Python 如果另一个pandas数据帧中包含完整字符串，则为pandas

我想使用数据帧对部件进行分类

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 

最后，我想说：

  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 

我得出的最接近的结论是：

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”： 描述

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 

---

如果它先找到“engine Cover”，然后使用此类别进行分类，而不转到“engine”，那么我是否可以使用某种方法来解决此问题。

您可以通过迭代字典来解决此问题：

from collections import OrderedDict

d = OrderedDict([(k, '|'.join(catg_df[k].tolist())) for k in catg_df.columns[::-1]])

for k, v in d.items():
    desc_df.loc[desc_df['col1'].str.contains(v), 'Category'] = k

结果

print(desc_df)

                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

解释

• 对于字典中的每个项目，检查

  str.contains

  条件与正则表达式值，并将键分配给“Category”列
• 使用

  collections.OrderedDict

  为列赋予优先级
• 在这种情况下，可以在构建

  d

  时反转列的迭代顺序

---

一种方法是使用

difflib

获取最接近的值和

lambda

：

首先创建映射器：

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，映射器将如下所示：

{'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

查找最接近的值：

# 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