对爬虫、逆向感兴趣的同学可以查看文章，一对一小班V教学：https://blog.csdn.net/weixin_35770067/article/details/142514698

关键词页面爬取代码

from DrissionPage import WebPage, ChromiumOptions
from DataRecorder import Recorder
import time
import random

path = r'C:\Program Files\Google\Chrome\Application\chrome.exe'
ChromiumOptions().set_browser_path(path).save()


def random_sleep():
    """随机延时1-3秒"""
    time.sleep(random.uniform(3, 5))


def main(url, save_name):
    r = Recorder('{}.xlsx'.format(save_name))
    r.add_data(('url', '标题', '店铺名', '销量数', '价格', '地区'))
    page = WebPage()
    #page.timeout = 5
    page = WebPage(timeout=5)  # 初始化时设置超时为5秒
    page.set.retry_times(3)
    page.set.retry_interval(1)

    scroll_step = 1000
    max_scroll_times = 2 #这个数字表示你要爬取多少页数据
    print(f"开始访问: {url}")
    page.get(url)

    captured_urls = set()
    current_scroll_times = 0

    while current_scroll_times < max_scroll_times:
        print("爬取第{}页数据".format(current_scroll_times + 1))
        page_ = input("请点击翻页按钮,点击完成请输入yes:")
        if page_ == "yes":
            try:
                random_sleep()
                # 滚动页面
                page.run_js(f'window.scrollTo(0, document.body.scrollHeight)')
                random_sleep()

                items = page.eles('css:.tbpc-col')
                print(f"本次找到 {len(items)} 个商品")

                for item in items:
                    try:
                        info = {
                            'url': item('tag:a').link,
                            'title': item('css:.title--qJ7Xg_90').text,
                            'shop': item('css:.shopNameText--DmtlsDKm').text,
                            'sales': item('css:.realSales--XZJiepmt').text,
                            'price': item('css:.priceInt--yqqZMJ5a').text,
                            'location': item('css:.procity--wlcT2xH9').text
                        }

                        if info['url'] not in captured_urls:
                            captured_urls.add(info['url'])
                            r.add_data((
                                info['url'],
                                info['title'],
                                info.get('shop', '未知店铺'),
                                info.get('sales', '0'),
                                info.get('price', '价格未知'),
                                info.get('location', '地区未知')
                            ))
                            print(f"已添加商品: {info['title']}")
                    except Exception as e:
                        print(f"处理商品数据时出错: {e}")
                        continue

                current_scroll_times += 1
                print(f"已完成第 {current_scroll_times} 次滚动")

                # 每5次滚动保存一次
                if current_scroll_times % 5 == 0:
                    r.record()
                    print(f"已保存 {len(captured_urls)} 条数据")

                if len(items) == 0:
                    print("未找到新商品，准备退出")
                    break

            except Exception as e:
                print(f"页面处理出错: {e}")
                continue
    r.record()
    print(f"总共抓取商品数量: {len(captured_urls)}")


if __name__ == '__main__':
    # 淘宝关键词搜索页面
    url = 'https://s.taobao.com/search?q=%E6%89%8B%E6%9C%BA%E7%83%AD%E9%94%80%E6%A6%9C&tab=all'
    save_name = 'phone'
    try:
        main(url,save_name)
    except Exception as e:
        print(f"程序出错: {e}")
    finally:
        print("程序结束")

数据分析代码

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import warnings
warnings.filterwarnings('ignore')
# 设置中文字体，避免显示乱码
font = FontProperties(fname=r'C:\Windows\Fonts\SimHei.ttf')
plt.rcParams['axes.unicode_minus'] = False
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 10000)
pd.set_option('display.max_colwidth', 10000)
pd.set_option('display.max_rows', None)


def extract_sales_number(text):
    """从销量文本中提取数字，处理类似'3000+人付款'的格式"""
    if pd.isna(text):
        return 0
    # 移除'+'号和'人付款'字样，只保留数字
    number = str(text).replace('+', '').replace('人付款', '')
    try:
        return int(number)
    except:
        return 0


def analyze_data():
    # 读取Excel文件，跳过前3行
    df = pd.read_excel('手机.xlsx', skiprows=3)

