├── autohome_spider ├── autohome_spider │ ├── __init__.py │ ├── __pycache__ │ │ ├── items.cpython-311.pyc │ │ ├── __init__.cpython-311.pyc │ │ ├── settings.cpython-311.pyc │ │ └── pipelines.cpython-311.pyc │ ├── spiders │ │ ├── __pycache__ │ │ │ └── __init__.cpython-311.pyc │ │ └── __init__.py │ ├── items.py │ ├── pipelines.py │ ├── settings.py │ └── middlewares.py ├── run.bat ├── requirements.txt ├── error.log ├── autohome_clean.csv ├── scrapy.cfg ├── clean_and_show.py └── code_method.md ├── imgs ├── 时间分析.png ├── 系统架构.png ├── 爬虫运行结果.png ├── 各种数据的占比统计.png ├── 持久化存储结果.png └── 保修是否存在与其他数据的关系.png ├── 项目报告模板.docx ├── 项目答辩PPT.pptx ├── .vscode └── launch.json ├── README.md └── LICENSE /autohome_spider/autohome_spider/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /autohome_spider/run.bat: -------------------------------------------------------------------------------- 1 | pip install -r requirements.txt 2 | scrapy crawl autohome_spider -------------------------------------------------------------------------------- /autohome_spider/requirements.txt: -------------------------------------------------------------------------------- 1 | scrapy 2 | pandas 3 | matplotlib 4 | seaborn 5 | numpy -------------------------------------------------------------------------------- /imgs/时间分析.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shandianchengzi/car_home_spider/HEAD/imgs/时间分析.png -------------------------------------------------------------------------------- /imgs/系统架构.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shandianchengzi/car_home_spider/HEAD/imgs/系统架构.png 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-------------------------------------------------------------------------------- 1 | import scrapy 2 | 3 | # 分析页面内容，可知包含的信息为“品牌”、“上市年份”、“车型”、“实物图”、“表显里程”、“上牌时间”、“价格”、“原厂保修时间”、“所属城市”和详细信息的访问链接。 4 | class AutohomeItem(scrapy.Item): 5 | brand = scrapy.Field() 6 | year = scrapy.Field() 7 | model = scrapy.Field() 8 | mileage = scrapy.Field() 9 | registration_time = scrapy.Field() 10 | price = scrapy.Field() 11 | warranty_time = scrapy.Field() 12 | city = scrapy.Field() 13 | link = scrapy.Field() 14 | -------------------------------------------------------------------------------- /.vscode/launch.json: -------------------------------------------------------------------------------- 1 | { 2 | // 使用 IntelliSense 了解相关属性。 3 | // 悬停以查看现有属性的描述。 4 | // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387 5 | "version": "0.2.0", 6 | "configurations": [ 7 | // scrapy crawl autohome_spider 8 | { 9 | "name": "Python: crawl autohome", 10 | "type": "debugpy", 11 | "request": "launch", 12 | "module": "scrapy", 13 | "console": "integratedTerminal", 14 | "args": [ 15 | "crawl", 16 | "autohome_spider" 17 | ], 18 | "cwd": "${workspaceFolder}/autohome_spider", 19 | "justMyCode": true 20 | }, 21 | { 22 | "name": "Python: Current File", 23 | "type": "python", 24 | "request": "launch", 25 | "program": "${file}", 26 | "console": "integratedTerminal", 27 | "justMyCode": true 28 | } 29 | ] 30 | } -------------------------------------------------------------------------------- /autohome_spider/autohome_spider/pipelines.py: -------------------------------------------------------------------------------- 1 | # Define your item pipelines here 2 | # 3 | # Don't forget to add your pipeline to the ITEM_PIPELINES setting 4 | # See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html 5 | 6 | 7 | # useful for handling different item types with a single interface 8 | from itemadapter import ItemAdapter 9 | import csv 10 | 11 | class AutohomeSpiderPipeline: 12 | def open_spider(self, spider): 13 | self.file = open('autohome.csv', 'w', newline='', encoding='utf-8') 14 | self.writer = csv.DictWriter(self.file, fieldnames=['brand', 'year', 'model', 'mileage', 'registration_time', 'price', 'warranty_time', 'city', 'link']) 15 | # ['品牌', '上市年份', '车型', '表显里程（公里）', '上牌时间（年）', '价格（万）', '原厂保修时间', '所属城市', '链接'] 16 | self.writer.writeheader() 17 | 18 | def close_spider(self, spider): 19 | self.file.close() 20 | 21 | def process_item(self, item, spider): 22 | # let item: {field: [value]} to {field: value} 23 | item = {k: v[0] for k, v in item.items()} 24 | self.writer.writerow(item) 25 | return item 26 | -------------------------------------------------------------------------------- /autohome_spider/autohome_spider/spiders/__init__.py: -------------------------------------------------------------------------------- 1 | import scrapy 2 | from scrapy.loader import ItemLoader 3 | from autohome_spider.items import AutohomeItem 4 | import re 5 | import lxml 6 | 7 | def getNumberAndFloat(text): 8 | result = re.findall(r'\d+\.?