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Scraping Website: How to Write a Script in Python

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Here's a python script that you can use as a model to scrape a website. Python script The below logic uses BeautifulSoup Package for web scraping. import requests from bs4 import BeautifulSoup url = 'https://www.example.com' response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') # Print the title of the webpage print(soup.title.text) # Print all the links in the webpage for link in soup.find_all('a'):     print(link.get('href')) In this script, we first import the Requests and Beautiful Soup libraries. We then define the URL we want to scrape and use the Requests library to send a GET request to that URL. We then pass the response text to Beautiful Soup to parse the HTML contents of the webpage. We then use Beautiful Soup to extract the title of the webpage and print it to the console. We also use a for loop to find all the links in the webpage and print their href attributes to the console. This is just a basic example, but

Efficiently Reading Python Dictionary Data with Loops

 Here's a python dictionary and the data it reads in a for loop and use the same for calculation. It's a helpful example of how to use a dictionary in real projects.


Sample Program to Read Dictionary


Python Dictionary program

yearly_revenue = {

   2017 : 1000000,

   2018 : 1200000,

   2019 : 1250000,

   2020 : 1100000,

   2021 : 1300000,

 }

total_income = 0

for year_id in yearly_revenue.keys():

  total_income+=yearly_revenue[year_id]

  print(year_id, yearly_revenue[year_id])


print(total_income)

print(total_income/len(yearly_revenue))


Output


2017 1000000

2018 1200000

2019 1250000

2020 1100000

2021 1300000

5850000

1170000.0



** Process exited - Return Code: 0 **

Press Enter to exit the terminal


How the program reads dictionary data

The input to for loop is dictionary keys. The total revenue sums up in the following code. The overall highlight of this program is the dictionary keys method.


References

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