python - Pandas -基于引用字典重复数据框列

Question

我需要根据引用字典重命名和重复我的数据框列。下面我创建了一个虚拟数据框:

rawdata= {'id':['json','molly','tina','jake','molly'],'entity':['present','absent','absent','present','present'],'entity2':['present','present','present','absent','absent'],'entity3':['absent','absent','absent','present','absent']}
df= pd.DataFrame(rawdata)
df.set_index('id')

        entity  entity2  entity3
id                              
json   present  present   absent
molly   absent  present   absent
tina    absent  present   absent
jake   present   absent  present
molly  present   absent   absent

现在我有以下示例字典:

ref_dict= {'entity':['entity_exp1'],'entity2':['entity2_exp1','entity2_exp2'],'entity3':['entity3_exp1','entity3_exp2','entity3_exp3']}

我现在需要根据 dict 值替换列名，如果一列有多个值，则应该重复该列。以下是我想要的数据框:

       entity_exp1  entity2_exp1 entity2_exp2 entity3_exp1 entity3_exp2 entity3_exp3
id                      
json    present      present      present      absent      absent    absent
molly   absent       present      present      absent      absent    absent
tina    absent       present      present      absent      absent    absent
jake    present      absent       absent       present     present   present
molly   present      absent       absent       absent      absent    absent

Answer 1

选项 1
在字典理解上使用 pd.concat

pd.concat({k: df[v] for v, l in ref_dict.items() for k in l}, axis=1)

      entity2_exp1 entity2_exp2 entity3_exp1 entity3_exp2 entity3_exp3 entity_exp1
id                                                                                
json       present      present       absent       absent       absent     present
molly      present      present       absent       absent       absent      absent
tina       present      present       absent       absent       absent      absent
jake        absent       absent      present      present      present     present
molly       absent       absent       absent       absent       absent     present

选项 2
切片数据框并重命名列

repeats = df.columns.map(lambda x: len(ref_dict[x]))
d1 = df.reindex_axis(df.columns.repeat(repeats), 1)
d1.columns = df.columns.map(ref_dict.get).values.sum()
d1

      entity_exp1 entity2_exp1 entity2_exp2 entity3_exp1 entity3_exp2 entity3_exp3
id                                                                                
json      present      present      present       absent       absent       absent
molly      absent      present      present       absent       absent       absent
tina       absent      present      present       absent       absent       absent
jake      present       absent       absent      present      present      present
molly     present       absent       absent       absent       absent       absent