\d*', text) 9 | if len(result) == 0: 10 | with open('error.log', 'a', encoding='utf-8') as f: 11 | f.write(text + '\n') 12 | return "" 13 | return result[0] 14 | 15 | # 产品库的二手车页面中，页码也在链接中。比如a0_0msdgscncgpi1lto2csp1ex/对应第一页，a0_0msdgscncgpi1lto2csp2ex/对应第二页。 16 | class AutohomeSpider(scrapy.Spider): 17 | name = 'autohome_spider' 18 | allowed_domains = ['autohome.com.cn'] 19 | base_url = 'https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1lto2csp{}ex/' 20 | page = 1 21 | start_urls = ['https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1lto2csp1ex/'] 22 | 23 | def parse(self, response): 24 | xml = lxml.etree.HTML(response.text) 25 | piclist = xml.xpath('//div[@class="piclist"]/ul/li') 26 | if len(piclist) == 0 or self.page > 100: # max page 100 27 | return 28 | for car in piclist: 29 | try: 30 | l = ItemLoader(item=AutohomeItem()) 31 | title = car.xpath('div[@class="title"]/a/text()')[0] 32 | title_href = car.xpath('div[@class="title"]/a/@href')[0] 33 | somethings = title.split(' ', 2) 34 | if len(somethings) != 3: 35 | with open('error.log', 'a') as f: 36 | f.write(title + '\n') 37 | continue 38 | # 获得icon_list里所有a标签的title属性并拼接非空的为字符串 39 | icon_list = car.xpath('div[@class="icon-list"]/a') 40 | city = car.xpath('div[@class="icon-list"]/span/span/text()')[0] 41 | icons_info = [] 42 | for icon in icon_list: 43 | icon_info = icon.xpath('@title') 44 | if icon_info: 45 | icons_info.extend(icon_info) 46 | icons_info = ', '.join(icons_info) 47 | # 用ItemLoader加载数据 48 | l.add_value('brand', somethings[0]) 49 | l.add_value('year', somethings[1]) 50 | l.add_value('model', somethings[2]) 51 | l.add_value('mileage', getNumberAndFloat(car.xpath('*/div[@class="detail-l"]/p[1]/text()')[0])) 52 | l.add_value('registration_time', getNumberAndFloat(car.xpath('*/div[@class="detail-l"]/p[2]/text()')[0])) 53 | l.add_value('price', car.xpath('*/div[@class="detail-r"]/span/text()')) 54 | l.add_value('warranty_time', icons_info) 55 | l.add_value('city', city) 56 | l.add_value('link', "https:" + title_href) 57 | yield l.load_item() 58 | except Exception as e: 59 | # skip no full information car 60 | pass 61 | 62 | # 下一页 63 | self.page += 1 64 | new_url = self.base_url.format(self.page) 65 | yield scrapy.Request(new_url, callback=self.parse) -------------------------------------------------------------------------------- /autohome_spider/autohome_spider/settings.py: -------------------------------------------------------------------------------- 1 | # Scrapy settings for autohome_spider project 2 | # 3 | # For simplicity, this file contains only settings considered important or 4 | # commonly used. You can find more settings consulting the documentation: 5 | # 6 | # https://docs.scrapy.org/en/latest/topics/settings.html 7 | # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html 8 | # https://docs.scrapy.org/en/latest/topics/spider-middleware.html 9 | 10 | BOT_NAME = "autohome_spider" 11 | 12 | SPIDER_MODULES = ["autohome_spider.spiders"] 13 | NEWSPIDER_MODULE = "autohome_spider.spiders" 14 | 15 | 16 | # Crawl responsibly by identifying yourself (and your website) on the user-agent 17 | #USER_AGENT = "autohome_spider (+http://www.yourdomain.com)" 18 | 19 | # Obey robots.txt rules 20 | ROBOTSTXT_OBEY = True 21 | 22 | # Configure maximum concurrent requests performed by Scrapy (default: 16) 23 | #CONCURRENT_REQUESTS = 32 24 | 25 | # Configure a delay for requests for the same website (default: 0) 26 | # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay 27 | # See also autothrottle settings