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Scott Rome - Math Ph.D. who works in Machine Learning

Math Ph.D. who works in Machine Learning
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srome.github.io
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It feels like ages since I last posted something, and I've been trying to figure out how I can come up with the sort of scraping scripts I normally prepare...but in Python instead of R. In R, I would normally use the rvest package for basic scraping, and I would add RSelenium for more advanced stuff.

To retrieve the site's HTML, I used the requests python package, and BeautifulSoup for the scraping. I've grown accustomed to the sort of data types I've been using in R, and after basic googling, it would appear that I wouldn't be too far off from R if I used pandas as the package for compiling everything into a dataframe.

The website holds information regarding new and used cars, and doesn't use too much javascript which is why I didn't use Selenium. The code is a bit messy and I used quite a few custom function in order to make the transition from R to Python a little more smoother. As always, the site URL in the code is not the actual site that i used in the script.

[code language=python]
#import necessary packages
import requests
import bs4
import pandas as pd


all_links = [] #empty list to be populated
for j in range(1,69): #maximum page numbers are hardcoded. 4 pages.
#Request the page from the site
page = requests.get("https://www.dummy_car_site.com?page=" +
str(j) + "&size=25")
#Parse the pages details using the Beautiful Soup package
soup = bs4.BeautifulSoup(page.content, "html.parser")

#By inspecting the HTML in Chrome, the below selector should retrieve the full list of posts from one page
#The result is a list of bs4.element.tag
links_list = soup.select("h2 > a")

#Assign an empty list to a variable
#According to documentation, the tag object has a dict with all the attributes.
#That means if there is a link (i.e. href) it can be retrieved as a key in a dictionary
for post in links_list:
link = post.attrs["href"]
all_links.append(link)


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tinaja.computer

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