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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
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5 Top R Vs SAS Differences

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Statistical analysis should know by every software engineer. R is an open source statistical programming language. SAS is licensed analysis suite for statistics. The two are very much popular in Machine learning and data analytics projects.


SAS is an Analysis-suite software and R is a programming language.

1. R Language

  1. R supports both statistical analysis and Graphics
  2. R is an open source project.
  3. R is 18th most popular Language
  4. R packages are written in C, C++, Java, Python and.Net
  5. R is popular in Machine learning, data mining and Statistical analysis projects.

a). R Advantages

  • R is flexible since a lot of packages are available.
  • R is best suited for data related projects and Machine learning.
  • Less cost since it is open source language.
  • R Studio is the best tool to develop R programming modules.
Ref: imartcus.org (read more advantages)

R vs SAS Read Today


b). R Disadvantages

  • R language architecture model is out of date. So may not use it for critical applications.
  • R is not suitable for Server programming, due to lack of security.
  • R code you cannot use in web browsers.

SAS

SAS is a statistical analysis suite. Developed to process data sets in mainframe computers. Later developed to support multi-platforms. Like Mainframe, Windows, and Linux, SAS has multiple products. SAS/ Base is very basic level. SAS is popular in data related projects.

a). SAS Advantages

  1. The data integration from any data source is faster in SAS.
  2. The licensed software suite, so you will get support from SAS organization for any issues.
  3. SAS has multiple products. Most popular in creating reports and statistical analysis.
  4. Best suited for data-oriented projects.

b). SAS Disadvantages

  1. Mining of text is hard in SAS.
  2. Graphical visualization is not present in SAS.
  3. SAS is not suitable for Machine learning projects.
  4. The SAS software is expensive.
  5. SAS studio is a useful tool to work on it.


References

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