编写循环:Beautifulsoup和lxml用于在页面到页面的跳过设置中获取页面内容 [英] Writing a loop: Beautifulsoup and lxml for getting page-content in a page-to-page skip-setting

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问题描述

更新:现在带有6600多个目标页面之一的图像: https://europa.eu/youth/volunteering/organisation/48592 参见下文-图像以及目标和所需数据的解释与说明.

Update: now with a image of one of the more than 6600 target-pages: https://europa.eu/youth/volunteering/organisation/48592 see below - the images and the explanation and description of the aimed goals and the data which are wanted.

我在志愿服务领域的数据工作领域是一个新手.任何帮助表示赞赏.在过去几天中，我从一些编码英雄那里学到了很多东西，例如αԋɱҽԃαмєяιcαη和KunduK.

I am a pretty new in the field of data work in the field of volunteering services. Any help is appreciated. I have learned a lot in the past few days from some coding heroes such as αԋɱҽԃ αмєяιcαη and KunduK.

基本上，我们的目标是对欧洲免费志愿服务的一系列机会进行快速概述.我有要用于获取数据的URL列表.我可以为一个网址做这样的事情:-目前正在动手研究Python编程:我已经有几个解析器部分已经在工作-请在几页的概述中查看以下内容.顺便说一句:我想我们应该用熊猫收集信息并将其存储在csv中...

Basically our goal is to create a quick overview on a set of opportunities for free volunteering in Europe. I have the list of the URL which I want to use to fetch the data. I can do for one url like this:- currently working on a hands on approach to dive into python programming: i have several parser-parts that work already - see below a overview on several pages. BTW: I guess that we should gather the info with pandas and store it in csv...

  • https://europa.eu/youth/volunteering/organisation/50160
  • https://europa.eu/youth/volunteering/organisation/50162
  • https://europa.eu/youth/volunteering/organisation/50163

...等等，以此类推....-[注意-并非每个URL和ID都有一个内容页面备份-因此我们需要增量n + 1设置]因此我们可以计算页面数每个一个-计算n + 1个增量

...and so forth and so forth .... - [note - not every URL and id is backed up with a content-page - therefore we need an incremental n+1 setting] therefore we can count the pages each by each - and count incremental n+1

请参阅示例:

  • https://europa.eu/youth/volunteering/organisation/48592
  • https://europa.eu/youth/volunteering/organisation/50160

方法:我使用了CSS选择器； XPath和CSS Selector可以完成相同的任务，但是-对于BS或lxml，我们可以使用它，也可以将它们与find()和findall()混合使用.

Approach: I used CSS Selector; XPath and CSS Selector do same task but - with both BS or lxml we can use this or mix with find() and findall().

所以我在这里运行这个迷你方法:

So I run this mini-approach here: 

from bs4 import BeautifulSoup

import requests

url = 'https://europa.eu/youth/volunteering/organisation/50160'

resonse = requests.get(url)

soup = BeautifulSoup(resonse.content, 'lxml')

tag_info = soup.select('.col-md-12 > p:nth-child(3) > i:nth-child(1)')

print(tag_info[0].text)

输出: Norwegian Judo Federation

小型方法2: 

from lxml import html

import requests

url = 'https://europa.eu/youth/volunteering/organisation/50160'

response = requests.get(url)

tree = html.fromstring(response.content)

tag_info = tree.xpath("//p[contains(text(),'Norwegian')]")

print(tag_info[0].text)

输出: Norwegian Judo Federation (NJF) is a center organisation for Norwegian Judo clubs. NJF has 65 member clubs, which have about 4500 active members. 73 % of the members are between ages of 3 and 19. NJF is organized in The Norwegian Olympic and Paralympic Committee and Confederation of Sports (NIF). We are a member organisation in European Judo Union (EJU) and International Judo Federation (IJF). NJF offers and organizes a wide range of educational opportunities to our member clubs.

