写在前面这次的爬虫是关于房价信息的抓取，目的在于练习10万以上的数据处理及整站式抓取。数据量的提升最直观的感觉便是对函数逻辑要求的提高，针对Python的特性，谨慎的选择数据结构。以往小数据量的抓取，即使函数逻辑部分重复，I/O请求频率密集，循环套嵌过深，也不过是1~2s的差别，而随着数据规模的提高，这1~2s的差别就有可能扩展成为1~2h。因此对于要抓取数据量较多的网站，可以从两方面着手降低抓取信息的时间成本。1）优化函数逻辑，选择适当的数据结构，符合Pythonic的编程习惯。例如，字符串的合并，使用join（）要比“+”节省内存空间。2）依据I/O密集与CPU密集，选择多线程、多进程并行的执行方式，提高执行效率。一、获取索引包装请求request，设置超时timeout
# 获取列表页面def get_page(url): headers = { 'User-Agent': r'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) ' r'Chrome/45.0.2454.85 Safari/537.36 115Browser/6.0.3', 'Referer': r'http://bj.fangjia.com/ershoufang/', 'Host': r'bj.fangjia.com', 'Connection': 'keep-alive' } timeout = 60 socket.setdefaulttimeout(timeout) # 设置超时 req = request.Request(url, headers=headers) response = request.urlopen(req).read() page = response.decode('utf-8') return page
一级位置：区域信息二级位置：板块信息（根据区域位置得到板块信息，以key_value对的形式存储在dict中）以dict方式存储，可以快速的查询到所要查找的目标。-> {‘朝阳’：{‘工体’，’安贞’，’健翔桥’……}}三级位置：地铁信息（搜索地铁周边房源信息）将所属位置地铁信息，添加至dict中。 -> {‘朝阳’：{‘工体’：{‘5号线’，’10号线’ , ’13号线’}，’安贞’，’健翔桥’……}}对应的url：http://bj.fangjia.com/ershoufang/–r-%E6%9C%9D%E9%98%B3%7Cw-5%E5%8F%B7%E7%BA%BF%7Cb-%E6%83%A0%E6%96%B0%E8%A5%BF%E8%A1%97解码后的url：http://bj.fangjia.com/ershoufang/–r-朝阳|w-5号线|b-惠新西街根据url的参数模式，可以有两种方式获取目的url：1）根据索引路径获得目的url
# 获取房源信息列表(嵌套字典遍历)def get_info_list(search_dict, layer, tmp_list, search_list): layer += 1 # 设置字典层级 for i in range(len(search_dict)): tmp_key = list(search_dict.keys())[i] # 提取当前字典层级key tmp_list.append(tmp_key) # 将当前key值作为索引添加至tmp_list tmp_value = search_dict[tmp_key] if isinstance(tmp_value, str): # 当键值为url时 tmp_list.append(tmp_value) # 将url添加至tmp_list search_list.append(copy.deepcopy(tmp_list)) # 将tmp_list索引url添加至search_list tmp_list = tmp_list[:layer] # 根据层级保留索引 elif tmp_value == '': # 键值为空时跳过 layer -= 2 # 跳出键值层级 tmp_list = tmp_list[:layer] # 根据层级保留索引 else: get_info_list(tmp_value, layer, tmp_list, search_list) # 当键值为列表时，迭代遍历 tmp_list = tmp_list[:layer] return search_list
2）根据dict信息包装url {‘朝阳’：{‘工体’：{‘5号线’}}}参数：—— r-朝阳—— b-工体—— w-5号线组装参数：http://bj.fangjia.com/ershoufang/–r-朝阳|w-5号线|b-工体
1 # 根据参数创建组合url2 def get_compose_url(compose_tmp_url, tag_args, key_args):3 compose_tmp_url_list = [compose_tmp_url, '|' if tag_args != 'r-' else '', tag_args, parse.quote(key_args), ]4 compose_url = ''.join(compose_tmp_url_list)5 return compose_url
二、获取索引页最大页数
# 获取当前索引页面页数的url列表def get_info_pn_list(search_list): fin_search_list = [] for i in range(len(search_list)): print('>>>正在抓取%s' % search_list[i][:3]) search_url = search_list[i][3] try: page = get_page(search_url) except: print('获取页面超时') continue soup = BS(page, 'lxml') # 获取最大页数 pn_num = soup.select('span[class="mr5"]')[0].get_text() rule = re.compile(r'\d+') max_pn = int(rule.findall(pn_num)[1]) # 组装url for pn in range(1, max_pn+1): print('************************正在抓取%s页************************' % pn) pn_rule = re.compile('[|]') fin_url = pn_rule.sub(r'|e-%s|' % pn, search_url, 1) tmp_url_list = copy.deepcopy(search_list[i][:3]) tmp_url_list.append(fin_url) fin_search_list.append(tmp_url_list) return fin_search_list
三、抓取房源信息Tag这是我们要抓取的Tag：[‘区域’, ‘板块’, ‘地铁’, ‘标题’, ‘位置’, ‘平米’, ‘户型’, ‘楼层’, ‘总价’, ‘单位平米价格’]
