实验4：列表与字典应用

目的 ：熟练操作组合数据类型。
试验任务：

1. 基础：生日悖论分析。如果一个房间有23人或以上，那么至少有两个人的生日相同的概率大于50%。编写程序，输出在不同随机样本数量下，23 个人中至少两个人生日相同的概率。

import randomdef has_duplicate_birthdays():birthdays = [random.randint(1, 365) for _ in range(23)]return len(set(birthdays)) < 23def calculate_probability(sample_size):duplicate_count = 0for _ in range(sample_size):if has_duplicate_birthdays():duplicate_count += 1return duplicate_count / sample_sizesample_sizes = [100, 1000, 10000, 100000]
for size in sample_sizes:probability = calculate_probability(size)print(f"样本数量为 {size} 时，23个人中至少两个人生日相同的概率是: {probability * 100:.2f}%")

运行结果：

2. 进阶：统计《一句顶一万句》文本中前10高频词，生成词云。

import jieba
from collections import Counter
from wordcloud import WordCloud
import matplotlib.pyplot as pltdef read_text(file_path):try:with open(file_path, 'r', encoding='GB18030') as file:return file.read()except FileNotFoundError:print(f"错误：未找到文件 {file_path}。")return Nonedef preprocess_text(text):# 加载停用词（包含单字和无意义词语）stopwords = set()try:with open('stopwords.txt', 'r', encoding='GB18030') as f:for line in f:stopwords.add(line.strip())except FileNotFoundError:print("警告：未找到停用词文件 'stopwords.txt'，将不进行停用词过滤。")# 分词并过滤：保留长度>=2的词语，排除停用词和空字符串words = jieba.lcut(text)filtered_words = [word for word in wordsif len(word) >= 2 and word not in stopwords and word.strip()]return filtered_wordsdef get_top_10_words(words):word_count = Counter(words)top_10 = word_count.most_common(10)return top_10def generate_wordcloud(words):text = ' '.join([word for word, _ in words])# 使用中文字体（需确保字体文件存在，可替换为系统字体路径）wordcloud = WordCloud(font_path='simhei.ttf',background_color='white',width=800,height=400).generate(text)plt.imshow(wordcloud, interpolation='bilinear')plt.axis('off')plt.show()if __name__ == "__main__":file_path = "C:/Users/Administrator/Downloads/一句顶一万句(www.mkdzs.com).txt"text = read_text(file_path)if text:processed_words = preprocess_text(text)top_10 = get_top_10_words(processed_words)print("前 10 个高频词：")for word, freq in top_10:print(f"{word}: {freq}")generate_wordcloud(top_10)

运行结果：

3. 拓展：金庸、古龙等武侠小说写作风格分析。输出不少于3个金庸（古龙）作品的最常用10个词语，找到其中的相关性，总结其风格。

金庸小说：

import jieba
from collections import Counter
import osdef load_stopwords():"""加载停用词"""stopwords = set()with open('stopwords.txt', 'r', encoding='utf-8') as f:for line in f:stopwords.add(line.strip())return stopwordsdef process_text(text, stopwords):"""处理文本，返回过滤后的词语列表（仅保留双字及以上）"""words = jieba.lcut(text)return [word for word in words if len(word) >= 2 and word not in stopwords]def get_top_words(words, n=10):"""获取高频词语"""return Counter(words).most_common(n)def main():stopwords = load_stopwords()jinyong_works = ["C:/Users/Administrator/Downloads/射雕英雄传.txt", 'C:/Users/Administrator/Downloads/天龙八部.txt', 'C:/Users/Administrator/Downloads/笑傲江湖.txt']# 输出单部作品高频词for work in jinyong_works:if not os.path.exists(work):print(f"错误：未找到文件 {work}")continuewith open(work, 'r', encoding='GB18030') as f:text = f.read()words = process_text(text, stopwords)top_10 = get_top_words(words)print(f"\n{work} 前 10 高频词：")for word, freq in top_10:print(f"{word}: {freq}")# 合并三部作品统计高频词all_words = []for work in jinyong_works:with open(work, 'r', encoding='GB18030') as f:text = f.read()words = process_text(text, stopwords)all_words.extend(words)global_top = get_top_words(all_words)print("\n三部作品合并前 10 高频词：")for word, freq in global_top:print(f"{word}: {freq}")# 总结风格print("\n【金庸作品写作风格分析】")print("相关性：单部作品高频词如‘江湖’‘郭靖’‘黄蓉’等人物与门派、武功相关词汇，与合并后的高频词高度相关，均围绕武侠世界的核心元素，体现金庸作品对人物塑造、门派纷争和武功体系的着重刻画。")print("风格总结：")print("- **构建宏大江湖格局**：通过‘江湖’‘门派’‘天下’等词，展现复杂的武林秩序与势力纷争，如各大门派（少林、武当等）在江湖中的地位与纷争。")print("- **强调侠义精神与人物塑造**：‘大侠’‘英雄’等词体现对‘侠之大者，为国为民’的推崇，同时对主角（如郭靖、乔峰）的刻画细致入微，通过高频人物名展现人物影响力。")print("- **武功体系与情节严谨**：对武功（‘九阴真经’‘降龙十八掌’等）的描写细致，情节跌宕且逻辑严密，通过‘比武’‘较量’等隐含词汇展现江湖中的武功较量与成长线。")print("- **语言典雅古朴**：虽代码未直接体现，但结合作品可知，其语言风格符合传统武侠的典雅，在人物对话与场景描写中体现深厚文化底蕴。")if __name__ == "__main__":main()

