print(soup.title)

print(soup.title.text)

Tutorials List - Javatpoint

for link in soup.find_all("a"):
print("Inner Text is: {}".format(link.text))
print("Title is: {}".format(link.get("title")))
print("href is: {}".format(link.get("href")))

href is: https://www.facebook.com/javatpoint
Inner Text is: 
The title is: None
href is: https://twitter.com/pagejavatpoint
Inner Text is: 
The title is: None
href is: https://www.youtube.com/channel/UCUnYvQVCrJoFWZhKK3O2xLg
Inner Text is: 
The title is: None
href is: https://javatpoint.blogspot.com
Inner Text is: Learn Java
Title is: None
href is: https://www.javatpoint.com/java-tutorial
Inner Text is: Learn Data Structures
Title is: None
href is: https://www.javatpoint.com/data-structure-tutorial
Inner Text is: Learn C Programming
Title is: None
href is: https://www.javatpoint.com/c-programming-language-tutorial
Inner Text is: Learn C++ Tutorial

from bs4 import BeautifulSoupas soup
from urllib.request import urlopen as uReq

# Request from the webpage
myurl = "https://www.flipkart.com/search?q=iphones&otracker=search&otracker1=search&marketplace=FLIPKART&as-show=on&as=off"


uClient  = uReq(myurl)
page_html = uClient.read()
uClient.close()

page_soup = soup(page_html, features="html.parser")

# print(soup.prettify(containers[0]))

# This variable held all html of webpage
containers = page_soup.find_all("div",{"class": "_3O0U0u"})
# container = containers[0]
# # print(soup.prettify(container))
#
# price = container.find_all("div",{"class": "col col-5-12 _2o7WAb"})
# print(price[0].text)
#
# ratings = container.find_all("div",{"class": "niH0FQ"})
# print(ratings[0].text)
#
# #
# # print(len(containers))
# print(container.div.img["alt"])

# Creating CSV File that will store all data 
filename = "product1.csv"
f = open(filename,"w")

headers = "Product_Name,Pricing,Ratings\n"
f.write(headers)

for container in containers:
    product_name = container.div.img["alt"]

    price_container = container.find_all("div", {"class": "col col-5-12 _2o7WAb"})
    price = price_container[0].text.strip()

    rating_container = container.find_all("div",{"class":"niH0FQ"})
    ratings = rating_container[0].text

# print("product_name:"+product_name)
    # print("price:"+price)
    # print("ratings:"+ str(ratings))

     edit_price = ''.join(price.split(','))
     sym_rupee = edit_price.split("?")
     add_rs_price = "Rs"+sym_rupee[1]
     split_price = add_rs_price.split("E")
     final_price = split_price[0]

     split_rating = str(ratings).split(" ")
     final_rating = split_rating[0]

     print(product_name.replace(",", "|")+","+final_price+","+final_rating+"\n")
f.write(product_name.replace(",", "|")+","+final_price+","+final_rating+"\n")

f.close()