使用pandas.Series.strip时：

df["com_item"] = df["item"] + "," + df["nxt_item"].astype(str).str.strip("{}")

输出：

print(df)

        item  Space  rem_spc        nxt_item                 com_item
0  Pineapple    0.5      0.5  {Mango, Grape}  Pineapple, Mango, Grape

如果希望nxt_item作为列表：

df['com_item'] = df.apply(lambda row: list(row['nxt_item']) + [row['item']] ,axis=1)

输出：

item  Space  rem_spc        nxt_item                   com_item
0  pineapple    0.5      0.5  {Grape, Mango}  [Grape, Mango, pineapple]

如果要作为字符串：

df['com_item'] = df.apply(lambda row: ' '.join(list(row['nxt_item']) + [row['item']] ),axis=1)

输出：

item  Space  rem_spc        nxt_item               com_item
0  pineapple    0.5      0.5  {Grape, Mango}  Grape Mango pineapple

对集合列使用Series.str.join并添加item值：

df['com_item'] = df['item'] + ',' + df['nxt_item'].str.join(',')
print (df)
        item  Space  rem_spc        nxt_item               com_item
0  Pineapple    0.5      0.5  {Grape, Mango}  Pineapple,Grape,Mango

或者使用Series.str.cat：

df['com_item'] = df['item'].str.cat( df['nxt_item'].str.join(','), sep=',')

如果需要，可以在com_item中为已消除重复数据的值创建第一个集合，将值item添加到集合中，并在列表解析中加入：

df['com_item'] = [','.join(b.union({a})) for a, b in zip(df['item'],df['nxt_item'])]

样品数据见溶液差异：

print (df)
        item  Space  rem_spc           nxt_item
0  Pineapple    0.5      0.5  {'Mango','Grape'}
1      Mango    0.5      0.5  {'Mango','Grape'}

df['nxt_item'] = df['nxt_item'].apply(ast.literal_eval)

df['com_item1'] = [','.join(b.union({a})) for a, b in zip(df['item'],df['nxt_item'])]
df['com_item2'] = df['item'] + ',' + df['nxt_item'].str.join(',')

print (df)
        item  Space  rem_spc        nxt_item              com_item1  \
0  Pineapple    0.5      0.5  {Grape, Mango}  Grape,Mango,Pineapple   
1      Mango    0.5      0.5  {Grape, Mango}            Grape,Mango   

               com_item2  
0  Pineapple,Grape,Mango  
1      Mango,Grape,Mango