Pandas 复制列元素并根据相关列表应用于另一列

Question

This is tricky problem and I am banging my head from a long time. I have the following data frame.

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

parts data frame is for reference, I have another piece of code that will provide a list for related parts and primary part for each store.

#For Store A -> TV : ['remote','antenna','speaker'] ; Store B -> Cell :['display','touchpad'] and my expected dataframe is:

   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

I have code that is good for the execution for the whole df at once. But due to other business rules this will be a slice of data . meaning 2 & 3 will be omitted so, .iloc value may be different for some records. So if you subset df on <=10 days and if is working for you then it will work for me.
If any more information is required please let me know. I know it is very complicated and is actually a brain teaser.

Answer 1

Replicated the scenario:

Your inputs :

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'}

Solution:

Converting the parts df to Dictionary :

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

Splitting the comma seperated sub products and making them to seperate rows :

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')

The logic to create the refer column :

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)

new_df or required output :

Please let me know if you have any doubts.