目录
一、前言
二、爬虫与数据处理
三、模型
一、前言
爬取数据仅用于练习和学习。本文运用二手房规格sepc(如3室2厅1卫)和二手房面积area预测二手房价格price，只是练习和学习，不代表如何实际意义。
二、爬虫与数据处理
import requests
import chardet
import pandas as pd
import time
from lxml import etree
from fake_useragent import UserAgentua = UserAgent()
user_agent = ua.random
print(user_agent)url = 'https://gy.58.com/ershoufang/'
headers = {'User-Agent':user_agent
}resp = requests.get(url=url, headers=headers)
encoding = chardet.detect(resp.content)['encoding']
resp.encoding = encoding
page_text = resp.texttree = etree.HTML(page_text)
page_num_data = tree.xpath('//*[@id="esfMain"]/section/section/section/section/div/ul/li/a/text()')
page_num = [item.strip() for item in page_num_data if item.strip().isdigit()]
last_page = int(page_num[-1])total_address_title = []
total_BR_LR_B = []
total_area = []
total_price = []
empty_title = 0
empty_address_data = 0
empty_BR_LR_B_data = 0
empty_area_data = 0
empty_price_data = 0for i in range(1, last_page+1):url = 'https://gy.58.com/ershoufang/p{}/?PGTID=0d100000-007d-f5b6-2cca-9cae0bcabf83&ClickID=1'.format(i)headers = {'User-Agent':user_agent}resp = requests.get(url=url, headers=headers)encoding = chardet.detect(resp.content)['encoding']resp.encoding = encodingpage_text = resp.texttree = etree.HTML(page_text)title = tree.xpath('//*[@id="esfMain"]/section/section/section/section/div/a/div/div/div/h3[@class="property-content-title-name"]/text()')time.sleep(3)address_data = tree.xpath('//*[@id="esfMain"]/section/section/section/section/div/a/div/div/section/div/p[@class="property-content-info-comm-address"]/span/text()')address = [''.join(address_data[i:i+3]) for i in range(0, len(address_data), 3)]time.sleep(3)title_address = [str(address[i]) + '||' + str(title[i]) for i in range(min(len(address), len(title)))]total_address_title.extend(title_address)BR_LR_B_data = tree.xpath('//*[@id="esfMain"]/section/section/section/section/div/a/div/div/section/div/p[@class="property-content-info-text property-content-info-attribute"]/span/text()')BR_LR_B = [''.join(BR_LR_B_data[i:i+6]) for i in range(0, len(BR_LR_B_data), 6)]total_BR_LR_B.extend(BR_LR_B)time.sleep(3)area_data = tree.xpath('//*[@id="esfMain"]/section/section/section/section/div/a/div/div/section/div/p[@class="property-content-info-text"]/text()')area = [item.strip() for item in area_data if '㎡' in item.strip()]total_area.extend(area)time.sleep(3)price_data = tree.xpath('//*[@id="esfMain"]/section/section/section/section/div/a/div/div/p/span[@class="property-price-total-num"]/text()')price = [price + '万' for price in price_data]total_price.extend(price)time.sleep(3)if len(title) == 0:empty_title += 1if len(address_data) == 0:empty_address_data += 1if len(BR_LR_B_data) == 0:empty_BR_LR_B_data += 1if len(area_data) == 0:empty_area_data += 1if len(price_data) == 0:empty_price_data += 1print('Page{} 爬取成功'.format(i))df = pd.DataFrame({'地址': total_address_title,'规格': total_BR_LR_B,'面积': total_area,'价格': total_price
})print(empty_title, empty_address_data, empty_BR_LR_B_data, empty_area_data, empty_price_data)df.to_excel('58二手房信息表.xlsx', index=False, engine='openpyxl')
print('58二手房信息表保存成功!')# 处理表格
df = pd.read_excel('C:\\Users\\sjl\\Desktop\\58Second-hand-house\\58二手房信息表.xlsx')delete_column = '地址'
df = df.drop(delete_column, axis=1) # 删除地址一列df['规格'] = df['规格'].str.replace('室', '')
df['规格'] = df['规格'].str.replace('厅', '')
df['规格'] = df['规格'].str.replace('卫', '')
df['面积'] = df['面积'].str.replace('㎡', '')
