目录
一、方法2
1. 导入库
2. 构建关键词
3. 构建句子
4. 建立统一索引
5. 表连接
6. 关键词匹配
二、方法2
1. 构建字典
2. 关键词匹配
3. 结果展示
4. 匹配结果展开
一、方法2
此方法是两个表构建某一相同字段,然后全连接,在做匹配结果筛选,此方法针对数据量不大的时候,逻辑比较简单,但是内存消耗较大
1. 导入库 import pandas as pdimport numpy as npimport re 2. 构建关键词 #关键词数据df_keyword = pd.DataFrame({ "keyid" : np.arange(5), "keyword" : ["numpy", "pandas", "matplotlib", "sklearn", "tensorflow"]})df_keyword 3. 构建句子 df_sentence = pd.DataFrame({ "senid" : np.arange(10,17), "sentence" : [ "怎样用pandas实现merge?", "Python之Numpy详细教程", "怎么使用Pandas批量拆分与合并Excel文件?", "怎样使用pandas的map和apply函数?", "深度学习之tensorflow简介", "tensorflow和numpy的关系", "基于sklearn的一些机器学习的代码" ]})df_sentence4. 建立统一索引 df_keyword['match'] = 1df_sentence['match'] = 1 5. 表连接 df_merge = pd.merge(df_keyword, df_sentence)df_merge 6. 关键词匹配 def match_func(row): return re.search(row["keyword"], row["sentence"], re.IGNORECASE) is not Nonedf_merge[df_merge.apply(match_func, axis = 1)]
匹配结果如下
二、方法2此方法对编程能力有要求,在大数据集上计算量较方法一小很多
1. 构建字典 key_word_dict = { row.keyword : row.keyid for row in df_keyword.itertuples()}key_word_dict {'numpy': 0, 'pandas': 1, 'matplotlib': 2, 'sklearn': 3, 'tensorflow': 4} 2. 关键词匹配 def merge_func(row): #新增一列,表示可以匹配的keyid row["keyids"] = [ keyid for key_word, keyid in key_word_dict.items() if re.search(key_word, row["sentence"], re.IGNORECASE) ] return rowdf_merge = df_sentence.apply(merge_func, axis = 1) 3. 结果展示 df_merge 4. 匹配结果展开 df_result = pd.merge(left = df_merge.explode("keyids"),right = df_keyword,left_on = "keyids",right_on = "keyid")df_result