and docs 28 | #DOWNLOAD_DELAY = 3 29 | # The download delay setting will honor only one of: 30 | #CONCURRENT_REQUESTS_PER_DOMAIN = 16 31 | #CONCURRENT_REQUESTS_PER_IP = 16 32 | 33 | # Disable cookies (enabled by default) 34 | #COOKIES_ENABLED = False 35 | 36 | # Disable Telnet Console (enabled by default) 37 | #TELNETCONSOLE_ENABLED = False 38 | 39 | # Override the default request headers: 40 | #DEFAULT_REQUEST_HEADERS = { 41 | # "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", 42 | # "Accept-Language": "en", 43 | #} 44 | 45 | # Enable or disable spider middlewares 46 | # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html 47 | #SPIDER_MIDDLEWARES = { 48 | # "autohome_spider.middlewares.AutohomeSpiderSpiderMiddleware": 543, 49 | #} 50 | 51 | # Enable or disable downloader middlewares 52 | # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html 53 | #DOWNLOADER_MIDDLEWARES = { 54 | # "autohome_spider.middlewares.AutohomeSpiderDownloaderMiddleware": 543, 55 | #} 56 | 57 | # Enable or disable extensions 58 | # See https://docs.scrapy.org/en/latest/topics/extensions.html 59 | #EXTENSIONS = { 60 | # "scrapy.extensions.telnet.TelnetConsole": None, 61 | #} 62 | 63 | # Configure item pipelines 64 | # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html 65 | ITEM_PIPELINES = { 66 | "autohome_spider.pipelines.AutohomeSpiderPipeline": 300, 67 | } 68 | 69 | # Enable and configure the AutoThrottle extension (disabled by default) 70 | # See https://docs.scrapy.org/en/latest/topics/autothrottle.html 71 | #AUTOTHROTTLE_ENABLED = True 72 | # The initial download delay 73 | #AUTOTHROTTLE_START_DELAY = 5 74 | # The maximum download delay to be set in case of high latencies 75 | #AUTOTHROTTLE_MAX_DELAY = 60 76 | # The average number of requests Scrapy should be sending in parallel to 77 | # each remote server 78 | #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 79 | # Enable showing throttling stats for every response received: 80 | #AUTOTHROTTLE_DEBUG = False 81 | 82 | # Enable and configure HTTP caching (disabled by default) 83 | # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings 84 | #HTTPCACHE_ENABLED = True 85 | #HTTPCACHE_EXPIRATION_SECS = 0 86 | #HTTPCACHE_DIR = "httpcache" 87 | #HTTPCACHE_IGNORE_HTTP_CODES = [] 88 | #HTTPCACHE_STORAGE = "scrapy.extensions.httpcache.FilesystemCacheStorage" 89 | 90 | # Set settings whose default value is deprecated to a future-proof value 91 | REQUEST_FINGERPRINTER_IMPLEMENTATION = "2.7" 92 | TWISTED_REACTOR = "twisted.internet.asyncioreactor.AsyncioSelectorReactor" 93 | FEED_EXPORT_ENCODING = "utf-8" 94 | -------------------------------------------------------------------------------- /autohome_spider/clean_and_show.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | ''' 3 | brand,year,model,mileage,registration_time,price,warranty_time,city,link 4 | ''' 5 | 6 | import pandas as pd 7 | import matplotlib.pyplot as plt 8 | import seaborn as sns 9 | 10 | import os 11 | file_dir = os.path.dirname(__file__) 12 | os.chdir(file_dir) 13 | 14 | # 数据路径和存储结果路径 15 | data_path = 'autohome.csv' 16 | result_path = 'autohome_clean.csv' 17 | imgs_dir = 'imgs' 18 | if not os.path.exists(imgs_dir): 19 | os.makedirs(imgs_dir) 20 | 21 | # 读取数据 22 | df = pd.read_csv(data_path) 23 | 24 | # 数据清洗 25 | # 判断清洗结果是否存在 26 | if os.path.exists(result_path): 27 | df = pd.read_csv(result_path, encoding='gbk') # for windows excel 28 | else: 29 | df.drop_duplicates(inplace=True) # 去重 30 | df.dropna(subset=['brand'], inplace=True) # 去掉品牌为空的数据 31 | # 合并前三列的数据形成字符串，用正则重新解析，查找"xxxx款"，该字符串前面的是brand，后面的是model，中间的是year 32 | df['brand_model_year'] = df['brand'] + df['year'] + df['model'] # 辅助列 33 | df['brand'] = df['brand_model_year'].str.extract(r'^(.*?)(\d{4}款)(.*)')[0] 34 | df['year'] = df['brand_model_year'].str.extract(r'^(.*?)(\d{4}款)(.*)')[1] 35 | df['model'] = df['brand_model_year'].str.extract(r'^(.*?)(\d{4}款)(.