1 import requests 2 import time 3 from bs4 import BeautifulSoup 4 5 #设置列表页URL的固定部分 6 url='http://bj.lianjia.com/ershoufang/' 7 #设置页面页的可变部分 8 page=('pg') 9 10 #设置请求头部信息 11 headers = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11', 12 'Accept':'text/html;q=0.9,*/*;q=0.8', 13 'Accept-Charset':'ISO-8859-1,utf-8;q=0.7,*;q=0.3', 14 'Accept-Encoding':'gzip', 15 'Connection':'close', 16 'Referer':'http://www.baidu.com/link?url=_andhfsjjjKRgEWkj7i9cFmYYGsisrnm2A-TN3XZDQXxvGsM9k9ZZSnikW2Yds4s&wd=&eqid=c3435a7d00006bd600000003582bfd1f' 17 } 18 19 #循环抓取列表页信息 20 for i in range(1,10): 21 if i == 1: 22 i=str(i) 23 a=(url+page+i+'/') 24 r=requests.get(url=a,headers=headers) 25 html=r.content 26 else: 27 i=str(i) 28 a=(url+page+i+'/') 29 r=requests.get(url=a,headers=headers) 30 html2=r.content 31 html = html + html2 32 #每次间隔0.5秒 33 time.sleep(0.5) 34 35 #解析抓取的页面内容 36 lj=BeautifulSoup(html,'html.parser') 37 38 #提取房源总价 39 price=lj.find_all('div',attrs={'class':'priceInfo'}) 40 tp=[] 41 for a in price: 42 totalPrice=a.span.string 43 tp.append(totalPrice) 44 45 #提取房源信息 46 houseInfo=lj.find_all('div',attrs={'class':'houseInfo'}) 47 hi=[] 48 for b in houseInfo: 49 house=b.get_text() 50 hi.append(house) 51 52 #提取房源关注度 53 followInfo=lj.find_all('div',attrs={'class':'followInfo'}) 54 fi=[] 55 for c in followInfo: 56 follow=c.get_text() 57 fi.append(follow) 58 59 #导入pandas库 60 import pandas as pd 61 #创建数据表 62 house=pd.DataFrame({'totalprice':tp,'houseinfo':hi,'followinfo':fi}) 63 #查看数据表的内容 64 house.head() 65 66 #对房源信息进行分列 67 houseinfo_split = pd.DataFrame((x.split('|') for x in house.houseinfo),index=house.index,columns=['xiaoqu','huxing','mianji','chaoxiang','zhuangxiu','dianti']) 68 69 #查看分列结果 70 houseinfo_split.head() 71 72 #将分列结果拼接回原数据表 73 house=pd.merge(house,houseinfo_split,right_index=True, left_index=True) 74 #完成拼接后的数据表中既包含了原有字段，也包含了分列后的新增字段。 75 #查看拼接后的数据表 76 house.head() 77 78 #对房源关注度进行分列 79 followinfo_split = pd.DataFrame((x.split('/') for x in house.followinfo),index=house.index,columns=['guanzhu','daikan','fabu']) 80 #将分列后的关注度信息拼接回原数据表 81 house=pd.merge(house,followinfo_split,right_index=True, left_index=True) 82 83 #按房源户型类别进行汇总 84 huxing=house.groupby('huxing')['huxing'].agg(len) 85 #查看户型汇总结果 86 huxing 87 88 #导入图表库 89 import matplotlib.pyplot as plt 90 #导入数值计算库 91 import numpy as np 92 93 #用len函数计算出huxing的长度 94 l = len(huxing) 95 # 定义一个hx空数组 96 hx=[] 97 for i in range(1,len(huxing)+1): 98 99 hx.append(i) 100 101 #绘制房源户型分布条形图 102 plt.rc('font', family='STXihei', size=11) 103 a=np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]) 104 plt.barh(hx,huxing,color='#052B6C',alpha=0.8,align='center',edgecolor='white') 105 plt.ylabel('户型') 106 plt.xlabel('数量') 107 plt.xlim(0,1300) 108 plt.ylim(0,20) 109 plt.title('房源户型分布情况') 110 plt.legend(['数量'], loc='upper right') 111 plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='y',alpha=0.4) 112 plt.yticks(a,('1室0厅','1室1厅','1室2厅','2室0厅','2室1厅','2室2厅','3室0厅','3室1厅','3室2厅','3室3厅','4室1厅','4室2厅','4室3厅','5室2厅','5室3厅','6室1厅','6室2厅','7室2厅','7室3厅')) 113 plt.show() 114 115 #对房源面积进行二次分列 116 mianji_num_split = pd.DataFrame((x.split('平') for x in