Python3，bs4，网络爬虫；连接太网站错误

Question

我正试图为一个特定的网站建立一个网络爬虫。但出于某种原因我不会连接到这个网站。我犯了一个错误，它不能连接。用selesium打电话给网站，我看到它没有连接

作为一个新手，我可能犯了一个愚蠢的错误，但我想不出是什么。希望你愿意帮助我。

import csv
import requests
import datetime
from time import sleep, time

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from bs4 import BeautifulSoup

browser = webdriver.Chrome('C:/Users/907133/Pythonstuff/chromedriver')
browser.set_window_position(0,0)
captcha = input('Press Enter after bypassing Captcha')

# def get_driver():
#     driver = webdriver.Chrome()
#     return driver


def get_driver():
    # initialize options
    options = webdriver.ChromeOptions()
    # pass in headless argument to options
    options.add_argument('--headless')
    # initialize driver
    driver = webdriver.Chrome(chrome_options=options)
    return driver


def connect_to_base(browser, page_number):
    base_url = f'https://www.jaap.nl/koophuizen/noord+holland/groot-amsterdam/amsterdam/p{page_number}'
    html = None
    links = None
    connection_attempts = 0
    while connection_attempts < 3:
        try:
            browser.get(base_url)
            #wait for table element with id = 'map' to load
            #before returning True
            WebDriverWait(browser, 10).until(
                EC.presence_of_element_located((By.CLASS_NAME, 'result-content')))
            return True
        except Exception as ex:
            connection_attempts += 1
            print(f'Error connecting to {base_url}')
            print(f'Attempt #{connection_attempts}')
    return False


def parse_html(html):
        soup = BeautifulSoup(html, 'html.parser')
        inside = soup.find_all('a', {'class':'property-inner'},{'href'})
        # Make empty lists with header lines
        output_list = []
        listing = 1
        for items in inside:
            href = items.get('href')
            url1 = href.format(page)    
        if len(browser.find_elements_by_xpath("//a[@class='CookiesOK']"))>0:
                browser.find_element_by_xpath("//a[@class='CookiesOK']").click()
        connection_attempts = 0
        while connection_attempts < 3:
            try:
                browser.get(url1)
                WebDriverWait(browser, 5).until(
                    EC.presence_of_element_located((By.CLASS_NAME, 'detail-address')))
                return True
            except Exception as ex:
                connection_attempts += 1
                print(f'Error connecting to {base_url}')
                print(f'Attempt #{connection_attempts}')

            details = BeautifulSoup(browser.page_source, 'html')       
            adres = details.find_all ('div', {'class':'detail-address'})
            for adresinfo in adres:
                try:
                    adres = adres[0].get_text(separator=',', strip=True)
                except Indexerror:
                    adres = "Unknown"

            kenmerken = details.find_all ('div', {'class':'detail-tab-content kenmerken'})
            try:
                tr_kenmerken = ','.join([td.text.strip() for td in kenmerken[0].select('td.value')])
            except IndexError:
                tr_kenmerken = 'Unknown'

            waarde = details.find_all ('div', {'class':'detail-tab-content woningwaarde'})
            try:
                tr_waarde = ','.join([td.text.strip() for td in waarde[0].select('td.value')])
            except IndexError:
                tr_waarde = 'Unknown'

            informatie = {
                'adres': adres, 
                'kenmerken': tr_kenmerken,
                'waarde': tr_waarde, 
                'url': href
            }

            output_list.append(informatie)
            listing += 1
        return output_list


def get_load_time(article_url):
    try:
        # set headers
        headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
        # make get request to article_url
        response = requests.get(
            article_url, headers=headers, stream=True, timeout=3.000)
        # get page load time
        load_time = response.elapsed.total_seconds()
    except Exception as ex:
        load_time = 'Loading Error'
    return load_time


def write_to_file(output_list, filename):
    for row in output_list:
        with open(filename, 'a') as csvfile:
            fieldnames = ['adres', 'kenmerken', 'waarde', 'link']
            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
            writer.writerow(row)


def run_process(page_number, filename, browser):
    if connect_to_base(browser, page_number):
        sleep(2)
        html = browser.page_source
        output_list = parse_html(html)
        write_to_file(output_list, filename)
    else:
        print('Error connecting to jaap')

if __name__ == '__main__':
    # set variables
    start_time = time()
    current_page = 1
    output_timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
    output_filename = f'output_{output_timestamp}.csv'
    browser = get_driver()
    # scrape and crawl
    while current_page <= 3:
        print(f'Scraping page #{current_page}...')
        run_process(current_page, output_filename, browser)
        current_page = current_page + 1
    # exit
    browser.quit()
    end_time = time()
    elapsed_time = end_time - start_time
    print(f'Elapsed run time: {elapsed_time} seconds')

