基于字符串中的子字符串的Python Pandas合并

Question

我有两个数据帧，格式如下：

df_search

SEARCH
part1
anotherpart
onemorepart


df_all

FILE             EXTENSION    PATH
part1_1         .prt    //server/folder1/part1_1
part1_2         .prt    //server/folder2/part1_2
part1_2         .pdf    //server/folder3/part1_2
part1_3         .prt    //server/folder2/part1_3
anotherpart_1   .prt    //server/folder1/anotherpart_1
anotherpart_2   .prt    //server/folder3/anotherpart_2
anotherpart_3   .prt    //server/folder2/anotherpart_3
anotherpart_3   .cgm    //server/folder1/anotherpart_3
anotherpart_4   .prt    //server/folder3/anotherpart_4
onemorepart_1   .prt    //server/folder2/onemorepart_1
onemorepart_2   .prt    //server/folder1/onemorepart_2
onemorepart_2   .dwg    //server/folder2/onemorepart_2
onemorepart_3   .prt    //server/folder1/onemorepart_3
onemorepart_4   .prt    //server/folder1/onemorepart_4

完整的df_搜索有15000个条目。df峈都有55万件物品。我试图根据文件字符串中的搜索项字符串合并两个数据帧。我想要的输出是：

^{pr2}$

简单的数据帧合并不起作用，因为字符串永远不是完全匹配的（它总是一个子字符串）。基于stackoverflow的其他问题，我也尝试了以下方法：

df_all[df_all.name.str.contains('|'.join(df_search.search))]

这给了我一个在df_all中找到的所有项目的完整列表，但是我不知道哪个搜索字符串返回了哪个结果。在

我设法让它与for循环一起工作，但对于我的数据集来说，它很慢（67分钟）：

super_df = []
for search_item in df_search.search:
     df_entire.loc[df_entire.file.str.contains(search_item), 'search'] = search_item
     temp_df = df_entire[df_entire.file.str.contains(search_item)]
super_df = pd.concat(super_df, axis=0, ignore_index=True)

有没有可能通过矢量化来提高性能？在

谢谢

Answer 1

使用^{}+^{}：

pat = "|".join(df_search.SEARCH)
df_all.insert(0, 'SEARCH', df_all['FILE'].str.extract("(" + pat + ')', expand=False))
print (df_all)
         SEARCH           FILE EXTENSION                            PATH
0         part1        part1_1      .prt        //server/folder1/part1_1
1         part1        part1_2      .prt        //server/folder2/part1_2
2         part1        part1_2      .pdf        //server/folder3/part1_2
3         part1        part1_3      .prt        //server/folder2/part1_3
4   anotherpart  anotherpart_1      .prt  //server/folder1/anotherpart_1
5   anotherpart  anotherpart_2      .prt  //server/folder3/anotherpart_2
6   anotherpart  anotherpart_3      .prt  //server/folder2/anotherpart_3
7   anotherpart  anotherpart_3      .cgm  //server/folder1/anotherpart_3
8   anotherpart  anotherpart_4      .prt  //server/folder3/anotherpart_4
9   onemorepart  onemorepart_1      .prt  //server/folder2/onemorepart_1
10  onemorepart  onemorepart_2      .prt  //server/folder1/onemorepart_2
11  onemorepart  onemorepart_2      .dwg  //server/folder2/onemorepart_2
12  onemorepart  onemorepart_3      .prt  //server/folder1/onemorepart_3
13  onemorepart  onemorepart_4      .prt  //server/folder1/onemorepart_4

Answer 2

我会这样做：

df_all['SEARCH'] = ''
for val in df_search.SEARCH:
    df_all.loc[df_all['FILE'].str.match(val), 'SEARCH'] = val