house.mianji),index=house.index,columns=['mianji_num','mi']) 117 #将分列后的房源面积拼接回原数据表 118 house=pd.merge(house,mianji_num_split,right_index=True, left_index=True) 119 120 #去除mianji_num字段两端的空格 121 #house['mianji_num']=house['mianji_num'].map(str.strip) 122 123 #更改mianji_num字段格式为float 124 house['mianji_num']=house['mianji_num'].astype(float) 125 126 #查看所有房源面积的范围值 127 house['mianji_num'].min(),house['mianji_num'].max() 128 (18.850000000000001, 332.63) 129 130 131 #对房源面积进行分组 132 bins = [0, 50, 100, 150, 200, 250, 300, 350] 133 group_mianji = ['小于50', '50-100', '100-150', '150-200','200-250','250-300','300-350'] 134 house['group_mianji'] = pd.cut(house['mianji_num'], bins, labels=group_mianji) 135 136 #按房源面积分组对房源数量进行汇总 137 group_mianji=house.groupby('group_mianji')['group_mianji'].agg(len) 138 139 #绘制房源面积分布图 140 plt.rc('font', family='STXihei', size=15) 141 a=np.array([1,2,3,4,5,6,7]) 142 plt.barh([1,2,3,4,5,6,7],group_mianji,color='#052B6C',alpha=0.8,align='center',edgecolor='white') 143 plt.ylabel('面积分组') 144 plt.xlabel('数量') 145 plt.title('房源面积分布') 146 plt.legend(['数量'], loc='upper right') 147 plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='y',alpha=0.4) 148 plt.yticks(a,('小于50', '50-100', '100-150', '150-200','200-250','250-300','300-350')) 149 plt.show() 150 151 #对房源关注度进行二次分列 152 guanzhu_num_split = pd.DataFrame((x.split('人') for x in house.guanzhu),index=house.index,columns=['guanzhu_num','ren']) 153 #将分列后的关注度数据拼接回原数据表 154 house=pd.merge(house,guanzhu_num_split,right_index=True, left_index=True) 155 #去除房源关注度字段两端的空格 156 house['guanzhu_num']=house['guanzhu_num'].map(str.strip) 157 #更改房源关注度及总价字段的格式 158 house[['guanzhu_num','totalprice']]=house[['guanzhu_num','totalprice']].astype(float) 159 160 #查看房源关注度的区间 161 house['guanzhu_num'].min(),house['guanzhu_num'].max() 162 (0.0, 725.0) 163 164 #对房源关注度进行分组 165 bins = [0, 100, 200, 300, 400, 500, 600, 700,800] 166 group_guanzhu = ['小于100', '100-200', '200-300', '300-400','400-500','500-600','600-700','700-800'] 167 house['group_guanzhu'] = pd.cut(house['guanzhu_num'], bins, labels=group_guanzhu) 168 group_guanzhu=house.groupby('group_guanzhu')['group_guanzhu'].agg(len) 169 170 #绘制房源关注度分布图 171 plt.rc('font', family='STXihei', size=15) 172 a=np.array([1,2,3,4,5,6,7,8]) 173 plt.barh([1,2,3,4,5,6,7,8],group_guanzhu,color='#052B6C',alpha=0.8,align='center',edgecolor='white') 174 plt.ylabel('关注度分组') 175 plt.xlabel('数量') 176 plt.xlim(0,3000) 177 plt.title('房源关注度分布') 178 plt.legend(['数量'], loc='upper right') 179 plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='y',alpha=0.4) 180 plt.yticks(a,('小于100', '100-200', '200-300', '300-400','400-500','500-600','600-700','700-800')) 181 plt.show() 182 183 #导入sklearn中的KMeans进行聚类分析 184 from sklearn.cluster import KMeans 185 #使用房源总价，面积和关注度三个字段进行聚类 186 house_type = np.array(house[['totalprice','mianji_num','guanzhu_num']]) 187 #设置质心数量为3 188 clf=KMeans(n_clusters=3) 189 #计算聚类结果 190 clf=clf.fit(house_type) 191 192 #查看分类结果的中心坐标 193 clf.cluster_centers_array([[ 772.97477064, 112.02389908, 58.96330275],[ 434.51073861, 84.92950236, 61.20115244],[ 1473.26719577, 170.65402116, 43.32275132]]) 194 195 #在原数据表中标注所属类别 196 house['label']= clf.labels_