# 获取tag信息def get_info(fin_search_list, process_i): print('进程%s开始' % process_i) fin_info_list = [] for i in range(len(fin_search_list)): url = fin_search_list[i][3] try: page = get_page(url) except: print('获取tag超时') continue soup = BS(page, 'lxml') title_list = soup.select('a[class="h_name"]') address_list = soup.select('span[class="address]') attr_list = soup.select('span[class="attribute"]') price_list = soup.find_all(attrs={"class": "xq_aprice xq_esf_width"}) # select对于某些属性值（属性值中间包含空格）无法识别，可以用find_all(attrs={})代替 for num in range(20): tag_tmp_list = [] try: title = title_list[num].attrs["title"] print(r'************************正在获取%s************************' % title) address = re.sub('\n', '', address_list[num].get_text()) area = re.search('\d+[\u4E00-\u9FA5]{2}', attr_list[num].get_text()).group(0) layout = re.search('\d[^0-9]\d.', attr_list[num].get_text()).group(0) floor = re.search('\d/\d', attr_list[num].get_text()).group(0) price = re.search('\d+[\u4E00-\u9FA5]', price_list[num].get_text()).group(0) unit_price = re.search('\d+[\u4E00-\u9FA5]/.', price_list[num].get_text()).group(0) tag_tmp_list = copy.deepcopy(fin_search_list[i][:3]) for tag in [title, address, area, layout, floor, price, unit_price]: tag_tmp_list.append(tag) fin_info_list.append(tag_tmp_list) except: print('【抓取失败】') continue print('进程%s结束' % process_i) return fin_info_list
四、分配任务，并行抓取对任务列表进行分片，设置进程池，并行抓取。
# 分配任务def assignment_search_list(fin_search_list, project_num): # project_num每个进程包含的任务数，数值越小，进程数越多 assignment_list = [] fin_search_list_len = len(fin_search_list) for i in range(0, fin_search_list_len, project_num): start = i end = i+project_num assignment_list.append(fin_search_list[start: end]) # 获取列表碎片 return assignment_list
p = Pool(4) # 设置进程池 assignment_list = assignment_search_list(fin_info_pn_list, 3) # 分配任务，用于多进程 result = [] # 多进程结果列表 for i in range(len(assignment_list)): result.append(p.apply_async(get_info, args=(assignment_list[i], i))) p.close() p.join() for result_i in range(len(result)): fin_info_result_list = result[result_i].get() fin_save_list.extend(fin_info_result_list) # 将各个进程获得的列表合并
通过设置进程池并行抓取，时间缩短为单进程抓取时间的3/1，总计时间3h。电脑为4核，经过测试，任务数为3时，在当前电脑运行效率最高。五、将抓取结果存储到excel中，等待可视化数据化处理
# 存储抓取结果def save_excel(fin_info_list, file_name): tag_name = ['区域', '板块', '地铁', '标题', '位置', '平米', '户型', '楼层', '总价', '单位平米价格'] book = xlsxwriter.Workbook(r'C:\Users\Administrator\Desktop\%s.xls' % file_name) # 默认存储在桌面上 tmp = book.add_worksheet() row_num = len(fin_info_list) for i in range(1, row_num): if i == 1: tag_pos = 'A%s' % i tmp.write_row(tag_pos, tag_name) else: con_pos = 'A%s' % i content = fin_info_list[i-1] # -1是因为被表格的表头所占 tmp.write_row(con_pos, content) book.close()
附上源码
