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How to extract hidden html content with scrapy?

I'm using scrapy (on PyCharm v2020.1.3) to build a spider that crawls this webpage: "https://www.woolworths.com.au/shop/browse/drinks/cordials-juices-iced-teas/iced-teas", i want to extract the products names, and the breadcrumb in a list format, and save the results in a csv file.
I tried the following code but it returns empty brackets [] , after i've inspected the html code i discovred that the content is hidden in angularjs format.
If someone has a solution for that it would be great
Thank you
import scrapy
class ProductsSpider(scrapy.Spider):
name = 'products'
start_urls = ['https://www.woolworths.com.au/shop/browse/drinks/cordials-juices-iced-teas/iced-teas']
def parse(self, response):
product = response.css('a.shelfProductTile-descriptionLink::text').extract()
yield "productnames"

You won't be able to get the desired products through parsing the HTML. It is heavily javascript orientated and therefore scrapy wont parse this.
The simplest way to get the product names, I'm not sure what you mean by breadcrumbs is to re-engineer the HTTP requests. The woolworths website generates the product details via an API. If we can mimick the request the browser makes to obtain that product information we can get the information in a nice neat format.
First you have to set within settings.py ROBOTSTXT_OBEY = False. Becareful about protracted scrapes of this data because your IP will probably get banned at some point.
Code Example
import scrapy
class TestSpider(scrapy.Spider):
name = 'test'
allowed_domains = ['woolworths.com']
data = {
'excludeUnavailable': 'true',
'source': 'RR-Best Sellers'}
def start_requests(self):
url = 'https://www.woolworths.com.au/apis/ui/products/58520,341057,305224,70660,208073,69391,69418,65416,305227,305084,305223,427068,201688,427069,341058,305195,201689,317793,714860,57624'
yield scrapy.Request(url=url,meta=self.data,callback=self.parse)
def parse(self, response):
data = response.json()
for a in data:
yield {
'name': a['Name'],
}
Explanation
We start of with our defined url in start_requests. This URL is the specific URL of the API woolworth uses to obtain information for iced tea. For any other link on woolworths the part of the URL after /products/ will be specific to that part of the website.
The reason why we're using this, is because using browser activity is slow and prone to being brittle. This is fast and the information we can get is usually highly structured much better for scraping.
So how do we get the URL you may be asking ? You need to inspect the page, and find the correct request. If you click on network tools and then reload the website. You'll see a bunch of requests. Usually the largest sized request is the one with all the data. Clicking that and clicking preview gives you a box on the right hand side. This gives all the details of the products.
In this next image, you can see a preview of the product data
We can then get the request URL and anything else from this request.
I will often copy this request as a CURL (Bash Command) as seen here
And enter it into curl.trillworks.com. This can convert CURL to python. Giving you a nice formatted headers and any other data needed to mimick the request.
Now putting this into jupyter and playing about, you actually only need the params NOT the headers which is much better.
So back to the code. We do a request, using meta argument we can pass on the data, remember because it's outside the function we have to use self.data and then specifying the callback to parse.
We can use the response.json() method to convert the JSON object to a set of python dictionaries corresponding to each product. YOU MUST have scrapy V2.2 to use this method. Other you could use data = json.loads(response.text), but you'll have put to import json at the top of the script.
From the preview and playing about with the json in requests we can see these python dictionaries are actually within a list and so we can use a for loop to loop round each product, which is what we are doing here.
We then yield a dictionary to extract the data, a refers to each products which is it's own dictionary and a['Name'] refers to that specific python dictionary key 'Name' and giving us the correct value. To get a better feel for this, I always use requests package in jupyter to figure out the correct way to get the data I want.
The only thing left to do is to use scrapy crawl test -o products.csv to output this to a CSV file.
I can't really help you more than this until you specify any other data you want from this page. Please remember that you're going against what the site wants you to scrape, but also any other pages on that website you will need to find out the specific URL to the API to get those products. I have given you the way to do this, I suggest if you want to automate this it would be worth your while trying to struggle with this. We are hear to help but an attempt on your part is how you're going to learn coding.
Additional Information on the Approach of Dynamic Content
There is a wealth of information on this topic. Here are some guidelines to think about when looking at javascript orientated websites. The default is you should try re-engineer the requests the browser makes to load the pages information. This is what the javascript in this site and many other sites is doing, it's providing a dynamic way without reloading the page to display new information by making an HTTP request. If we can mimic that request, we can get the information we desire. This is the most efficent way to get dynamic content.
In order of preference
Re-engineering the HTTP requests
Scrapy-splash
Scrapy_selenium
importing selenium package into your scripts
Scrapy-splash is slightly better than the selenium package, as it pre-renders the page, giving you access to the selectors with the data. Selenium is slow, prone to errors but will allow you to mimic browser activity.
There are multiple ways to include selenium into your scripts see down below as an overview.
Recommended Reading/Research
Look at the scrapy documentation with regard to dynamic content here
This will give you an overview of the steps to handling dynamic content. I will say generally speaking selenium should be thought of as a last resort. It's pretty inefficient when doing larger scale scraping.
If you are consider adding in the selenium package into your script. This might be the lower barrier of entry to getting your script working but not necessarily that efficient. At the end of the day scrapy is a framework but there is a lot of flexibility in adding in 3rd party packages. The spider scripts are just a python class importing the scrapy architecture in the background. As long as you're mindful of the response and translating some of the selenium to work with scrapy, you should be able to input selenium into your scripts. I would this solution is probably the least efficient though.
Consider using scrapy-splash, splash pre-renders the page and allows for you to add in javascript execution. Docs are here and a good article from scrapinghub here
Scrapy-selenium is a package with a custom scrapy downloader middleware that allows you to do selenium actions and execute javascript. Docs here You'll need to have a play around to get the login in procedure from this, it doesn't have the same level of detail as the selenium package itself.

