python - Pandas 如果完整的字符串包含在另一个 Pandas 数据框中

Question

我想使用数据框对零件进行分类。

简化问题以尝试显示问题:

data = {'col1': ['engine','blue engine cover','spark plug',
        'rear panel','black rear panel', 'blue engine']}
desc_df = pd.DataFrame(data=data)

catg = {'bodywork': ['engine cover','side panel','rear panel'],'underhood':['engine','spark plug','oil filter'],
   'Glass':['Windscreen','window','demister']}

catg_df = pd.DataFrame(data=catg)

catg_df


   Glass         bodywork       underhood
0 Windscreen     engine cover   engine 
1 window         side panel     spark plug 
2 demister       rear panel     oil filter 

desc_df

     col1
0   engine 
1 blue engine cover 
2 spark plug 
3 rear panel 
4 black rear panel 
5 blue engine 

我想结束:

  col1                Category
0 engine              underhood 
1 blue engine cover   underhood 
2 spark plug          underhood 
3 rear panel          bodywork 
4 black rear panel    bodywork 
5 blue engine         underhood 

我想到的最接近的是:

d=catg_df.apply('|'.join).to_dict()

desc_df['Category'] = desc_df['col1'].apply(lambda x : ''.join([z if pd.Series(x).str.contains(y).values else '' for z,y in d.items()]))

但我最终在字符串中找到了“engine”和“engine cover”: desc_df

col1                   Category
0 engine              underhood 
1 blue engine cover   bodyworkunderhood 
2 spark plug          underhood 
3 rear panel          bodywork 
4 black rear panel    bodywork 
5 blue engine         underhood 

如果它首先找到“engine Cover”然后使用此类别进行分类并且不会移动到“engine”，是否有一些我可以使用的方法。

Answer 1

一种方法可能是使用 difflib 来获得最接近的值和 lambda:

首先创建一个映射器:

from difflib import get_close_matches
mapper = {val:k for k, v in catg_df.to_dict('list').items() for val in v}
print(mapper)

因此，映射器将是:

{'Windscreen': 'Glass',
 'demister': 'Glass',
 'engine': 'underhood',
 'engine cover': 'bodywork',
 'oil filter': 'underhood',
 'rear panel': 'bodywork',
 'side panel': 'bodywork',
 'spark plug': 'underhood',
 'window': 'Glass'}

现在，使用 lambda 和 difflib 来找到最接近的值:

# avoid calling mapper.keys() in lambda 
keys = mapper.keys()
desc_df['Category'] = desc_df['col1'].apply(lambda row: mapper[get_close_matches(row, keys)[0]])

结果:

                col1   Category
0             engine  underhood
1  blue engine cover   bodywork
2         spark plug  underhood
3         rear panel   bodywork
4   black rear panel   bodywork
5        blue engine  underhood