from datasets import Dataset import json from datasets import concatenate_datasets, Dataset arrow_files = ['data/data-00000-of-00003.arrow','data/data-00001-of-00003.arrow','data/data-00002-of-00003.arrow'] ds = concatenate_datasets([Dataset.from_file(arrow_file) for arrow_file in arrow_files]) threshold = 6000 df = ds.to_pandas() df['question_id'] = df['METADATA'].apply(lambda x: int(json.loads(x)['question_id'])) df = df.drop('SOURCE', axis=1) df = df.drop('METADATA', axis=1) df = df[df['upvotes'] > threshold] import pandas as pd pd.set_option('display.max_columns', None) # Show all columns pd.set_option('display.max_rows', None) pd.set_option('display.max_colwidth', 200) # Set display options filtered_df = df[df['INSTRUCTION'].str.contains('吃饭')] class1 = ['船','高中历史', '陈情令','流浪地球', '厦门','鞋','购买','高中化学','考研政治','高中政治','英语','数学','语文','解题','高二','高三','演员','周星驰','王宝强','口吻','编程','免费下载', 'iphone','壁纸','购买的游戏','有什么好玩的手机游戏','诗歌','图片','视频是什么','买什么书','饮品','推荐','做饭','手机','APP', '考研', '5G', '成都', '旅游', '深度学习','如何入门','足球', '篮球','周杰伦', '演唱会','高考','歌','法律','中医','LeetCode', '面试','iPad','工具'] mask = df['INSTRUCTION'].str.contains('|'.join(class1)) class1_df = df[mask] result = df[~df.isin(class1_df).all(axis=1)] threshold = 10000 df = ds.to_pandas() df['question_id'] = df['METADATA'].apply(lambda x: int(json.loads(x)['question_id'])) df = df.drop('SOURCE', axis=1) df = df.drop('METADATA', axis=1) df = df[df['upvotes'] > threshold] filtered_df = df[df['INSTRUCTION'].str.contains('历史')] class1 = ['魔戒','奥运','梅西','船','高中历史', '陈情令','流浪地球', '厦门','鞋','购买','高中化学','考研政治','高中政治','英语','数学','语文','解题','高二','高三','演员','周星驰','王宝强','口吻','编程','免费下载', 'iphone','壁纸','购买的游戏','有什么好玩的手机游戏','诗歌','图片','视频是什么','买什么书','饮品','做饭','手机','APP', '考研', '5G', '成都', '旅游', '深度学习','如何入门','足球', '篮球','周杰伦', '演唱会','高考','歌','法律','中医','LeetCode', '面试','iPad','大罗小罗','破门绝平创历','大气二氧化碳浓度','大雁塔」','帕克太阳探测器','CPU','NBA','霍比特人','冯绍峰','历史中考', '曼联','奥会单板','冬奥会','射手王', '浏览器的历史记录','2020赛季的F2','TES正在开创自己', 'x86','极地涡旋','百度百科', '马刺','chrome'] mask = filtered_df['INSTRUCTION'].str.contains('|'.join(class1)) removedf = filtered_df[mask] resultdf = filtered_df[~filtered_df.isin(removedf).all(axis=1)] grouped_sorted_df = resultdf.sort_values(['question_id','upvotes'], ascending=[False, False]) # grouped_sorted_df['INSTRUCTION'].unique() # grouped_sorted_df idx = -1 with open('history.md', 'w', encoding='utf-8') as f: for index, row in grouped_sorted_df.iterrows(): # Write the question with markdown formatting if idx != row['question_id']: f.write("#### " + row['INSTRUCTION'] +"\n") idx = row['question_id'] f.write("- " +'['+str(row['upvotes'])+'] ' + row['RESPONSE']+"\n") f.write("\n\n")