将列表作为熊猫数据框中的列值

Question

I have a dataframe as below :

Card_x  Country Age     Code        Card_y
S       INDIA   Adult   Garments    S,E,D,G,M,A
S       INDIA   Adult   Grocery     D,S,G,A,M,E

I have list as below :

lis1 = [S,D,G,E,M,A]

Now i wanted my dataframe to be as below :

Explanation : Group by Card_x, Country , Age and get the lis1 values as "Card_y"

Card_x  Country Age     Card_y
S       INDIA   Adult   S,D,G,E,M,A

Can i be helped ?

Note : Logic for calulating lis1 is below :

lis1=[]
for i in range(len(t)):
    l=df.Card_y.iloc[i].split(',')
    lis1.append(l)
sorted(lis1[0], key=lambda elem: sum(sublist.index(elem) for sublist in lis1) / len(lis1))

Basically, lis1 gets the Rank of each Card_y for different "Code" and gets the Average Rank and recomputes the Rank with least Average.

Eg : S is in 1st Rank for Code - Garments, and 2rd Rank for Code - Grocery.so average is 1+2/2=1.5

D is 3rd Rank for Code - Garments, and 1st Rank for Code - Grocery. so average is 3+1/2=2.

Now based on the average, with least average i get the Ranked list. so it will be S,D,G,E,M,A

Answer 1

Try:

df_out = df.groupby(['Card_x','Country','Age'])['Card_y'].apply(lambda x: x.str.split(',', expand=True)
                                                                  .rename(columns = lambda x: x+1)
                                                                  .stack().reset_index(level=1))

df_out = df_out.groupby(['Card_x','Country','Age',0])['level_1'].mean().sort_values().reset_index(level=-1)

df_out.groupby(['Card_x','Country','Age'])[0].agg(','.join).rename('Card_y').reset_index()

Output:

  Card_x Country    Age       Card_y
0      S   INDIA  Adult  S,D,G,E,A,M