复制列元素并基于相关列表应用于另一列

Question

这是一个棘手的问题，我很久以来一直在头痛。我有下面的数据框

dct = {'Store': ('A','A','A','A','A','A','B','B','B','C','C','C'),
       'code_num':('INC101','INC102','INC103','INC104','INC105','INC106','INC201','INC202','INC203','INC301','INC302','INC303'),
       'days':('4','18','9','15','3','6','10','5','3','1','8','5'),
       'products': ('remote','antenna','remote, antenna','TV','display','TV','display, touchpad','speaker','Cell','display','speaker','antenna')
}

df = pd.DataFrame(dct)

pts = {'Primary': ('TV','TV','TV','Cell','Cell'),
         'Related' :('remote','antenna','speaker','display','touchpad')
    
}

parts = pd.DataFrame(pts)

print(df)


   Store code_num days           products
0      A   INC101    4             remote
1      A   INC102    18            antenna
2      A   INC103    9    remote, antenna
3      A   INC104   15                 TV
4      A   INC105    3            display
5      A   INC106    6                 TV
6      B   INC201   10  display, touchpad
7      B   INC202    5            speaker
8      B   INC203    3               Cell
9      C   INC301    1            display
10     C   INC302    8            speaker
11     C   INC303    5            antenna

零件数据框仅供参考，我有另一段代码，它将为每个商店提供相关零件和主要零件的列表

#对于门店A->；电视：[“遥控器”、“天线”、“扬声器]；商店B->；单元格：['display'，'touchpad'] 我期望的数据帧是：

   Store code_num days           products      refer
0      A   INC101    4             remote      INC106
1      A   INC102   18            antenna              -> omitted in 1st pass; because >10 days
2      A   INC103    9    remote, antenna      INC106
3      A   INC104   15                 TV              -> omitted in 1st pass; because >10 days
4      A   INC105    3            display      
5      A   INC106    6                 TV      INC106
6      B   INC201   10  display, touchpad      INC203
7      B   INC202    5            speaker      
8      B   INC203    3               Cell      INC203
9      C   INC301    1            display              -> blank because no primary present
10     C   INC302    8            speaker              -> blank because no primary present
11     C   INC303    5            antenna              -> blank because no primary present

我有一段代码，可以立即执行整个df。但由于其他业务规则，这将是一部分数据。意思2&；3将被省略，因此对于某些记录，.iloc值可能不同。因此，如果您在<=10天，如果它对你有效，那么它对我也有效。
如果需要更多信息，请告诉我。 我知道这很复杂，实际上是个脑筋急转弯

Answer 1

复制了该场景：

你的意见：

dct = {'Store': ('A','A','A','A','A','A','B','B','B','C','C','C'),
       'code_num':('INC101','INC102','INC103','INC104','INC105','INC106','INC201','INC202','INC203','INC301','INC302','INC303'),
       'days':('4','18','9','15','3','6','10','5','3','1','8','5'),
       'products': ('remote','antenna','remote,antenna','TV','display','TV','display,touchpad','speaker','Cell','display','speaker','antenna')
}

df = pd.DataFrame(dct)
pts = {'Primary': ('TV','TV','TV','Cell','Cell'),
         'Related' :('remote','antenna','speaker','display','touchpad')
    
}

parts = pd.DataFrame(pts)
store = {'A':'TV','B':'Cell'}

解决方案：

将零件df转换为字典：

 parts_df_dict = dict(zip(parts['Related'],parts['Primary']))

拆分逗号分隔的子产品并使其分隔行：

new_df = pd.DataFrame(df.products.str.split(',').tolist(), index=df.code_num).stack()
new_df = new_df.reset_index([0, 'code_num'])
new_df.columns = ['code_num', 'Prod_seperated']
new_df = new_df.merge(df, on='code_num', how='left')

创建引用列的逻辑：

store_prod = {}
for k,v in store.items():
    store_prod[k] = k+'_'+v
new_df['prod_store'] = new_df['Store'].map(store_prod)
new_df['p_store'] = new_df['Store'].map(store)
new_df['main_ind'] = ' '
new_df.loc[(new_df['prod_store']==new_df['Store']+'_'+new_df['Prod_seperated'])&(new_df['days'].astype('int')<10),'main_ind']=new_df['code_num']
refer_dic = new_df.groupby('Store')['main_ind'].max().to_dict()
new_df['prod_subproducts'] = new_df['Prod_seperated'].map(parts_df_dict)
new_df['refer']  = np.where((new_df['p_store']==new_df['prod_subproducts'])&(new_df['days'].astype('int')<=10),new_df['Store'].map(refer_dic),np.nan) 

new_df['refer'].fillna(new_df['main_ind'],inplace=True)
new_df.drop(['Prod_seperated','prod_store','p_store','main_ind','prod_subproducts'],axis=1,inplace=True)
new_df.drop_duplicates(inplace=True)

新的或所需的输出：

如果你有任何疑问，请告诉我