*)')[2] 36 | df.drop(columns=['brand_model_year'], inplace=True) 37 | df['year'] = df['year'].replace('款', '', regex=True) 38 | # 如果原厂保修时间为空，填充为0，否则填充1 39 | df['warranty_time_exist'] = df['warranty_time'].notnull().astype(int) 40 | df.to_csv(result_path, index=False, encoding='gbk') # for windows excel 41 | 42 | # 数据可视化 43 | df.columns = ['品牌', '上市年份', '车型', '表显里程（公里）', '上牌时间（年）', '价格（万）', '原厂保修时间', '所属城市', '链接'] 44 | # 设置字体 45 | plt.rcParams['font.sans-serif'] = ['SimHei'] 46 | plt.rcParams['axes.unicode_minus'] = False 47 | # 1. 各种数据的占比统计，全画在一张图上 48 | plt.figure(figsize=(20, 10)) 49 | # 1.1 价格分布(只做0~200万的) 50 | plt.subplot(2, 2, 1) 51 | sns.histplot(df['price'], bins=20, kde=True) 52 | plt.xlim(0, 200) 53 | plt.title('价格分布') 54 | # 1.2 里程分布 55 | plt.subplot(2, 2, 2) 56 | sns.histplot(df['mileage'], bins=20, kde=True) 57 | plt.title('里程分布') 58 | # 1.3 品牌分布（前20，扇形图，不要x和y的label） 59 | plt.subplot(2, 2, 3) 60 | df['brand'].value_counts().head(20).plot.pie(autopct='%1.1f%%') 61 | plt.ylabel('') 62 | plt.title('品牌数量分布（前20）') 63 | # 1.4 车型分布(前10，扇形图，不要x和y的label) 64 | plt.subplot(2, 2, 4) 65 | df['model'].value_counts().head(10).plot.pie(autopct='%1.1f%%') 66 | plt.ylabel('') 67 | plt.title('车型数量分布（前10）') 68 | plt.savefig(os.path.join(imgs_dir, '各种数据的占比统计.png')) 69 | # 2 时间分析 70 | plt.figure(figsize=(20, 10)) 71 | # 2.1 上牌时间分布 72 | plt.subplot(1, 2, 1) 73 | sns.histplot(df['registration_time'], bins=20, kde=True) 74 | plt.title('上牌时间分布') 75 | # 2.2 上市年份分布 76 | plt.subplot(1, 2, 2) 77 | sns.histplot(df['year'], bins=20, kde=True) 78 | plt.title('上市年份分布') 79 | plt.savefig(os.path.join(imgs_dir, '时间分析.png')) 80 | # 3. 保修存在性与其他数据的关系，标好图例 81 | plt.figure(figsize=(20, 10)) 82 | # 3.1 保修存在占比（1表示存在，0表示不存在，扇形图） 83 | plt.subplot(2, 2, 1) 84 | df['warranty_time_exist'].value_counts().plot.pie(autopct='%1.1f%%') 85 | plt.legend(['无保修', '有保修']) 86 | plt.ylabel('') 87 | plt.title('保修存在占比') 88 | # 3.2 保修存在与价格的关系 89 | plt.subplot(2, 2, 2) 90 | sns.boxplot(x='warranty_time_exist', y='price', data=df) 91 | plt.xticks([0, 1], ['无保修', '有保修']) 92 | plt.title('保修存在与价格的关系') 93 | # 3.3 保修存在与里程的关系 94 | plt.subplot(2, 2, 3) 95 | sns.boxplot(x='warranty_time_exist', y='mileage', data=df) 96 | plt.xticks([0, 1], ['无保修', '有保修']) 97 | plt.title('保修存在与里程的关系') 98 | # 3.4 保修存在与上牌时间的关系 99 | plt.subplot(2, 2, 4) 100 | sns.boxplot(x='warranty_time_exist', y='registration_time', data=df) 101 | plt.xticks([0, 1], ['无保修', '有保修']) 102 | plt.title('保修存在与上牌时间的关系') 103 | plt.savefig(os.path.join(imgs_dir, '保修是否存在与其他数据的关系.png')) 104 | 105 | 106 | 107 | -------------------------------------------------------------------------------- /autohome_spider/code_method.md: -------------------------------------------------------------------------------- 1 | ### 项目：汽车之家数据爬取、清洗与可视化 2 | 3 | #### 一、Scrapy框架数据爬取 4 | 5 | ##### 使用Scrapy框架进行数据爬取 6 | 7 | 1. **创建Scrapy项目**：在命令行中执行`scrapy startproject autohome_spider`，创建一个新的Scrapy项目。 8 | 9 | 2. **定义Item**：在`items.py`文件中定义数据模型，用于存储爬取的汽车信息。 10 | 11 | ```python 12 | import scrapy 13 | 14 | class AutohomeItem(scrapy.Item): 15 | brand = scrapy.Field() 16 | model = scrapy.Field() 17 | price = scrapy.Field() 18 | fuel_consumption = scrapy.Field() 19 | engine_type = scrapy.Field() 20 | ``` 21 | 22 | 3. **编写Spider**：在`spiders`目录下创建一个Spider，用于爬取数据。 23 | 24 | ```python 25 | import scrapy 26 | from autohome_spider.items import AutohomeItem 27 | 28 | class AutohomeSpider(scrapy.Spider): 29 | name = 'autohome' 30 | allowed_domains = ['autohome.com.cn'] 31 | start_urls = ['https://www.autohome.com.cn/grade/carhtml/A.html'] 32 | 33 | def parse(self, response): 34 | for car in response.css('dl.car-list-text'): 35 | item = AutohomeItem() 36 | item['brand'] = car.css('dt a::text').get() 37 | item['model'] = car.css('dd.h3-tit a::text').get() 38 | item['price'] = car.css('dd.main-price::text').get().strip() 39 | yield item 40 | ``` 41 | 42 | 4. **设置Pipelines**：在`settings.py`中启用Pipelines，并在`pipelines.py`中定义Pipelines，用于处理爬取的数据。 