以此类推.我要达到的目标:目标是从所有6800页中收集所有有趣的信息-这意味着信息，例如:

and so forth and so fort. What I am trying to achieve: aimed is to gather all the interesting information from all the 6800 pages - this means information, such as:

  • 页面的URL以及页面中所有标记为红色的部分
  • 组织名称
  • 地址
  • 组织描述
  • 角色
  • 到期日期
  • 范围
  • 最后更新
  • 组织主题(并非在每个页面上都注明:偶尔出现)

...然后迭代到下一页，获取所有信息等等.因此，我尝试下一步以获得更多经验:...从所有页面中收集信息注意:，我们有 6926个页面

...and iterate to the next page, getting all the information and so forth. So I try a next step to get some more experience:... to gather info form all of the pages Note: we've got 6926 pages

问题是-关于URL，如何确定第一个URL和最后一个URL-想法:如果我们从零迭代到10000，该怎么办！?

The question is - regarding the URLs how to find out which is the first and which is the last URL - idea: what if we iterate from zero to 10 000!?

带有网址的号码！? 

import requests
from bs4 import BeautifulSoup
import pandas as pd

numbers = [48592, 50160]


def Main(url):
    with requests.Session() as req:
        for num in numbers:
            resonse = req.get(url.format(num))
            soup = BeautifulSoup(resonse.content, 'lxml')
            tag_info =soup.select('.col-md-12 > p:nth-child(3) > i:nth-child(1)')
            print(tag_info[0].text)



Main("https://europa.eu/youth/volunteering/organisation/{}/")

但是在这里我遇到了问题.猜想我在结合上述各部分的思想的同时监督了一些事情.再次.我想我们应该用熊猫收集信息并将其存储在csv中...

but here i run into issues. Guess that i have overseen some thing while combining the ideas of the above mentioned parts. Again. I guess that we should gather the infos with pandas and store it in csv...

推荐答案

import requests
from bs4 import BeautifulSoup
import re
import csv
from tqdm import tqdm


first = "https://europa.eu/youth/volunteering/organisations_en?page={}"
second = "https://europa.eu/youth/volunteering/organisation/{}_en"


def catch(url):
    with requests.Session() as req:
        pages = []
        print("Loading All IDS\n")
        for item in tqdm(range(0, 347)):
            r = req.get(url.format(item))
            soup = BeautifulSoup(r.content, 'html.parser')
            numbers = [item.get("href").split("/")[-1].split("_")[0] for item in soup.findAll(
                "a", href=re.compile("^/youth/volunteering/organisation/"), class_="btn btn-default")]
            pages.append(numbers)
        return numbers


def parse(url):
    links = catch(first)
    with requests.Session() as req:
        with open("Data.csv", 'w', newline="", encoding="UTF-8") as f:
            writer = csv.writer(f)
            writer.writerow(["Name", "Address", "Site", "Phone",
                             "Description", "Scope", "Rec", "Send", "PIC", "OID", "Topic"])
            print("\nParsing Now... \n")
            for link in tqdm(links):
                r = req.get(url.format(link))
                soup = BeautifulSoup(r.content, 'html.parser')
                task = soup.find("section", class_="col-sm-12").contents
                name = task[1].text
                add = task[3].find(
                    "i", class_="fa fa-location-arrow fa-lg").parent.text.strip()
                try:
                    site = task[3].find("a", class_="link-default").get("href")
                except:
                    site = "N/A"
                try:
                    phone = task[3].find(
                        "i", class_="fa fa-phone").next_element.strip()
                except:
                    phone = "N/A"
                desc = task[3].find(
                    "h3", class_="eyp-project-heading underline").find_next("p").text
                scope = task[3].findAll("span", class_="pull-right")[1].text
                rec = task[3].select("tbody td")[1].text
                send = task[3].select("tbody td")[-1].text
                pic = task[3].select(
                    "span.vertical-space")[0].text.split(" ")[1]
                oid = task[3].select(
                    "span.vertical-space")[-1].text.split(" ")[1]
                topic = [item.next_element.strip() for item in task[3].select(
                    "i.fa.fa-check.fa-lg")]
                writer.writerow([name, add, site, phone, desc,
                                 scope, rec, send, pic, oid, "".join(topic)])


parse(second)

注意:我已经测试了前10页，如果您希望获得更多的speed，我建议您使用concurrent.futures.以及是否有任何错误.使用try/except.

Note: I've tested for the first 10 pages, in case if you are looking to gain more speed, i advise you to use concurrent.futures. and if there's any error. use try/except.

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