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Google Trends 反爬虫

Google Trends 反爬虫应对策略

背景

Google Trends 是一个强大的 SEO 工具,可以帮助用户了解历史、当前和未来的趋势。然而,由于其数据的高价值,许多开发者尝试通过网络爬虫获取这些数据。为了保护数据安全和防止滥用,Google 对爬虫行为进行了限制。

应对策略

  1. 使用官方 API

    • Pytrends 模块: Pytrends 是一个非官方的 Python 模块,可以与 Google Trends 进行通信。虽然它不是官方 API,但可以用于获取趋势数据。
    • 注意事项: 使用 Pytrends 时需要注意,它并非官方 API,滥用可能会导致被 Google 阻止访问。
  2. 延时请求

    • time 模块: 在脚本中使用 time.sleep() 函数来延时请求,减少对 Google 服务器的压力,避免被识别为爬虫。

    • 示例代码:

      import time
      import pytrends
      from pytrends.request import TrendReq
      
      pytrends = TrendReq(hl='zh-CN', tz=360)
      kw_list = ['关键词1', '关键词2']
      pytrends.build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='')
      time.sleep(10)  # 延时 10 秒
      interest_over_time_df = pytrends.interest_over_time()
  3. 处理数据

    • Pandas 模块: 使用 Pandas 处理返回的 JSON 数据,将其转换为 DataFrame 格式,便于分析。

    • 示例代码:

      import pandas as pd
      
      # 获取数据
      interest_over_time_df = pytrends.interest_over_time()
      
      # 处理数据
      interest_over_time_df.drop(columns=['isPartial'], inplace=True)
      interest_over_time_df.fillna(0, inplace=True)
  4. 数据可视化

    • data_table 模块: 使用 data_table 模块增强数据框的可视化效果(适用于 Google Colab)。

    • 示例代码:

      from IPython.display import display
      import data_table
      
      display(data_table.DataTable(interest_over_time_df))
  5. 错误处理

    • 检查数据完整性: 确保获取的数据是完整的,如果没有数据则打印错误信息。
    • 示例代码:
      if interest_over_time_df.empty:
          print("没有获取到数据")
      else:
          print("数据获取成功")
  6. 合并数据

    • 将 Google Trends 数据与 Ahrefs 数据合并: 如果需要更全面的数据,可以将 Google Trends 的数据与 Ahrefs 的数据合并。
    • 示例代码:
      ahrefs_data = pd.read_csv('ahrefs_data.csv')
      combined_data = pd.concat([interest_over_time_df, ahrefs_data], axis=1)

总结

通过以上策略,可以有效地从 Google Trends 获取数据并进行分析,同时避免被 Google 识别为爬虫。使用官方或非官方 API、延时请求、数据处理和可视化等方法,可以确保数据的完整性和安全性。

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