df['价格'] = df['价格'].str.replace('万', '') # 删除文字和字符，保留数值df = df.rename(columns={'规格': 'spec', '面积': 'area', '价格': 'price'}) # 重命名列df = df * 0.001 # 缩小数值, 减少计算量df.to_excel('58Second-hand-house.xlsx', index=False, engine='openpyxl')
print('数据处理成功!')1. 运用chardet库自动获取网页编码
import chardet
resp = requests.get(url=url, headers=headers)encoding = chardet.detect(resp.content)['encoding']
resp.encoding = encoding
2. 运用fake_useragent库，生成随机的用户代理字符串，获取一个随机的用户代理来使用
from fake_useragent import UserAgent
ua = UserAgent()
user_agent = ua.random
print(user_agent)
3. 使用列表推导，去除每个元素的空白字符，并保留那些只包含数字的元素，以获取网站页数
page_num = [item.strip() for item in page_num_data if item.strip().isdigit()]
首先使用strip()方法去除其两端的空白字符（包括换行符\n、空格等），接着使用isdigit()方法检查处理后的字符串是否只包含数字。如果条件成立，即字符串只包含数字，那么这个处理后的字符串就会被包含在page_num列表中。
4. 使用列表推导来遍历列表，并将每三个元素组合成一个元素，获取大致地址
address = [''.join(address_data[i:i+3]) for i in range(0, len(address_data), 3)]
首先通过range(0, len(address_data) 3)生成一个从0开始，address_data最后一位长度结束，步长为3的序列。然后，对于序列中的每个i，使用''.join(address_data[i, i+3])连接从i到i+3（不包括i+3）的元素。这样，每三个元素就被拼接成了一个元素，并存储在address中。
5. 考虑到大致地址会有重复，在地址后附加上标题，作为每个二手房独一无二的标志
title_address = [str(address[i]) + '||' + str(title[i]) for i in range(min(len(address), len(title)))]
6. 同样合并'3'，'室'，'2'，'厅'，'1'，'卫'
BR_LR_B = [''.join(BR_LR_B_data[i:i+6]) for i in range(0, len(BR_LR_B_data), 6)]
7. 使用列表推导结合字符串处理方法获得只包含面积部分
area = [item.strip() for item in area_data if '㎡' in item.strip()]
遍历列表，对于每个元素，使用strip()方法去除前后的空格和换行符。检查处理过的字符串是否包含 "㎡" 字符，如果包含，则认为这个字符串表示面积信息。将这些面积信息添加到一个area列表中。
8. 在价格后加上 "万"
price = [price + '万' for price in price_data]
9. 监控得到有9页数据爬取失败
if len(title) == 0:
empty_title += 1
if len(address_data) == 0:
empty_address_data += 1
if len(BR_LR_B_data) == 0:
empty_BR_LR_B_data += 1
if len(area_data) == 0:
empty_area_data += 1
if len(price_data) == 0:
empty_price_data += 1
10. 删除表中的文字
df['规格'] = df['规格'].str.replace('室', '')
df['规格'] = df['规格'].str.replace('厅', '')
df['规格'] = df['规格'].str.replace('卫', '')
df['面积'] = df['面积'].str.replace('㎡', '')
df['价格'] = df['价格'].str.replace('万', '')
11.部分数据展示(处理前后)
delete_column = '地址'
df = df.drop(delete_column, axis=1) # 删除地址一列
df['规格'] = df['规格'].str.replace('室', '')
df['规格'] = df['规格'].str.replace('厅', '')
df['规格'] = df['规格'].str.replace('卫', '')
df['面积'] = df['面积'].str.replace('㎡', '')
df['价格'] = df['价格'].str.replace('万', '') # 删除文字和字符，保留数值
df = df.rename(columns={'规格': 'spec', '面积': 'area', '价格': 'price'}) # 重命名列
df = df * 0.001 # 缩小数值, 减少计算量
三、模型
模型官网：Ml regression in PythonOver 13 examples of ML Regression including changing color, size, log axes, and more in Python.
https://plotly.com/python/ml-regression/
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from sklearn.svm import SVRmesh_size = .02
margin = 0df = pd.read_excel('C:\\Users\\sjl\\Desktop\\58Second-hand-house\\58Second-hand-house.xlsx')X = df[['spec', 'area']]
y = df['price']# Condition the model on sepal width and length, predict the petal width
model = SVR(C=1.)
model.fit(X, y)# Create a mesh grid on which we will run our model
x_min, x_max = X.spec.min() - margin, X.spec.max() + margin
y_min, y_max = X.area.min() - margin, X.area.max() + margin
xrange = np.arange(x_min, x_max, mesh_size)
yrange = np.arange(y_min, y_max, mesh_size)
xx, yy = np.meshgrid(xrange, yrange)# Run model
pred = model.predict(np.c_[xx.ravel(), yy.ravel()])
pred = pred.reshape(xx.shape)# Generate the plot
fig = px.scatter_3d(df, x='spec', y='area', z='price')
fig.update_traces(marker=dict(size=5))
fig.add_traces(go.Surface(x=xrange, y=yrange, z=pred, name='pred_surface'))
fig.show() 
![[c++]多态的原理](https://img-blog.csdnimg.cn/direct/10bde5d2673440e5a22380e7c1d42b9d.png#pic_center)