Answer 1

我看到你把EC.presence_of_element_located((By.ID,{'class':'result-content'}))变成了EC.presence_of_element_located((By.CLASS_NAME,'result-content')))

接下来，您可能会遇到一个问题(取决于打开浏览器的位置)，您必须绕过/单击一个javascript，该javascript表示您很好并接受cookie。

但是，考虑到数据作为json格式存储在来自html的script标记中，所有这些代码似乎都是一项艰巨的工作。为什么不简单地使用requests，取出json，转换成dataframe，然后写到csv？

import requests
import datetime
from time import sleep, time
from bs4 import BeautifulSoup
import json
import pandas as pd
from pandas.io.json import json_normalize

def run_process(page_number):
    base_url = f'https://www.jaap.nl/koophuizen/noord+holland/groot-amsterdam/amsterdam/p{page_number}'
    response = requests.get(base_url)
    soup = BeautifulSoup(response.text, 'html.parser')

    jsonStr = soup.find('script', {'id':'page-data'}).text
    jsonData = json.loads(jsonStr)

    df = json_normalize(jsonData['properties'])
    return df



if __name__ == '__main__':
    # set variables
    start_time = time()
    current_page = 1
    output_timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
    output_filename = f'C:/test/output_{output_timestamp}.csv'

    final_df = pd.DataFrame()
    while current_page <= 3:
        print(f'Scraping page #{current_page}...')
        df = run_process(current_page)
        final_df = final_df.append(df, sort=True).reset_index(drop=True)
        current_page = current_page + 1
    final_df.to_csv(output_filename, index=False)
    end_time = time()
    elapsed_time = end_time - start_time
    print(f'Elapsed run time: {elapsed_time} seconds')

输出：

Scraping page #1...
Scraping page #2...
Scraping page #3...
Elapsed run time: 7.441420555114746 seconds

和csv文件，它看起来像：

     app area                                         detailsUrl  expired       houseTypeValue        id  latLng  latLng.latitude  latLng.longitude location.city               location.street location.zipcode   lotSize market numberOfRooms openHouseDate openHouseTimes  openhouse                             photo      price  priceToShow showoffColor showoffCustomText showoffPhotoText  spotlight             status  veiling
0  False  165  /te-koop/noord+holland/groot-amsterdam/amsterd...    False            Herenhuis   6899666     NaN        52.368420          4.833631     AMSTERDAM         Hof van Versailles 61           1064NX       216   sale             4          None           None      False  10014EAAF8B8883668593EFAC9E5FF1C   595000.0     595000.0         None              None             None      False               Sale    False
1   True  211  /te-koop/noord+holland/groot-amsterdam/amsterd...    False          Appartement  10585731     NaN        52.327550          4.889076     AMSTERDAM                Beysterveld 35           1083KA  Onbekend   sale             4          None           None      False  E4F9E5BC7BC90B5B92C7BD8D48B7A677   925000.0     925000.0         None              None             None      False               Sale    False
2   True  111  /te-koop/noord+holland/groot-amsterdam/amsterd...    False  Dubbele bovenwoning  11731386     NaN        52.341890          4.896053     AMSTERDAM      Uiterwaardenstraat 320 2           1079DC  Onbekend   sale             5          None           None      False  AB9F45B2CD4AD7879C5A80F18092F9D4   750000.0     750000.0         None              None             None      False  SoldConditionally    False
3  False  269  /te-koop/noord+holland/groot-amsterdam/amsterd...    False            Herenhuis  11840681     NaN        52.358266          4.875508     AMSTERDAM      Korte van Eeghenstraat 4           1071ER       107   sale             9          None           None      False  A3DF2B1D426B5E4D501503C5D0E66966  3100000.0    3100000.0         None              None             None      False               Sale    False
4  False  100  /te-koop/noord+holland/groot-amsterdam/amsterd...    False         Tussenwoning  12152943     NaN        52.421245          4.899478     AMSTERDAM  Pieter A v Heijningestraat 9           1035SV        83   sale             5          None           None      False  55C6F589523FA553D67A709776DD70DD   399000.0     399000.0         None              None             None      False               Sale    False
5   True  111  /te-koop/noord+holland/groot-amsterdam/amsterd...    False          Bovenwoning  15796874     NaN              NaN               NaN     AMSTERDAM      Eerste Amstelvlietpad 20           1096GB  Onbekend   sale             3          None           None      False  AE822B627ED096310B9ECBE7756340C8  1200000.0    1200000.0         None              None             None      False               Sale    False
6   True   76  /te-koop/noord+holland/groot-amsterdam/amsterd...    False        Benedenwoning  10580650     NaN        52.346010          4.888799     AMSTERDAM       Grevelingenstraat 18 HS           1078KP  Onbekend   sale             2          None           None      False  6FD1011D917E776DCF4DA836B5FFEE3E   550000.0     550000.0         None              None             None      False  SoldConditionally    False
7  False  298  /te-koop/noord+holland/groot-amsterdam/amsterd...    False                Villa   9623182     NaN        52.330610          4.862902     AMSTERDAM                 Cannenburg 51           1081GW       651   sale             7          None           None      False  15FA170B99D4E2DEA03B6FC27E3B5B74  2495000.0    2495000.0         None              None             None      False               Sale    False
8  False  270  /te-koop/noord+holland/groot-amsterdam/amsterd...    False            Herenhuis  15791215     NaN        52.347780          5.004530     AMSTERDAM            Nico Jessekade 189           1087MR       200   sale             9          None           None      False  6EA5C0CDA0475DFC88A3A918A6B2909A  1549000.0    1549000.0         None              None             None      False  SoldConditionally    False
9  False  201  /te-koop/noord+holland/groot-amsterdam/amsterd...    False                Villa   9617942     NaN        52.377391          4.764554     AMSTERDAM               Osdorperweg 803           1067SW      1348   sale             6          None           None      False  4680429D99EC5AC47C950D57A77DF1EB   950000.0     950000.0         None              None             None      False               Sale    False