43 | 44 | ```python 45 | # settings.py 46 | ITEM_PIPELINES = { 47 | 'autohome_spider.pipelines.AutohomePipeline': 300, 48 | } 49 | 50 | # pipelines.py 51 | import csv 52 | 53 | class AutohomePipeline: 54 | def open_spider(self, spider): 55 | self.file = open('autohome.csv', 'w', newline='', encoding='utf-8') 56 | self.writer = csv.DictWriter(self.file, fieldnames=['brand', 'model', 'price', 'fuel_consumption', 'engine_type']) 57 | self.writer.writeheader() 58 | 59 | def close_spider(self, spider): 60 | self.file.close() 61 | 62 | def process_item(self, item, spider): 63 | self.writer.writerow(item) 64 | return item 65 | ``` 66 | 67 | #### 二、Pandas数据清洗与可视化 68 | 69 | ##### 使用Pandas进行数据清洗 70 | 数据清洗的主要目的是为了去掉重复和空数据、纠正错误。我之前的解析代码中忽略了品牌的名称中可能携带空格，导致最终解析出来的结果，品牌名称被分割到下一列中，出现错误。数据清洗的过程中可以纠正。 71 | 72 | ```python 73 | import re 74 | 75 | df.drop_duplicates(inplace=True) # 去重 76 | df.dropna(subset=['brand'], inplace=True) # 去掉品牌为空的数据 77 | # 合并前三列的数据形成字符串，用正则重新解析 78 | df['brand'] = df['brand'] + ' ' + df['model'] + ' ' + df['price'] 79 | df['year'] = df['year'].replace('款', '', regex=True) 80 | # df.columns = ['品牌', '上市年份', '车型', '表显里程（公里）', '上牌时间（年）', '价格（万）', '原厂保修时间', '所属城市', '链接'] 81 | df.to_csv(result_path, index=False, encoding='gbk') 82 | ``` 83 | 84 | 1. **读取CSV文件**：使用Pandas读取由Scrapy爬取并存储的CSV文件。 85 | 86 | ```python 87 | import pandas as pd 88 | 89 | df = pd.read_csv('autohome.csv') 90 | ``` 91 | 92 | 2. **数据清洗**：处理缺失值、重复项和异常值。 93 | 94 | ```python 95 | df.drop_duplicates(inplace=True) 96 | df.dropna(inplace=True) 97 | ``` 98 | 99 | 3. **数据转换**：将价格从字符串转换为数值型。 100 | 101 | ```python 102 | df['price'] = df['price'].str.replace('万', '').astype(float) 103 | ``` 104 | 105 | ##### 使用Pandas进行数据可视化 106 | 107 | 1. **导入可视化库**：使用Matplotlib或Seaborn进行数据可视化。 108 | 109 | ```python 110 | import matplotlib.pyplot as plt 111 | import seaborn as sns 112 | ``` 113 | 114 | 2. **价格分布直方图**：查看不同品牌汽车的价格分布。 115 | 116 | ```python 117 | sns.histplot(df['price'], kde=True) 118 | plt.title('Price Distribution of Cars') 119 | plt.show() 120 | ``` 121 | 122 | 3. **品牌价格箱线图**：对比不同品牌汽车的价格范围。 123 | 124 | ```python 125 | sns.boxplot(x='brand', y='price', data=df) 126 | plt.xticks(rotation=90) 127 | plt.title('Price Range by Brand') 128 | plt.show() 129 | ``` 130 | 131 | 通过上述步骤，我们不仅完成了数据的爬取和存储，还进行了有效的数据清洗和可视化分析，为后续的数据挖掘和业务决策提供了有力支持。 -------------------------------------------------------------------------------- /autohome_spider/autohome_spider/middlewares.py: -------------------------------------------------------------------------------- 1 | # Define here the models for your spider middleware 2 | # 3 | # See documentation in: 4 | # https://docs.scrapy.org/en/latest/topics/spider-middleware.html 5 | 6 | from scrapy import signals 7 | 8 | # useful for handling different item types with a single interface 9 | from itemadapter import is_item, ItemAdapter 10 | 11 | 12 | class AutohomeSpiderSpiderMiddleware: 13 | # Not all methods need to be defined. If a method is not defined, 14 | # scrapy acts as if the spider middleware does not modify the 15 | # passed objects. 16 | 17 | @classmethod 18 | def from_crawler(cls, crawler): 19 | # This method is used by Scrapy to create your spiders. 20 | s = cls() 21 | crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) 22 | return s 23 | 24 | def process_spider_input(self, response, spider): 25 | # Called for each response that goes through the spider 26 | # middleware and into the spider. 27 | 28 | # Should return None or raise an exception. 29 | return None 30 | 31 | def process_spider_output(self, response, result, spider): 32 | # Called with the results returned from the Spider, after 33 | # it has processed the response. 34 | 35 | # Must return an iterable of Request, or item objects. 36 | for i in result: 37 | yield i 38 | 39 | def process_spider_exception(self, response, exception, spider): 40 | # Called when a spider or process_spider_input() method 41 | # (from other spider middleware) raises an exception. 42 | 43 | # Should return either None or an iterable of Request or item objects. 44 | pass 45 | 46 | def process_start_requests(self, start_requests, spider): 47 | # Called with the start requests of the spider, and works 48 | # similarly to the process_spider_output() method, except 49 | # that it doesn’t have a response associated. 50 | 51 | # Must return only requests (not items). 52 | for r in start_requests: 53 | yield r 54 | 55 | def spider_opened(self, spider): 56 | spider.logger.info("Spider opened: %s" % spider.name) 57 | 58 | 59 | class AutohomeSpiderDownloaderMiddleware: 60 | # Not all methods need to be defined. If a method is not defined, 61 | # scrapy acts as if the downloader middleware does not modify the 62 | # passed objects. 63 | 64 | @classmethod 65 | def from_crawler(cls, crawler): 66 | # This method is used by Scrapy to create your spiders. 67 | s = cls() 68 | crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) 69 | return s 70 | 71 | def process_request(self, request, spider): 72 | # Called for each request that goes through the downloader 73 | # middleware. 74 | 75 | # Must either: 76 | # - return None: continue processing this request 77 | # - or return a Response object 78 | # - or return a Request object 79 | # - or raise IgnoreRequest: process_exception() methods of 80 | # installed downloader middleware will be called 81 | return None 82 | 83 | def process_response(self, request, response, spider): 84 | # Called with the response returned from the downloader. 85 | 86 | # Must either; 87 | # - return a Response object 88 | # - return a Request object 89 | # - or raise IgnoreRequest 90 | return response 91 | 92 | def process_exception(self, request, exception, spider): 93 | # Called when a download handler or a process_request() 94 | # (from other downloader middleware) raises an exception. 95 | 96 | # Must either: 97 | # - return None: continue processing this exception 98 | # - return a Response object: stops process_exception() chain 99 | # - return a Request object: stops process_exception() chain 100 | pass 101 | 102 | def spider_opened(self, spider): 103 | spider.logger.info("Spider opened: %s" % spider.name) 104 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 开源地址：[https://github.com/shandianchengzi/car_home_spider](https://github.com/shandianchengzi/car_home_spider) 2 | 3 | 使用说明：切换到autohome_spider目录下，运行`run.bat`。 4 | 5 | # 汽车之家大连市二手车车辆数据爬取、清洗与可视化 6 | 7 | ## 一、项目简介 8 | 9 | 本项目旨在通过网络爬虫技术，从“汽车之家”网站的二手车频道自动抓取各类汽车的详细信息，包括品牌、车型、价格、公里数、上牌时间等，然后利用Pandas库对数据进行清洗和可视化分析，为用户提供直观的数据展示和分析结果。通过本项目，用户可以了解到大连市二手车市场的价格分布、常购品牌等信息，为购车提供参考依据。 10 | 11 | ## 二、项目架构 12 | 13 | ### 1）运行环境 14 | 15 | 1. 硬件系统：Intel Core i7, 16GB RAM, NVIDIA GeForce GTX 1050 Ti 16 | 2. 操作系统：Windows 10, Version 20H2 17 | 3. 软件版本：Python 3.8.5, SQLite 3.31.1, Visual Studio Code 1.57.1 18 | 4. 运行时刻：2024年7月13日 23:00 19 | 20 | ### 3）系统架构图 21 | 22 | ![系统架构图](imgs/系统架构.png) 23 | 24 | ### 4）数据库设计 25 | 数据存入csv文件，包含字段：品牌、上市年份、车型、表显里程（公里）、上牌时间（年）、价格（万）、原厂保修时间、所属城市、链接。 26 | 27 | ## 三、项目核心代码说明 28 | ### 1）Scrapy框架数据爬取 29 | 爬取之前，先分析网站信息。 30 | 31 | 1. 确定网页：在网上汽车之家能找到两个网页是卖二手车的，一个在产品库[^1]里、另一个在二手车严选[^2]上。结果显示，2024年7月13日，产品库中的车源更多，有2714条；而二手车严选里只有1934条。因此，本项目选择产品库中的结果。 32 | 33 | 2. 确定请求方式：打开开发者工具并刷新，然后搜索页面上的任意车辆的名称，检查返回的结果对应的请求。结果表明，请求方式为Get，请求的就是网站的链接，同时，没有传递任何Get参数。 34 | 35 | 3. 确定传参模式： 36 | 37 | 1. 筛选机制：注意到网页本身拥有排序功能，这样之后就不需要额外排序。它的排序选项不在参数中，在链接中，比较小众。比如[*a0\_0msdgscncgpi1ltocsp2ex/*](https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1ltocsp2ex/) 38 | 对应默认排序，[*a0\_0msdgscncgpi1lto2cspex/*](https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1lto2cspex/) 39 | 对应价格降序。 40 | 41 | 2. 翻页机制：产品库的二手车页面中，页码也在链接中。比如[*a0\_0msdgscncgpi1lto2csp1ex/*](https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1lto2csp1ex/)对应第一页，[*a0\_0msdgscncgpi1lto2csp2ex/*](https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1lto2csp2ex/)对应第二页。 42 | 43 | 4. 确定结果格式：由于他返回的就是网页，没有json之类的额外信息，所以爬取的结果就是网页上能看到的结果。分析页面内容，可知包含的信息为“品牌”、“上市年份”、“车型”、“实物图”、“表显里程”、“上牌时间”、“价格”、“原厂保修时间”、“所属城市”和详细信息的访问链接。观察到图片采用了懒加载技术，需要额外请求别的链接才能返回，加之数据分析时并不需要图片，因此干脆去掉实物图这一个信息。 44 | 45 | 5. 额外注意事项：特别坑的一点是，页数超过100页时会自动重定向回到100页，而不是返回空，这样代码中加空判断就无法真正停止下来。这里得额外写一个100页的判断。而且也因为这个，最多返回结果数量2400条。考虑到总共也只有2714条，可以认为该数量是可以接受的，因此不更改筛选条件重新跑了。 