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import os
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import pandas as pd
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def merge_csv_to_excel(folder_path, output_file):
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# 合并所有弹幕csv文件为一个excel文件
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all_data = pd.DataFrame()
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# 遍历文件夹中的所有文件
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for file_name in os.listdir(folder_path):
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if file_name.endswith('.csv'): # 只处理csv文件
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file_path = os.path.join(folder_path, file_name) # 获取完整路径
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print(f"正在读取文件: {file_path}")
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# 读取csv文件并追加到all_data中
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df = pd.read_csv(file_path)
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all_data = pd.concat([all_data, df], ignore_index=True) # 合并DataFrame
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# 将合并后的DataFrame保存到新的excel文件
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with pd.ExcelWriter(output_file, engine='xlsxwriter') as writer:
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all_data.to_excel(writer, index=False, sheet_name='MergedData')
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print(f"所有弹幕csv文件已合并并保存到: {output_file}")
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def analyze_danmu(input_file, output_nums_file):
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# 分析合并后的excel文件，统计与AI相关的弹幕数量
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df = pd.read_excel(input_file)
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# 假设弹幕内容在名为 '弹幕文本' 的列中
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if '弹幕文本' not in df.columns:
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print("弹幕数据列未找到，请检查列名。")
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print("读取的列名:", df.columns.tolist()) # 打印出读取的列名以帮助调试
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return
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# 定义与 AI 技术应用相关的关键词
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ai_keywords = ['AI', '人工智能', '机器学习', '深度学习']
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# 筛选包含 AI 相关关键词的弹幕
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filtered_danmu = df[df['弹幕文本'].str.contains('|'.join(ai_keywords), na=False)]
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# 统计每种弹幕出现的次数
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danmu_counts = filtered_danmu['弹幕文本'].value_counts()
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# 获取数量排名前8的弹幕
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top_danmu = danmu_counts.head(8)
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# 输出结果到控制台
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print("数量排名前8的弹幕：")
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print(top_danmu)
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# 将结果写入 ai.xlsx 文件
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top_danmu_df = top_danmu.reset_index()
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top_danmu_df.columns = ['弹幕文本', '出现次数'] # 重命名列
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with pd.ExcelWriter(output_nums_file, engine='xlsxwriter') as writer:
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top_danmu_df.to_excel(writer, index=False, sheet_name='TopDanmu')
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print(f"分析结果已保存到: {output_nums_file}")
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if __name__ == '__main__':
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folder_path = '弹幕csv' # 替换为你的 CSV 文件夹路径
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output_file = '合并弹幕.xlsx' # 合并后的输出文件名
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output_nums_file = 'ai.xlsx' # 分析结果输出文件名
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# 合并 CSV 文件为 Excel
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merge_csv_to_excel(folder_path, output_file)
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# 分析弹幕数据并保存结果
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analyze_danmu(output_file, output_nums_file)
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