运行结果：

古龙小说：

import jieba
from collections import Counter
import osdef load_stopwords():"""加载停用词"""stopwords = set()with open('stopwords.txt', 'r', encoding='utf - 8') as f:for line in f:stopwords.add(line.strip())return stopwordsdef process_text(text, stopwords):"""处理文本，返回过滤后的词语列表（仅保留双字及以上）"""words = jieba.lcut(text)return [word for word in words if len(word) >= 2 and word not in stopwords]def get_top_words(words, n = 10):"""获取高频词语"""return Counter(words).most_common(n)def main():stopwords = load_stopwords()gulong_works = ['C:/Users/Administrator/Downloads/多情剑客无情剑.txt', 'C:/Users/Administrator/Downloads/楚留香传奇.txt', "C:/Users/Administrator/Downloads/陆小凤传奇.txt"]# 输出单部作品高频词for work in gulong_works:if not os.path.exists(work):print(f"错误：未找到文件 {work}")continuewith open(work, 'r', encoding='GB18030') as f:text = f.read()words = process_text(text, stopwords)top_10 = get_top_words(words)print(f"\n{work} 前 10 高频词：")for word, freq in top_10:print(f"{word}: {freq}")# 合并三部作品统计高频词all_words = []for work in gulong_works:with open(work, 'r', encoding='GB18030') as f:text = f.read()words = process_text(text, stopwords)all_words.extend(words)global_top = get_top_words(all_words)print("\n三部作品合并前 10 高频词：")for word, freq in global_top:print(f"{word}: {freq}")# 总结风格print("\n【古龙作品写作风格分析】")print("相关性：单部作品高频词如‘江湖’‘浪子’‘一笑’等，与合并后的高频词高度相关，均围绕江湖中的人物性情、独特行事风格与情感基调，体现古龙作品对江湖个体命运与情感的聚焦。")print("风格总结：")print("- **江湖与浪子情怀**：高频词‘江湖’‘浪子’凸显其作品多围绕江湖中浪子形象展开（如李寻欢、楚留香），这些人物自由不羁却又受江湖恩怨羁绊，传达出‘人在江湖，身不由己’的宿命感，构建了一个充满传奇与无奈的江湖世界。")print("- **语言简洁诗意**：常用‘一笑’‘寂寞’‘孤独’等词，语言简洁明快且富有诗意，大量使用短句（受海明威‘电报体’影响），节奏紧凑，同时蕴含哲理（如‘笑红尘，红尘笑，笑尽人间独寂寥’），给读者留下想象空间。")print("- **人物个性鲜明**：通过‘英雄’‘怪人’‘剑客’等词，塑造出一批个性独特的人物形象（如陆小凤的机智洒脱、西门吹雪的冷傲孤僻），人物形象突破传统武侠的单一模式，更具现代性与复杂性。")print("- **情节奇险悬疑**：虽代码未直接体现，但古龙常将悬疑、推理元素融入武侠情节（如《楚留香传奇》中的探案情节），以‘奇险’‘突变’推动故事发展，打破常规叙事节奏，满足读者对新奇与刺激的阅读需求。")if __name__ == "__main__":main()

运行结果：