Unable to get all the links within a page

I am trying to scrape this page:
https://www.jny.com/collections/bottoms
It has a total of 55 products listed with only 24 listed once the page is loaded. However, the div contains list of all the 55 products. I am trying to scrape that using scrappy like this :
def parse(self, response):
print("in herre")
self.product_url = response.xpath('//div[#class = "collection-grid js-filter-grid"]//a/#href').getall()
print(len(self.product_url))
print(self.product_url)
It only gives me a list of length 25. How do I get the rest?

I would suggest scraping it through the API directly - the other option would be rendering Javascript using something like Splash/Selenium, which is really not ideal.
If you open up the Network panel in the Developer Tools on Chrome/Firefox, filter down to only the XHR Requests and reload the page, you should be able to see all of the requests being sent out. Some of those requests can help us figure out how the data is being loaded into the HTML. Here's a screenshot of what's going on there behind the scenes.
Clicking on those requests can give us more details on how the requests are being made and the request structure. At the end of the day, for your use case, you would probably want to send out a request to https://www.jny.com/collections/bottoms/products.json?limit=250&page=1 and parse the body_html attribute for each Product in the response (perhaps using scrapy.selector.Selector) and use that however you want. Good luck!

How can i convert scraping script as web-service?

I want to build a api that accepts a string and returns html code.
Here is my scraping code that i want as a web-service.
Code
from selenium import webdriver
import bs4
import requests
import time
url = "https://www.pnrconverter.com/"
browser = webdriver.Firefox()
browser.get(url)
string = "3 PS 232 M 03FEB 7 JFKKBP HK2 1230A 420P 03FEB E
PS/JPIX8U"
button =
browser.find_element_by_xpath("//textarea[#class='dataInputChild']")
button.send_keys(string) #accept string
button.submit()
time.sleep(5)
soup = bs4.BeautifulSoup(browser.page_source,'html.parser')
html = soup.find('div',class_="main-content") #returns html
print(html)
Can anyone tell me the best possible solution to wrap up my code as a api/web-service.

There's no best possible solution in general, because a solution has to fit the problem and the available resources.
Right now it seems like you're trying to wrap someone else's website. If that's the problem you're actually trying to solve, and you want to give credit, you should probably just forward people to their site. Have your site return a 302 Redirect with their URL in the Location field in your header.
If what you're trying to do is get the response from this one sample check you have hardcoded, and and make that result available, I would suggest you put it in a static file behind nginx.
If what you're trying to do is use their backend to turn itineraries you have into responses you can return, you can do that by using their backend API, once that becomes available. Read the documentation, use the requests library to hit the API endpoint that you want, and get the JSON result back, and format it to your desires.
If you're trying to duplicate their site by making yourself a man-in-the-middle, that may be illegal and you should reconsider what you're doing.
For hosting purposes, you need to figure out how often your API will be hit. You can probably start on Heroku or something similar fairly easily, and scale up if you need to. You'll probably want WebObj or Flask or something similar sitting at the website where you intend to host this application. You can use those to process what I presume will be a simple request into the string you wish to hit their API with.

I am the owner of PNR Converter, so I can shed some light on your attempt to scrape content from our site. Unfortunately scraping from PNR Converter is not recommended. We are developing an API which looks like it would suit your needs, and should be ready in the not too distant future. If you contact us through the site we would be happy to work with you should you wish to use PNR Converter legitimately. PNR Converter gets at least one complete update per year and as such we change all the code on a regular basis. We also monitor all requests to our site, and we will block any requests which are deemed as improper usage. Our filter has already picked up your IP address (ends in 250.144) as potential misuse.
Like I said, should you wish to work with us at PNR Converter legitimately and not scrape our content then we would be happy to do so! please keep checking https://www.pnrconverter.com/api-introduction for information relating to our API.
We are releasing a backend upgrade this weekend, which will have a different HTML structure, and dynamically named elements which will cause a serious issue for web scrapers!