    # 设置列名
    df.columns = ['url', '标题', '店铺名', '销量数', '价格', '地区']

    # 数据清洗
    # 处理销量数据
    df['销量数'] = df['销量数'].apply(extract_sales_number)

    # 处理价格数据
    df['价格'] = pd.to_numeric(df['价格'], errors='coerce')

    # 移除无效数据
    df = df[df['价格'] > 0]
    df = df[df['销量数'] > 0]

    # 1. 价格与销量的关系（柱状图）
    plt.figure(figsize=(15, 8))
    price_bins = [0, 1000, 2000, 3000, 4000, 5000, float('inf')]
    price_labels = ['0-1000', '1000-2000', '2000-3000', '3000-4000', '4000-5000', '5000+']
    df['价格区间'] = pd.cut(df['价格'], bins=price_bins, labels=price_labels)

    price_sales = df.groupby('价格区间')['销量数'].mean()

    plt.bar(price_sales.index, price_sales.values, alpha=0.6, color='skyblue')
    plt.title('手机价格区间与平均销量关系图', fontproperties=font, fontsize=14)
    plt.xlabel('价格区间（元）', fontproperties=font)
    plt.ylabel('平均销量（件）', fontproperties=font)
    plt.xticks(rotation=45, fontproperties=font)
    plt.grid(True, linestyle='--', alpha=0.3)

    # 添加数值标签
    for i, v in enumerate(price_sales.values):
        plt.text(i, v, f'{float(v)}', ha='center', va='bottom', fontproperties=font)

    plt.tight_layout()
    plt.savefig('价格销量关系图.png')
    plt.close()

    # 2. 价格与地区的关系（折线图）
    region_price = df.groupby('地区')['价格'].mean().sort_values(ascending=False)

    plt.figure(figsize=(15, 8))
    plt.plot(region_price.index, region_price.values, marker='o', linewidth=2, markersize=8, color='orange')
    plt.title('各地区手机平均价格对比', fontproperties=font, fontsize=14)
    plt.xlabel('地区', fontproperties=font)
    plt.ylabel('平均价格（元）', fontproperties=font)
    plt.xticks(rotation=45, fontproperties=font)
    plt.grid(True, linestyle='--', alpha=0.7)

    # 添加数值标签
    for i, v in enumerate(region_price.values):
        plt.text(i, v, f'{int(v)}元', ha='center', va='bottom', fontproperties=font)

    plt.tight_layout()
    plt.savefig('地区价格关系图.png')
    plt.close()

    # 输出统计信息
    print("\n=== 数据统计信息 ===")
    print(f"\n总商品数量：{len(df)}个")

    print(f"\n价格统计：")
    price_stats = df['价格'].describe()
    print(f"平均价格：{price_stats['mean']:.2f}元")
    print(f"最高价格：{price_stats['max']:.2f}元")
    print(f"最低价格：{price_stats['min']:.2f}元")

    print(f"\n销量统计：")
    sales_stats = df['销量数'].describe()
    print(f"平均销量：{sales_stats['mean']:.2f}件")
    print(f"最高销量：{int(sales_stats['max'])}件")
    print(f"最低销量：{int(sales_stats['min'])}件")

    print(f"\n各地区平均价格：")
    for region, price in region_price.items():
        print(f"{region}: {price:.2f}元")

    print(f"\n各价格区间的平均销量：")
    for interval, sales in price_sales.items():
        print(f"{interval}: {float(sales)}件")


if __name__ == '__main__':
    try:
        analyze_data()
        print("\n数据分析完成！图表已保存。")
    except Exception as e:
        print(f"数据分析过程中出错: {e}")
        import traceback

        print(traceback.format_exc())