Question

我有两个数据框df_1和df_2

df_1是我的主数据帧，而df_2是查找数据帧。

我想测试df_1[‘col_c1’]中的值是否包含df_2[‘col_a2’]中的任何值。
如果是这样（可以有多个匹配项！）；

将值从df_2[‘col_b2’]添加到df_1[‘col_d1’]
将值从df_2[‘col_c2’]添加到df_1[‘col_e1’]

我该如何实现？
我真的不知道，因此我无法为此共享任何代码。

示例df_1

col_a1 | col_b1 | col_c1         | col_d1 | col_e1
----------------------------------------------------
1_001  | aaaaaa | bbbbccccdddd   |        | 
1_002  | zzzzz  | ggggjjjjjkkkkk |        | 
1_003  | pppp   | qqqqffffgggg   |        | 
1_004  | sss    | wwwcccyyy      |        |
1_005  | eeeeee | eecccffffll    |        |
1_006  | tttt   | hhggeeuuuuu    |        |

示例df_2

col_a2 | col_b2 | col_c2
------------------------------
ccc    | 2_001  | some_data_c
jjj    | 2_002  | some_data_j
fff    | 2_003  | some_data_f

所需的输出df_1

col_a1 | col_b1 | col_c1         | col_d1       | col_e1
------------------------------------------------------------------------------
1_001  | aaaaaa | bbbbccccdddd   | 2_001        | some_data_c
1_002  | zzzzz  | ggggjjjjjkkkkk | 2_002        | some_data_j
1_003  | pppp   | qqqqffffgggg   | 2_003        | some_data_f
1_004  | sss    | wwwcccyyy      | 2_001        | some_data_c
1_005  | eeeeee | eecccffffll    | 2_001;2_003  | some_data_c; some_data_f
1_006  | tttt   | hhggeeuuuuu    |              |

df_1大约有45.000行，而df_2大约有45,000行。 16.000行。（还添加了不匹配的行）

我已经为此苦苦挣扎了好几个小时，但我真的不知道。
我不认为合并是一种选择，因为没有完全匹配。
非常感谢您的帮助。

Answer 1

这会解决

df['col_d1'] = df.apply(lambda x: ','.join([df2['col_b2'][i] for i in range(len(df2)) if df2['col_a2'][i] in x.col_c1]), axis=1)

df['col_e1'] = df.apply(lambda x: ','.join([df2['col_c2'][i] for i in range(len(df2)) if df2['col_a2'][i] in x.col_c1]), axis=1)

输出

    col_a1    col_b1            col_c1             col_d1  \
0  1_001     aaaaaa    bbbbccccdddd               2_001     
1  1_002     zzzzz     ggggjjjjjkkkkk             2_002     
2  1_003     pppp      qqqqffffgggg               2_003     
3  1_004     sss       wwwcccyyy                  2_001     
4  1_005     eeeeee    eecccffffll       2_001  , 2_003     

                      col_e1  
0                some_data_c  
1                some_data_j  
2                some_data_f  
3                some_data_c  
4   some_data_c; some_data_f

Answer 2

使用：

#exctract values by df_2["col_a2"] to new column
s = (df_1['col_c1'].str.extractall(f'({"|".join(df_2["col_a2"])})')[0].rename('new')
                   .reset_index(level=1, drop=True))

#repeat rows with duplicated match
df_1 = df_1.join(s)
#add new columns by map
df_1['col_d1'] = df_1['new'].map(df_2.set_index('col_a2')['col_b2'])
df_1['col_e1'] = df_1['new'].map(df_2.set_index('col_a2')['col_c2'])
#aggregate join
cols = df_1.columns.difference(['new','col_d1','col_e1']).tolist()
df = df_1.drop('new', axis=1).groupby(cols).agg(','.join).reset_index()

print (df)
  col_a1  col_b1          col_c1       col_d1                   col_e1
0  1_001  aaaaaa    bbbbccccdddd        2_001              some_data_c
1  1_002   zzzzz  ggggjjjjjkkkkk        2_002              some_data_j
2  1_003    pppp    qqqqffffgggg        2_003              some_data_f
3  1_004     sss       wwwcccyyy        2_001              some_data_c
4  1_005  eeeeee     eecccffffll  2_001,2_003  some_data_c,some_data_f

使用一个数据框作为另一个数据框的查找

2 个答案: