extract 5000 ride details from blablacar site - python

can anyone please help me to extract the details of rider from the url of blabla car or please put some idea for web scrawling
EXTRACT THE FIRST 5000 RIDE DETAILS FROM THE url of blabla car website
I am new to web scrawling and python .So kindly anyone put some hint to do the task

At first, you should always think where your scraping starting point is.
In this case https://www.blablacar.in/search-car-sharing looks pretty good, as there are links to the most popular routes.
Here is the pipeline you may want to follow:
Declare a spider.
Set USER_AGENT (in settings.py) to something custom to not get 403 responses.
Set DOWNLOAD_DELAY to something like 0.5 or so to not be banned (may need to make the value even bigger).
Add starting point to the spider: start_urls = ['https://www.blablacar.in/search-car-sharing']
Add a parse method that will yield requests to route pages.
Add a parse_route method that will yield information about the rides and follows the pagination.
That's how parse method may look like:
def parse(self, response):
for a_tag in response.css('.search-empty__meeting-points a'):
yield response.follow(a_tag, self.parse_route)
And here is parse_route example that parses name and date of the ride:
def parse_route(self, response):
for trip in response.css('.trip-search-results li'):
item = {}
item['name'] = trip.css('.ProfileCard-info--name::text').extract_first().strip()
item['date'] = trip.css('.description .time::attr(content)').extract_first()
yield item
for a_tag in response.css('.pagination .next:not(.disabled) a'):
yield response.follow(a_tag, self.parse_route)
Hope this gives you an intuition on how to address the task.

Related

Where to add request.meta on my script to crawl once

I downloaded scrapy-crawl-once and I am trying to run it in my program. I want to scrape each book's url from the first page of http://books.toscrape.com/ and then scrape the title of the book from that url. I know I can scrape each book title from the first page, but as practice for scrapy-crawl-once, I wanted to do it this way. I already added the middlewares and need to know where to add request.meta. From doing some research, there isn't much codes out there for some example guidance so was hoping someone can help here. I learned the basics of python two weeks ago so struggling right now. I tried this, but the results hasn't changed. Can someone help me out please. I added [:2] so that if I change it to [:3], I can show myself that it works.
def parse(self, response):
all_the_books = response.xpath("//article[#class='product_pod']")
for div in all_the_books[:2]:
book_link = 'http://books.toscrape.com/' + div.xpath(".//h3/a/#href").get()
request = scrapy.Request(book_link, self.parse_book)
request.meta['book_link'] = book_link
yield request
def parse_book(self, response):
name = response.xpath("//div[#class='col-sm-6 product_main']/h1/text()").get()
yield {
'name': name,
}
Its docs says
To avoid crawling a particular page multiple times set
request.meta['crawl_once'] = True
so you need to do
def parse(self, response):
all_the_books = response.xpath("//article[#class='product_pod']")
for div in all_the_books[:2]:
book_link = 'http://books.toscrape.com/' + div.xpath(".//h3/a/#href").get()
request = scrapy.Request(book_link, self.parse_book)
request.meta['crawl_once'] = True
yield request
And it will not crawl that link again

Scrapy get all links from any website

I have the following code for a web crawler in Python 3:
import requests
from bs4 import BeautifulSoup
import re
def get_links(link):
return_links = []
r = requests.get(link)
soup = BeautifulSoup(r.content, "lxml")
if r.status_code != 200:
print("Error. Something is wrong here")
else:
for link in soup.findAll('a', attrs={'href': re.compile("^http")}):
return_links.append(link.get('href')))
def recursive_search(links)
for i in links:
links.append(get_links(i))
recursive_search(links)
recursive_search(get_links("https://www.brandonskerritt.github.io"))
The code basically gets all the links off of my GitHub pages website, and then it gets all the links off of those links, and so on until the end of time or an error occurs.
I want to recreate this code in Scrapy so it can obey robots.txt and be a better web crawler overall. I've researched online and I can only find tutorials / guides / stackoverflow / quora / blog posts about how to scrape a specific domain (allowed_domains=["google.com"], for example). I do not want to do this. I want to create code that will scrape all websites recursively.
This isn't much of a problem but all the blog posts etc only show how to get the links from a specific website (for example, it might be that he links are in list tags). The code I have above works for all anchor tags, regardless of what website it's being run on.
I do not want to use this in the wild, I need it for demonstration purposes so I'm not going to suddenly annoy everyone with excessive web crawling.
Any help will be appreciated!
There is an entire section of scrapy guide dedicated to broad crawls. I suggest you to fine-grain your settings for doing this succesfully.
For recreating the behaviour you need in scrapy, you must
set your start url in your page.
write a parse function that follow all links and recursively call itself, adding to a spider variable the requested urls
An untested example (that can be, of course, refined):
class AllSpider(scrapy.Spider):
name = 'all'
start_urls = ['https://yourgithub.com']
def __init__(self):
self.links=[]
def parse(self, response):
self.links.append(response.url)
for href in response.css('a::attr(href)'):
yield response.follow(href, self.parse)
If you want to allow crawling of all domains, simply don't specify allowed_domains, and use a LinkExtractor which extracts all links.
A simple spider that follows all links:
class FollowAllSpider(CrawlSpider):
name = 'follow_all'
start_urls = ['https://example.com']
rules = [Rule(LinkExtractor(), callback='parse_item', follow=True)]
def parse_item(self, response):
pass

Scrapy: Spider optimization

I'm trying to scrap an e-commerce web site, and I'm doing it in 2 steps.
This website has a structure like this:
The homepage has the links to the family-items and subfamily-items pages
Each family & subfamily page has a list of products paginated
Right now I have 2 spiders:
GeneralSpider to get the homepage links and store them
ItemSpider to get elements from each page
I'm completely new to Scrapy, I'm following some tutorials to achieve this. I'm wondering how complex can be the parse functions and how rules works. My spiders right now looks like:
GeneralSpider:
class GeneralSpider(CrawlSpider):
name = 'domain'
allowed_domains = ['domain.org']
start_urls = ['http://www.domain.org/home']
def parse(self, response):
links = LinksItem()
links['content'] = response.xpath("//div[#id='h45F23']").extract()
return links
ItemSpider:
class GeneralSpider(CrawlSpider):
name = 'domain'
allowed_domains = ['domain.org']
f = open("urls.txt")
start_urls = [url.strip() for url in f.readlines()]
# Each URL in the file has pagination if it has more than 30 elements
# I don't know how to paginate over each URL
f.close()
def parse(self, response):
item = ShopItem()
item['name'] = response.xpath("//h1[#id='u_name']").extract()
item['description'] = response.xpath("//h3[#id='desc_item']").extract()
item['prize'] = response.xpath("//div[#id='price_eur']").extract()
return item
Wich is the best way to make the spider follow the pagination of an url ?
If the pagination is JQuery, meaning there is no GET variable in the URL, Would be possible to follow the pagination ?
Can I have different "rules" in the same spider to scrap different parts of the page ? or is better to have the spiders specialized, each spider focused in one thing?
I've also googled looking for any book related with Scrapy, but it seems there isn't any finished book yet, or at least I couldn't find one.
Does anyone know if some Scrapy book that will be released soon ?
Edit:
This 2 URL's fits for this example. In the Eroski Home page you can get the URL's to the products page.
In the products page you have a list of items paginated (Eroski Items):
URL to get Links: Eroski Home
URL to get Items: Eroski Fruits
In the Eroski Fruits page, the pagination of the items seems to be JQuery/AJAX, because more items are shown when you scroll down, is there a way to get all this items with Scrapy ?
Which is the best way to make the spider follow the pagination of an url ?
This is very site-specific and depends on how the pagination is implemented.
If the pagination is JQuery, meaning there is no GET variable in the URL, Would be possible to follow the pagination ?
This is exactly your use case - the pagination is made via additional AJAX calls that you can simulate inside your Scrapy spider.
Can I have different "rules" in the same spider to scrape different parts of the page ? or is better to have the spiders specialized, each spider focused in one thing?
Yes, the "rules" mechanism that a CrawlSpider provides is a very powerful piece of technology - it is highly configurable - you can have multiple rules, some of them would follow specific links that match specific criteria, or located in a specific section of a page. Having a single spider with multiple rules should be preferred comparing to having multiple spiders.
Speaking about your specific use-case, here is the idea:
make a rule to follow categories and subcategories in the navigation menu of the home page - this is there restrict_xpaths would help
in the callback, for every category or subcategory yield a Request that would mimic the AJAX request sent by your browser when you open a category page
in the AJAX response handler (callback) parse the available items and yield an another Request for the same category/subcategory but increasing the page GET parameter (getting next page)
Example working implementation:
import re
import urllib
import scrapy
from scrapy.contrib.spiders import CrawlSpider, Rule
from scrapy.contrib.linkextractors import LinkExtractor
class ProductItem(scrapy.Item):
description = scrapy.Field()
price = scrapy.Field()
class GrupoeroskiSpider(CrawlSpider):
name = 'grupoeroski'
allowed_domains = ['compraonline.grupoeroski.com']
start_urls = ['http://www.compraonline.grupoeroski.com/supermercado/home.jsp']
rules = [
Rule(LinkExtractor(restrict_xpaths='//div[#class="navmenu"]'), callback='parse_categories')
]
def parse_categories(self, response):
pattern = re.compile(r'/(\d+)\-\w+')
groups = pattern.findall(response.url)
params = {'page': 1, 'categoria': groups.pop(0)}
if groups:
params['grupo'] = groups.pop(0)
if groups:
params['familia'] = groups.pop(0)
url = 'http://www.compraonline.grupoeroski.com/supermercado/ajax/listProducts.jsp?' + urllib.urlencode(params)
yield scrapy.Request(url,
meta={'params': params},
callback=self.parse_products,
headers={'X-Requested-With': 'XMLHttpRequest'})
def parse_products(self, response):
for product in response.xpath('//div[#class="product_element"]'):
item = ProductItem()
item['description'] = product.xpath('.//span[#class="description_1"]/text()').extract()[0]
item['price'] = product.xpath('.//div[#class="precio_line"]/p/text()').extract()[0]
yield item
params = response.meta['params']
params['page'] += 1
url = 'http://www.compraonline.grupoeroski.com/supermercado/ajax/listProducts.jsp?' + urllib.urlencode(params)
yield scrapy.Request(url,
meta={'params': params},
callback=self.parse_products,
headers={'X-Requested-With': 'XMLHttpRequest'})
Hope this is a good starting point for you.
Does anyone know if some Scrapy book that will be released soon?
Nothing specific that I can recall.
Though I heard that some publisher has some plans to may be release a book about web-scraping, but I'm not supposed to tell you that.

Scrapy - Visiting nested links and grabbing meta data from each level

I am relatively new to scrapy and have been getting a lot of exceptions...
Here is what I am trying to do:
There 4 nested links that I want to grab data from:
Let's say I have 5 items that I want to crawl in total. These items are
Industry=scrapy.Field()
Company=scrapy.Field()
Contact_First_name=scrapy.Field()
Contact_Last_name=scrapy.Field()
Website=scrapy.Field()
Now to begin crawling I would first have to get the Industry.
The Industry xpath also contains the link to individual listings of companies that belong to their Industry segments.
Next I want to use the Industry xpath and go into the link. This page does not contain any data that I want to crawl. But this page contains href links to individual companies that have their own basic info page.
Using the href link from the listings page, I now arrive at one page that contains the information for one company. Now I want to scrape the Company, Address, and Website.
There is another href link that I need to click in order to lead to Contact_First_Name, Contact_Last_Name.
Using the href link, I now arrive at another page that contains the Contact_First_Name, and Contact_Last_Name
After crawling all of these pages, I should have items that look somewhat like this:
Industry Company Website Contact_First_Name Contact_Last_Name
Finance JPMC JP.com Jamie Dimon
Finance BOA BOA.com Bryan Moynihan
Technology ADSK ADSK.com Carl Bass
EDITED
Here is the code that is working. Anzel's recommendations really helped out but i realized the subclass allowed_domains was wrong which stopped the nested links from following through. Once I changed it, it works.
class PschamberSpider(scrapy.Spider):
name="pschamber"
allowed_domains = ["cm.pschamber.com"]
start_urls = ["http://cm.pschamber.com/list/"]
def parse(self, response):
item = PschamberItem()
for sel in response.xpath('//*[#id="mn-ql"]/ul/li/a'):
# xpath and xpath().extract() will return a list
# extract()[0] will return the first element in the list
item['Industry'] = sel.xpath('text()').extract()
# another mistake you made here
# you're trying to call scrapy.Request(LIST of hrefs) which will fail
# scrapy.Request only takes a url string, not list
# another big mistake is you're trying to yield the item,
# whereas you should yield the Request object
yield scrapy.Request(sel.xpath('#href').extract()[0], callback=self.parse_2, meta={'item': item})
# another mistake, your callback function DOESNT take item as argument
def parse_2(self, response):
for sel in response.xpath('.//*[#id="mn-members"]/div/div/div/div/div/a').extract():
# you can access your response meta like this
item=response.meta['item']
item['Company'] = sel.xpath('text()').extract()
yield scrapy.Request(sel.xpath('#href').extract()[0], callback=self.parse_3, meta={'item': item})
# again, yield the Request object
def parse_3(self, response):
item=response.meta['item']
item['Website'] = response.xpath('.//[#id="mn-memberinfo-block-website"]/a/#href').extract()
# OK, finally assume you're done, just return the item object
return item
There are quite a few mistakes you've made in your code therefore it's not running as you expected. Please see my below brief sample how to get the items you need and passing the meta to other callbacks. I am not copying your xpath as I just grab the most straight forward one from the site, you can apply your own.
I will try to comment as clear as possible to let you know where you did wrong.
class PschamberSpider(scrapy.Spider):
name = "pschamber"
# start from this, since your domain is a sub-domain on its own,
# you need to change to this without http://
allowed_domains = ["cm.pschamber.com"]
start_urls = (
'http://cm.pschamber.com/list/',
)
def parse(self, response):
item = PschamberItem()
for sel in response.xpath('//div[#id="mn-ql"]//a'):
# xpath and xpath().extract() will return a list
# extract()[0] will return the first element in the list
item['industry'] = sel.xpath('text()').extract()[0]
# another mistake you made here
# you're trying to call scrapy.Request(LIST of hrefs) which will fail
# scrapy.Request only takes a url string, not list
# another big mistake is you're trying to yield the item,
# whereas you should yield the Request object
yield scrapy.Request(
sel.xpath('#href').extract()[0],
callback=self.parse_2,
meta={'item': item}
)
# another mistake, your callback function DOESNT take item as argument
def parse_2(self, response):
for sel in response.xpath('//div[#class="mn-title"]//a'):
# you can access your response meta like this
item = response.meta['item']
item['company'] = sel.xpath('text()').extract()[0]
# again, yield the Request object
yield scrapy.Request(
sel.xpath('#href').extract()[0],
callback=self.parse_3,
meta={'item': item}
)
def parse_3(self, response):
item = response.meta['item']
item['website'] = response.xpath('//a[#class="mn-print-url"]/text()').extract()
# OK, finally assume you're done, just return the item object
return item
Hope this is self-explanatory and you get to understand the basic of scrapy, you should READ thoroughly the doc from Scrapy, and sooner you will learn another method to set rules to follow links with certain patterns... well of course once you get the basic right you will understand them.
Although everyone's journey differs, I strongly recommend you keep reading and practice until you're confident in what you're doing before crawling actual website. Also, there are rules to protect web contents which can be scraped, and copyright about the content you scrape.
Keep this in mind or you may find yourself in big trouble in future. Anyway, good luck and I hope this answer helps you resolve the problem!