更新：

import requests
import datetime
from time import sleep, time
from bs4 import BeautifulSoup
import json
import pandas as pd
from pandas.io.json import json_normalize
import numpy as np

def run_process(page_number):
    page_number = 1
    base_url = f'https://www.jaap.nl/koophuizen/noord+holland/groot-amsterdam/amsterdam/p{page_number}'
    response = requests.get(base_url)
    soup = BeautifulSoup(response.text, 'html.parser')

    jsonStr = soup.find('script', {'id':'page-data'}).text
    jsonData = json.loads(jsonStr)


    df = json_normalize(jsonData['properties'])
    root_URL = 'https://jaap.nl'
    df['detailsUrl'] = root_URL + df['detailsUrl']

    allPropDetails = pd.DataFrame()


    for idx, row in df.iterrows():
        propDetails = pd.DataFrame(index=[0])
        w=1
        detailLink = row['detailsUrl']
        print ('Scraping: %s' %(row['location.street']))
        dfs = pd.read_html(detailLink)

        for each in dfs:

            #each = dfs[8]
            w=1
            if each.isnull().all().all():
                continue
            each = each.dropna(axis=0, how='all')

            specialCase = False
            for col in list(each.columns):
                if each[col].dtypes == 'object':
                    if each[col].str.contains('Voorziening').any():
                        specialCase = True
                        break

            if specialCase == True:
                df_obj = each.select_dtypes(['object'])
                each[df_obj.columns] = df_obj.apply(lambda x: x.str.rstrip('. '))
                cols1 = list(each.iloc[2:,0])
                each = each.iloc[2:,:]
                each[1] = each[1] + '---' + each[2]
                each = each.iloc[:,-2]
                each.index = cols1

                each = each.to_frame().T
                propRow = each
                propRow.index = [0]

                temp_df = pd.DataFrame(index=[0])
                for col in propRow.columns:
                    temp_df = temp_df.merge(propRow[col].str.split('---', expand=True).rename(columns={0:col, 1:col+'.distance'}),left_index=True, right_index=True )
                propRow = temp_df


            else:
                df_obj = each.select_dtypes(['object'])
                each[df_obj.columns] = df_obj.apply(lambda x: x.str.rstrip('. '))

                temp_df = each.T
                cols = [ temp_df.index[0] + '_' + colName for colName in list(temp_df.iloc[0,:]) ]

                propRow = temp_df.iloc[-1,:]
                propRow.index = cols
                propRow = propRow.to_frame().T
                propRow.index = [0]

            propDetails = propDetails.merge(propRow, left_index=True, right_index=True)


        propDetails.index = [idx]    
        allPropDetails = allPropDetails.append(propDetails, sort=True)

    df = df.merge(allPropDetails, how = 'left', left_index=True, right_index=True)        
    return df



if __name__ == '__main__':
    # set variables
    start_time = time()
    current_page = 1
    output_timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
    output_filename = f'C:/test/output_{output_timestamp}.csv'

    final_df = pd.DataFrame()
    while current_page <= 3:
        print(f'Scraping page #{current_page}...')
        df = run_process(current_page)
        final_df = final_df.append(df, sort=True).reset_index(drop=True)
        current_page = current_page + 1
    final_df.to_csv(output_filename, index=False)
    end_time = time()
    elapsed_time = end_time - start_time
    print(f'Elapsed run time: {elapsed_time} seconds')