#! -*-coding:utf-8-*-# Function: 房价调查# Author：蘭兹from urllib import parse, requestfrom bs4 import BeautifulSoup as BSfrom multiprocessing import Poolimport reimport lxmlimport datetimeimport cProfileimport socketimport copyimport xlsxwriterstarttime = datetime.datetime.now()base_url = r'http://bj.fangjia.com/ershoufang/'test_search_dict = {'昌平': {'霍营': {'13号线': 'http://bj.fangjia.com/ershoufang/--r-%E6%98%8C%E5%B9%B3|w-13%E5%8F%B7%E7%BA%BF|b-%E9%9C%8D%E8%90%A5'}}}search_list = [] # 房源信息url列表tmp_list = [] # 房源信息url缓存列表layer = -1# 获取列表页面def get_page(url): headers = { 'User-Agent': r'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) ' r'Chrome/45.0.2454.85 Safari/537.36 115Browser/6.0.3', 'Referer': r'http://bj.fangjia.com/ershoufang/', 'Host': r'bj.fangjia.com', 'Connection': 'keep-alive' } timeout = 60 socket.setdefaulttimeout(timeout) # 设置超时 req = request.Request(url, headers=headers) response = request.urlopen(req).read() page = response.decode('utf-8') return page# 获取查询关键词dictdef get_search(page, key): soup = BS(page, 'lxml') search_list = soup.find_all(href=re.compile(key), target='') search_dict = {} for i in range(len(search_list)): soup = BS(str(search_list[i]), 'lxml') key = soup.select('a')[0].get_text() value = soup.a.attrs['href'] search_dict[key] = value return search_dict# 获取房源信息列表(嵌套字典遍历)def get_info_list(search_dict, layer, tmp_list, search_list): layer += 1 # 设置字典层级 for i in range(len(search_dict)): tmp_key = list(search_dict.keys())[i] # 提取当前字典层级key tmp_list.append(tmp_key) # 将当前key值作为索引添加至tmp_list tmp_value = search_dict[tmp_key] if isinstance(tmp_value, str): # 当键值为url时 tmp_list.append(tmp_value) # 将url添加至tmp_list search_list.append(copy.deepcopy(tmp_list)) # 将tmp_list索引url添加至search_list tmp_list = tmp_list[:layer] # 根据层级保留索引 elif tmp_value == '': # 键值为空时跳过 layer -= 2 # 跳出键值层级 tmp_list = tmp_list[:layer] # 根据层级保留索引 else: get_info_list(tmp_value, layer, tmp_list, search_list) # 当键值为列表时，迭代遍历 tmp_list = tmp_list[:layer] return search_list# 获取房源信息详情def get_info_pn_list(search_list): fin_search_list = [] for i in range(len(search_list)): print('>>>正在抓取%s' % search_list[i][:3]) search_url = search_list[i][3] try: page = get_page(search_url) except: print('获取页面超时') continue soup = BS(page, 'lxml') # 获取最大页数 pn_num = soup.select('span[class="mr5"]')[0].get_text() rule = re.compile(r'\d+') max_pn = int(rule.findall(pn_num)[1]) # 组装url for pn in range(1, max_pn+1): print('************************正在抓取%s页************************' % pn) pn_rule = re.compile('[|]') fin_url = pn_rule.sub(r'|e-%s|' % pn, search_url, 1) tmp_url_list = copy.deepcopy(search_list[i][:3]) tmp_url_list.append(fin_url) fin_search_list.append(tmp_url_list) return fin_search_list# 获取tag信息def get_info(fin_search_list, process_i): print('进程%s开始' % process_i) fin_info_list = [] for i in range(len(fin_search_list)): url = fin_search_list[i][3] try: page = get_page(url) except: print('获取tag超时') continue soup = BS(page, 'lxml') title_list = soup.select('a[class="h_name"]') address_list = soup.select('span[class="address]') attr_list = soup.select('span[class="attribute"]') price_list = soup.find_all(attrs={"class": "xq_aprice xq_esf_width"}) # select对于某些属性值（属性值中间包含空格）无法识别，可以用find_all(attrs={})代替 for num in range(20): tag_tmp_list = [] try: title = title_list[num].attrs["title"] print(r'************************正在获取%s************************' % title) address = re.sub('\n', '', address_list[num].get_text()) area = re.search('\d+[\u4E00-\u9FA5]{2}', attr_list[num].get_text()).group(0) layout = re.search('\d[^0-9]\d.', attr_list[num].get_text()).group(0) floor = re.search('\d/\d', attr_list[num].get_text()).group(0) price = re.search('\d+[\u4E00-\u9FA5]', price_list[num].get_text()).group(0) unit_price = re.search('\d+[\u4E00-\u9FA5]/.', price_list[num].get_text()).group(0) tag_tmp_list = copy.deepcopy(fin_search_list[i][:3]) for tag in [title, address, area, layout, floor, price, unit_price]: tag_tmp_list.append(tag) fin_info_list.append(tag_tmp_list) except: print('【抓取失败】') continue print('进程%s结束' % process_i) return fin_info_list# 分配任务def assignment_search_list(fin_search_list, project_num): # project_num每个进程包含的任务数，数值越小，进程数越多 assignment_list = [] fin_search_list_len = len(fin_search_list) for i in range(0, fin_search_list_len, project_num): start = i end = i+project_num assignment_list.append(fin_search_list[start: end]) # 获取列表碎片 return assignment_list# 存储抓取结果def save_excel(fin_info_list, file_name): tag_name = ['区域', '板块', '地铁', '标题', '位置', '平米', '户型', '楼层', '总价', '单位平米价格'] book = xlsxwriter.Workbook(r'C:\Users\Administrator\Desktop\%s.xls' % file_name) # 默认存储在桌面上 tmp = book.add_worksheet() row_num = len(fin_info_list) for i in range(1, row_num): if i == 1: tag_pos = 'A%s' % i tmp.write_row(tag_pos, tag_name) else: con_pos = 'A%s' % i content = fin_info_list[i-1] # -1是因为被表格的表头所占 tmp.write_row(con_pos, content) book.close()if __name__ == '__main__': file_name = input(r'抓取完成，输入文件名保存：') fin_save_list = [] # 抓取信息存储列表 # 一级筛选 page = get_page(base_url) search_dict = get_search(page, 'r-') # 二级筛选 for k in search_dict: print(r'************************一级抓取：正在抓取【%s】************************' % k) url = search_dict[k] second_page = get_page(url) second_search_dict = get_search(second_page, 'b-') search_dict[k] = second_search_dict # 三级筛选 for k in search_dict: second_dict = search_dict[k] for s_k in second_dict: print(r'************************二级抓取：正在抓取【%s】************************' % s_k) url = second_dict[s_k] third_page = get_page(url) third_search_dict = get_search(third_page, 'w-') print('%s>%s' % (k, s_k)) second_dict[s_k] = third_search_dict fin_info_list = get_info_list(search_dict, layer, tmp_list, search_list) fin_info_pn_list = get_info_pn_list(fin_info_list) p = Pool(4) # 设置进程池 assignment_list = assignment_search_list(fin_info_pn_list, 2) # 分配任务，用于多进程 result = [] # 多进程结果列表 for i in range(len(assignment_list)): result.append(p.apply_async(get_info, args=(assignment_list[i], i))) p.close() p.join() for result_i in range(len(result)): fin_info_result_list = result[result_i].get() fin_save_list.extend(fin_info_result_list) # 将各个进程获得的列表合并 save_excel(fin_save_list, file_name) endtime = datetime.datetime.now() time = (endtime - starttime).seconds print('总共用时：%s s' % time)
总结：当抓取数据规模越大，对程序逻辑要求就愈严谨，对python语法要求就越熟练。如何写出更加pythonic的语法，也需要不断学习掌握的。以上就是本文的全部内容，希望本文的内容对大家的学习或者工作能带来一定的帮助，同时也希望多多支持！才能做到人在旅途，感悟人生，享受人生。
