Categories
Python Code

Scraping a page via CSS style data

The challenge was to scrape a site where the class names for each element were out of order / randomised. So the only way to get the data in the correct sequence was to sort through the CSS styles by left, top, and match to the class names in the divs…

The px varied slightly: see 710 and 711 above

The names were meaningless and there was no way of establishing an order, a quick check in developer tools shows the highlighter rectangle jumps all over the page in no particular order when traversing the source code.

page_useddemo-gear_fF0iR8Rn6hrUOJh0YwkOA body 

So we began the code by identifying the CSS and then parsing it:

The idea is to extract the CSS style data, parse it for left, and top px, and sort through those to match the out of sequence div/classnames in the body of the page.

There was NO possibility of sequentially looping through the divs to extract ordered data.

After watching the intro to the challenge set on YouTube, and a handy hint from CMK we got to work.

From this article you will learn as much about coding with Python as you will about web scraping in specific. Note I used requests_html, as it provided me with the option to use XPATH.

BeautifulSoup could also have been used.

Identifying columns and rows based on “left px” and “top px”

Python methods used:

round

x = round(1466,-2)
print(x) # 1500

x = round(1526,-2)
print(x) # 1500

x = round(1526,-1)
print(x) # 1530

I needed to use round as the only way to identify a “Column” of text was to cross reference the <style> “left px” and “top px” with the class names used inside the divs. Round was required as there was on occasion a 2 or 3 px variation in the “left” value.

itemgetter

Item getterfrom operator import itemgetter

ls.sort(key=itemgetter('left','top'))

I had to sort the values from the parsed css style in order of “left” to identify 3 columns of data, and then “top” to sort the contents of the 3 columns A, B, C.

zip

zipped = zip(ls_desc,ls_sellp,ls_suggp)
Zipping the 3 lists – read on to find out the issue with doing this…

rows = list(zipped)

So to get the data from the 3 columns, A (ls_desc), B (ls_sellp), and C (ls_suggp) I used ZIP, but…….the were/are 2 values missing in column C!!

A had 77 values,

B had 77 values

C had 75 !

Not only was there no text in 2 of the blanks in column C, there was also NO text or even any CSS.

We only identified this as an issue after running the code – visually the page looked consistent, alas the last part of column “C” becomes out of sequence with the data in colmumn A and B which are both correct.

Solution?

Go back and check if column “C” has a value at the same top px value as Column “B”. If no value then insert an “x” or spacer into Column C at that top px value.

This will need to be rewritten using dictionaries, and create one dictionary per ROW rather than my initial idea of 1 list per column and zipping them!

Zipping the 3 lists nearly works..but 2 missing values in “Suggested Price” means that the data in Column C become out of synch.

special thanks to “code monkey king” for the idea/challenge!

url=’http://audioeden.com/useddemo-gear/4525583102

My initial solution:

https://github.com/RGGH/Experimental-Custom-Scrapers/blob/master/audio1.py

Next :

Rewrite the section for “Column B” to check for presence of text in column “C” on the same row…

webscraping-css-style

1 missing value halfway down column “C” means more error checking is required! – If you just want the “Selling Price” and “Description” then this is code is 100% successful! πŸ‘

See the solution, and error on the YouTube Video

Conclusion:

For more robust web scraping where css elements may be missing use dictionaries/enumerate each row and check. It’s the old case of “you don’t know what you don’t know”

If you can ensure each list has the same number of items, then ZIP is ok to use.

Categories
Python Code Scrapy

Web Scraping Introduction

As an overview of how web scraping works, here is a brief introduction to the process, with the emphasis on using Scrapy to scrape a listing site.

If you would like to know more, or would like us to scrape data from a specific site please get in touch.

*This article also assumes you have some knowledge of Python, and have Scrapy installed. It is recommended to use a virtual environment. Although the focus is on using Scrapy, similar logic will apply if you are using Beautiful Soup, or Requests. (Beautiful Soup does some of the hard work for you with find, and select).

Below is a basic representation of the process used to scrape a page / site

web scraping with scrapy
Web Scraping Process Simplified
Most sites you will want to scrape provide data in a list – so check the number per page, and the total number. Is there a “next page” link? – If so, that will be useful later on…
Identify the div and class name to use in your “selector”

Identifying the div and class name

Using your web browser developer tools, traverse up through the elements (Chrome = Inspect Elements) until you find a ‘div’ (well, it’s usually a div) that contains the entire advert, and go no higher up the DOM.

(advert = typically: the thumbnail + mini description that leads to the detail page)

The code inside the ‘div’ will be the iterable that you use with the for loop.
The β€œ.” before the β€œ//” in the xpath means you select all of them
eg. All 20, on a listings page that has 20 adverts per page

Now you have the xpath and checked it in scrapy shell, you can proceed to use it with a for loop and selectors for each piece of information you want to pick out. If you are using XPATH, you can use the class name from the listing, and just add “.” to the start as highlighted below.

This “.” ensures you will be able to iterate through all 20 adverts at the same node level. (i.e All 20 on the page).

parse

To go to the details page we use “Yield” but we also have to pass the variables that we have picked out on the main page. So we use ‘meta’ (or newer version = cb_kwargs”).

yield Request(absolute_url, callback=self.fetch_detail, meta={'link': link, 'logo_url': logo_url, 'lcompanyname':lcompanyname})

Using ‘meta’ allows us to pass variables to the next function – in this case it’s called “fetch_details” – where they will be added to the rest of the variables collected and sent to the FEEDS export which makes the output file.

There is also a newer, recommended version of β€œmeta” to pass variables between functions in Scrapy: β€œcb_kwargs”

Once you have all the data it is time to use β€œYield” to send it to the FEEDS export.

The “FEEDS” method that let you write output to your chosen file format/destination

This is the format and destination that you have set for your output file.

*Note it can also be a database, rather than JSON or CSV file.

Putting it all together

See the fully working spider code here :

https://github.com/RGGH/Scrapy5/blob/master/yelpspider.py

You may wish to run all of your code from within the script, in which case you can do this:

# main driver

if __name__ == "__main__":

    process = CrawlerProcess()

    process.crawl(YelpSpider)

    process.start()

# Also you will need to add this at the start :

from scrapy.crawler import CrawlerProcess

Web Scraping – Summary

We have looked at the steps involved and some of the code you’ll find useful when using Scrapy.

Identifying the html to iterate through is the key

Try and find the block of code that has all of the listings / adverts, and then narrow it down to one advert/listing. Once you have done that you can test your code in “scrapy shell” and start building your spider.

(Scrapy shell can be run from your CLI, independent of your spider code):

xpath-starts-with
Scrapy shell is your friend!

! Some of this content may be easier to relate to if you have studied and completed the following : https://docs.scrapy.org/en/latest/intro/tutorial.html

If you have any questions please get in touch and we’ll be pleased to help.