Loading Ajax with Python Requests

For a personal project, I'm trying to get a full friends list of a user (myself for now) from Facebook using Requests and BeautifulSoup.
The main friends page however displays only 20, and the rest are loaded with Ajax when you scroll down.
The request url looks something like this (method is GET):
https://www.facebook.com/ajax/pagelet/generic.php/AllFriendsAppCollectionPagelet?dpr=1&data={"collection_token":"1244314824:2256358349:2","cursor":"MDpub3Rfc3RydWN0dXJlZDoxMzU2MDIxMTkw","tab_key":"friends","profile_id":1244214828,"overview":false,"ftid":null,"order":null,"sk":"friends","importer_state":null}&__user=1364274824&__a=1&__dyn=aihaFayfyGmagngDxfIJ3G85oWq2WiWF298yeqrWo8popyUW3F6wAxu13y78awHx24UJi28cWGzEgDKuEjKeCxicxabwTz9UcTCxaFEW58nVV8-cxnxm1typ9Voybx24oqyUf9UgC_UrQ4bBv-2jAxEhw&__af=o&__req=5&__be=-1&__pc=EXP1:DEFAULT&__rev=2677430&__srp_t=1474288976
My question is, is it possible to recreate the dynamically generated tokens such as the __dyn, cursor, collection_token etc. to send manually in my request? Is there some way to figure out how they are generated or is it a lost cause?
I know that the current Facebook API does not support viewing a full friends list. I also know that I can do this with Selenium, or some other browser simulator, but that feels way too slow, ideally I want to scrape thousands of friends lists (of users whose friends lists are public) in a reasonable time.
My current code is this:
import requests
from bs4 import BeautifulSoup
with requests.Session() as S:
requests.utils.add_dict_to_cookiejar(S.cookies, {'locale': 'en_US'})
form = {}
form['email'] = 'myusername'
form['pass'] = 'mypassword'
response = S.post('https://www.facebook.com/login.php?login_attempt=1&lwv=110', data=form)
# Im logged in
page = S.get('https://www.facebook.com/yoshidakai/friends?source_ref=pb_friends_tl')
Any help will be appreciated, including other methods to achieve this :)

As of this writing, you can extract this information by parsing the page and then get the next cursor for latter pages by parsing the preceding ajax response. However, as Facebook regularly makes updates to its backend, I have had more stable results using selenium to drive a Chrome headless browser to scroll through the page, and then parsing the resulting HTML.

Webscraping Financial Data from Morningstar

I am trying to scrape data from the morningstar website below:
http://financials.morningstar.com/ratios/r.html?t=IBM&region=USA&culture=en_US
I am currently trying to do just IBM but hope to eventually be able to type in the code of another company and do this same with that one. My code so far is below:
import requests, os, bs4, string
url = 'http://financials.morningstar.com/ratios/r.html?t=IBM&region=USA&culture=en_US';
fin_tbl = ()
page = requests.get(url)
c = page.content
soup = bs4.BeautifulSoup(c, "html.parser")
summary = soup.find("div", {"class":"r_bodywrap"})
tables = summary.find_all('table')
print(tables[0])
The problem I am experiencing at the moment is unlike a simpler webpage I have scraped the program can't seem to locate any tables even though I can see them in the HTML for the page.
In researching this problem the closest stackoverflow question is below:
Python webscraping - NoneObeject Failure - broken HTML?
In that one they explained that Morningstar's tables are dynamically loaded and used some json code I am unfamiliar with and somehow generated a different weblink which managed to scrape the data but I don't understand where it came from?

It's a real problem scraping some modern web pages, particularly on pages generated by single-page applications (where the content is maintained by AJAX calls and DOM modification rather than delivered as ready-to-go HTML in a single server response).
The best way I have found to access such content is to use the Selenium web testing environment to have a browser load the page under the control of my program, then extract the page contents from Selenium for scraping. There are other environments that will execute the scripts and modify the DOM appropriately, but I haven't used any of them.
It's not as difficult as it sounds, but it will take you a little jiggering around to get there.

Web scraping can be greatly simplified when the site offers an API, be it officially supported or just an unofficial hack. Even the hack is better than trying to fiddle with the HTML which can change every day.
So a search for morningstar api might be fruitful. And, in fact, some friendly Gister has already worked this out for you.
Would the search be without result, a usually fruitful approach is to investigate what ajax calls the page is doing to retrieve data and then issue them directly. This can be achieved via the browser debuggers, tab "network" or so where each request can be investigated in detail in a very friendly UI.

I've found scraping dynamic sites to be a lot easier with JavaScript than with Python + Selenium. There is a great module for nodejs/phantomjs: ScraperJS. It is very easy to use: it injects jQuery into the scraped page and you can extract data with jQuery selectors.