Scrapy - parse a page to extract items - then follow and store item url contents

I have a question on how to do this thing in scrapy. I have a spider that crawls for listing pages of items.
Every time a listing page is found, with items, there's the parse_item() callback that is called for extracting items data, and yielding items. So far so good, everything works great.
But each item, has among other data, an url, with more details on that item. I want to follow that url and store in another item field (url_contents) the fetched contents of that item's url.
And I'm not sure how to organize code to achieve that, since the two links (listings link, and one particular item link) are followed differently, with callbacks called at different times, but I have to correlate them in the same item processing.
My code so far looks like this:
class MySpider(CrawlSpider):
name = "example.com"
allowed_domains = ["example.com"]
start_urls = [
"http://www.example.com/?q=example",
]
rules = (
Rule(SgmlLinkExtractor(allow=('example\.com', 'start='), deny=('sort='), restrict_xpaths = '//div[#class="pagination"]'), callback='parse_item'),
Rule(SgmlLinkExtractor(allow=('item\/detail', )), follow = False),
)
def parse_item(self, response):
main_selector = HtmlXPathSelector(response)
xpath = '//h2[#class="title"]'
sub_selectors = main_selector.select(xpath)
for sel in sub_selectors:
item = ExampleItem()
l = ExampleLoader(item = item, selector = sel)
l.add_xpath('title', 'a[#title]/#title')
......
yield l.load_item()
After some testing and thinking, I found this solution that works for me.
The idea is to use just the first rule, that gives you listings of items, and also, very important, add follow=True to that rule.
And in parse_item() you have to yield a request instead of an item, but after you load the item. The request is to item detail url. And you have to send the loaded item to that request callback. You do your job with the response, and there is where you yield the item.
So the finish of parse_item() will look like this:
itemloaded = l.load_item()
# fill url contents
url = sel.select(item_url_xpath).extract()[0]
request = Request(url, callback = lambda r: self.parse_url_contents(r))
request.meta['item'] = itemloaded
yield request
And then parse_url_contents() will look like this:
def parse_url_contents(self, response):
item = response.request.meta['item']
item['url_contents'] = response.body
yield item
If anyone has another (better) approach, let us know.
Stefan
I'm sitting with exactly the same problem, and from the fact that no-one has answered your question for 2 days I take it that the only solution is to follow that URL manually, from within your parse_item function.
I'm new to Scrapy, so I wouldn't attempt it with that (although I'm sure it's possible), but my solution will be to use urllib and BeatifulSoup to load the second page manually, extract that information myself, and save it as part of the Item. Yes, much more trouble than Scrapy makes normal parsing, but it should get the job done with the least hassle.

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