46 | 47 | 分析好了这些内容之后，再开始用Scrapy写爬虫，代码如下： 48 | 49 | 1. Spider中数据爬取解析： 50 | 51 | ```python 52 | class AutohomeSpider(scrapy.Spider): 53 | name = 'autohome_spider' 54 | allowed_domains = ['autohome.com.cn'] 55 | base_url = 'https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1lto2csp{}ex/' 56 | page = 1 57 | start_urls = ['https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1lto2csp1ex/'] 58 | 59 | def parse(self, response): 60 | xml = lxml.etree.HTML(response.text) 61 | piclist = xml.xpath('//div[@class="piclist"]/ul/li') 62 | if len(piclist) == 0 or self.page > 100: # max page 100 63 | return 64 | for car in piclist: 65 | try: 66 | l = ItemLoader(item=AutohomeItem()) 67 | title = car.xpath('div[@class="title"]/a/text()')[0] 68 | title_href = car.xpath('div[@class="title"]/a/@href')[0] 69 | somethings = title.split(' ', 2) 70 | if len(somethings) != 3: 71 | with open('error.log', 'a') as f: 72 | f.write(title + '\n') 73 | continue 74 | # 获得icon_list里所有a标签的title属性并拼接非空的为字符串 75 | icon_list = car.xpath('div[@class="icon-list"]/a') 76 | city = car.xpath('div[@class="icon-list"]/span/span/text()')[0] 77 | icons_info = [] 78 | for icon in icon_list: 79 | icon_info = icon.xpath('@title') 80 | if icon_info: 81 | icons_info.extend(icon_info) 82 | icons_info = ', '.join(icons_info) 83 | # 用ItemLoader加载数据 84 | l.add_value('brand', somethings[0]) 85 | l.add_value('year', somethings[1]) 86 | l.add_value('model', somethings[2]) 87 | l.add_value('mileage', getNumberAndFloat(car.xpath('*/div[@class="detail-l"]/p[1]/text()')[0])) 88 | l.add_value('registration_time', getNumberAndFloat(car.xpath('*/div[@class="detail-l"]/p[2]/text()')[0])) 89 | l.add_value('price', car.xpath('*/div[@class="detail-r"]/span/text()')) 90 | l.add_value('warranty_time', icons_info) 91 | l.add_value('city', city) 92 | l.add_value('link', "https:" + title_href) 93 | yield l.load_item() 94 | except Exception as e: 95 | # skip no full information car 96 | pass 97 | 98 | # 下一页 99 | self.page += 1 100 | new_url = self.base_url.format(self.page) 101 | yield scrapy.Request(new_url, callback=self.parse) 102 | ``` 103 | 104 | 2. Pipeline中做数据存储： 105 | ```python 106 | class AutohomeSpiderPipeline: 107 | def open_spider(self, spider): 108 | self.file = open('autohome.csv', 'w', newline='', encoding='utf-8') 109 | self.writer = csv.DictWriter(self.file, fieldnames=['brand', 'year', 'model', 'mileage', 'registration_time', 'price', 'warranty_time', 'city', 'link']) 110 | # ['品牌', '上市年份', '车型', '表显里程（公里）', '上牌时间（年）', '价格（万）', '原厂保修时间', '所属城市', '链接'] 111 | self.writer.writeheader() 112 | 113 | def close_spider(self, spider): 114 | self.file.close() 115 | 116 | def process_item(self, item, spider): 117 | # let item: {field: [value]} to {field: value} 118 | item = {k: v[0] for k, v in item.items()} 119 | self.writer.writerow(item) 120 | return item 121 | ``` 122 | ### 2）Pandas数据清洗与可视化 123 | 数据清洗主要是将错误的数据修改正确或者删除，以便进一步分析。我在爬虫代码的解析过程中，对品牌的解析有误，有一些品牌的名称，本身就带空格，我用空格做分割，导致该列被解析到下一列中。代码如下： 124 | 125 | ```python 126 | # 数据清洗 127 | df.drop_duplicates(inplace=True) # 去重 128 | df.dropna(subset=['brand'], inplace=True) # 去掉品牌为空的数据 129 | # 合并前三列的数据形成字符串，用正则重新解析，查找"xxxx款"，该字符串前面的是brand，后面的是model，中间的是year 130 | df['brand_model_year'] = df['brand'] + df['year'] + df['model'] # 辅助列 131 | df['brand'] = df['brand_model_year'].str.extract(r'^(.*?)(\d{4}款)(.*)')[0] 132 | df['year'] = df['brand_model_year'].str.extract(r'^(.*?)(\d{4}款)(.*)')[1] 133 | df['model'] = df['brand_model_year'].str.extract(r'^(.*?)(\d{4}款)(.*)')[2] 134 | df.drop(columns=['brand_model_year'], inplace=True) 135 | df['year'] = df['year'].replace('款', '', regex=True) 136 | # 如果原厂保修时间为空，填充为0 137 | df['warranty_time'].fillna('0', inplace=True) 138 | df.to_csv(result_path, index=False, encoding='gbk') # for windows excel 139 | ``` 140 | 141 | ### 3）Matplotlib数据可视化 142 | 多角度可视化代码如下： 143 | 144 | ```python 145 | # 数据可视化 146 | df.columns = ['品牌', '上市年份', '车型', '表显里程（公里）', '上牌时间（年）', '价格（万）', '原厂保修时间', '所属城市', '链接'] 147 | # 设置字体 148 | plt.rcParams['font.sans-serif'] = ['SimHei'] 149 | plt.rcParams['axes.unicode_minus'] = False 150 | # 1. 