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 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.
So we began the code by identifying the CSS and then parsing it:
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.
Python methods used:
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.
Item getterfrom operator import itemgetter
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.
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.
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!
special thanks to “code monkey king” for the idea/challenge!
Instead, in your browser, check if you may be able to parse the code, beginning with ctrl + f, and “json” and track down some JSON in the form of a python dictionary. You ‘just’ need to isolate it.
The response is not nice, but you can gradually shrink it down, in Scrapy shell or python shell…
Split, strip, replace
From within Scrapy, or your own Python code you can split, strip, and replace, with the built-in python commands until you have just a dictionary that you can use with json.loads.
x = response.text.split('JSON.parse').replace("\u0022","\"").replace("\u2019m","'").lstrip("(").split(" ").strip().replace("\"","",1).replace("\");","")
Master replace, strip , and split and you won’t need regular expressions!
With the response.text now ready as a JSON friendly dictionary you can do this:
import json q = json.loads(x)
comment = (q[‘doctor’][‘sample_rating_comment’])
The key thing to remember to use when parsing the response text is to use the index, to pick out the section you want, and then make use of “\” backslash to escaped characters when you are working with quotes, and actual backslashes in the text you’re parsing.
You may see a mass of text on your screen to begin with, but persevere and you can arrive at the dictionary contained within…