各种数据的占比统计，全画在一张图上 151 | plt.figure(figsize=(20, 10)) 152 | # 1.1 价格分布(只做0~200万的) 153 | plt.subplot(2, 2, 1) 154 | sns.histplot(df['price'], bins=20, kde=True) 155 | plt.xlim(0, 200) 156 | plt.title('价格分布') 157 | # 1.2 里程分布 158 | plt.subplot(2, 2, 2) 159 | sns.histplot(df['mileage'], bins=20, kde=True) 160 | plt.title('里程分布') 161 | # 1.3 品牌分布（前20，扇形图，不要x和y的label） 162 | plt.subplot(2, 2, 3) 163 | df['brand'].value_counts().head(20).plot.pie(autopct='%1.1f%%') 164 | plt.ylabel('') 165 | plt.title('品牌数量分布（前20）') 166 | # 1.4 车型分布(前10，扇形图，不要x和y的label) 167 | plt.subplot(2, 2, 4) 168 | df['model'].value_counts().head(10).plot.pie(autopct='%1.1f%%') 169 | plt.ylabel('') 170 | plt.title('车型数量分布（前10）') 171 | plt.savefig(os.path.join(imgs_dir, '各种数据的占比统计.png')) 172 | # 2 时间分析 173 | plt.figure(figsize=(20, 10)) 174 | # 2.1 上牌时间分布 175 | plt.subplot(1, 2, 1) 176 | sns.histplot(df['registration_time'], bins=20, kde=True) 177 | plt.title('上牌时间分布') 178 | # 2.2 上市年份分布 179 | plt.subplot(1, 2, 2) 180 | sns.histplot(df['year'], bins=20, kde=True) 181 | plt.title('上市年份分布') 182 | plt.savefig(os.path.join(imgs_dir, '时间分析.png')) 183 | # 3. 保修存在性与其他数据的关系，标好图例 184 | plt.figure(figsize=(20, 10)) 185 | # 3.1 保修存在占比（1表示存在，0表示不存在，扇形图） 186 | plt.subplot(2, 2, 1) 187 | df['warranty_time_exist'].value_counts().plot.pie(autopct='%1.1f%%') 188 | plt.legend(['无保修', '有保修']) 189 | plt.ylabel('') 190 | plt.title('保修存在占比') 191 | # 3.2 保修存在与价格的关系 192 | plt.subplot(2, 2, 2) 193 | sns.boxplot(x='warranty_time_exist', y='price', data=df) 194 | plt.xticks([0, 1], ['无保修', '有保修']) 195 | plt.title('保修存在与价格的关系') 196 | # 3.3 保修存在与里程的关系 197 | plt.subplot(2, 2, 3) 198 | sns.boxplot(x='warranty_time_exist', y='mileage', data=df) 199 | plt.xticks([0, 1], ['无保修', '有保修']) 200 | plt.title('保修存在与里程的关系') 201 | # 3.4 保修存在与上牌时间的关系 202 | plt.subplot(2, 2, 4) 203 | sns.boxplot(x='warranty_time_exist', y='registration_time', data=df) 204 | plt.xticks([0, 1], ['无保修', '有保修']) 205 | plt.title('保修存在与上牌时间的关系') 206 | plt.savefig(os.path.join(imgs_dir, '保修是否存在与其他数据的关系.png')) 207 | ``` 208 | 209 | ## 四、项目运行截图 210 | ### 1） 爬虫结果截图 211 | 212 | ![爬虫结果](imgs/爬虫运行结果.png) 213 | 214 | ### 2） 持久化存储结果截图 215 | 216 | ![持久化存储结果](imgs/持久化存储结果.png) 217 | 218 | ### 3） 可视化分析图形截图 219 | 220 | 价格、里程、品牌和车型数量分布可视化 221 | ![各种数据的占比统计](imgs/各种数据的占比统计.png) 222 | 223 | 上牌时间和上市时间分布 224 | ![时间分析](imgs/时间分析.png) 225 | 226 | 保修的存在性与价格、里程和上牌时间的关联 227 | ![保修是否存在与其他数据的关系](imgs/保修是否存在与其他数据的关系.png) 228 | 229 | ## 五、总结 230 | 通过本项目，我学习并掌握了网络爬虫的基本原理和方法，以及如何利用Python进行数据处理和可视化分析。在项目中，我使用Scrapy框架爬取了汽车之家网站的二手车信息，然后利用Pandas库对数据进行清洗和整理，最后使用Matplotlib库对数据进行可视化分析。通过这个项目，我不仅提高了自己的数据处理和分析能力，还为大连市二手车市场的价格分布、常购品牌等信息提供了直观的展示和分析结果，为购车提供了参考依据。 231 | 232 | ## 六、参考资料 233 | - [Requests官方文档](https://docs.python-requests.org/en/master/)：[*https://docs.python-requests.org/en/master/*](https://docs.python-requests.org/en/master/) 234 | 235 | - [BeautifulSoup官方文档](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)：[*https://www.crummy.com/software/BeautifulSoup/bs4/doc/*](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) 236 | 237 | - [通义千问AI问答](https://lxblog.com/qianwen/share?shareId=6f7c1cba-e39f-4e1e-8b59-b7477e346fa9)：[*https://lxblog.com/qianwen/share?shareId=6f7c1cba-e39f-4e1e-8b59-b7477e346fa9*](https://lxblog.com/qianwen/share?shareId=6f7c1cba-e39f-4e1e-8b59-b7477e346fa9) 238 | 239 | - [Scrapy笔记（5）- Item详解\_scrapy 240 | item-CSDN博客](https://blog.csdn.net/sdulsj/article/details/52984619#SnippetTab)：[*https://blog.csdn.net/sdulsj/article/details/52984619\#SnippetTab*](https://blog.csdn.net/sdulsj/article/details/52984619#SnippetTab) 241 | 242 | - [Python爬虫实战+数据分析+数据可视化（汽车之家）\_基于大数据技术对汽车交易的可视化分析汽车之家-CSDN博客](https://blog.csdn.net/qq_45821420/article/details/115366180)：[*https://blog.csdn.net/qq\_45821420/article/details/115366180*](https://blog.csdn.net/qq_45821420/article/details/115366180) 243 | 244 | - [python爬虫学习笔记-scrapy框架之start\_url\_scrapy的start 245 | url-CSDN博客](https://blog.csdn.net/weixin_42672765/article/details/85380212)：[*https://blog.csdn.net/weixin\_42672765/article/details/85380212*](https://blog.csdn.net/weixin_42672765/article/details/85380212) 246 | 247 | 248 | [^1]: [*https://car.autohome.com.cn/2sc/dalian/a0\_0msdgscncgpi1lto2csp1ex/*](https://car.autohome.com.cn/2sc/dalian/a0_0msdgscncgpi1lto2csp1ex/) 249 | 250 | [^2]: [*https://www.che168.com/dalian/list/\#pvareaid=100945*](https://www.che168.com/dalian/list/#pvareaid=100945) 251 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------