Question

我想聚合一个数据框 - 获取每个组的第一行，同时连接'upc'列中的值：

df = pd.DataFrame({
    'id1': [1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 5, 6, 6, 6, 7, 7],
    'id2': [11, 22, 11, 11, 22, 33, 33, 33, 33, 44, 44, 55, 66, 66, 22, 77, 77],
    'value1': ["1first", "1second", "1third",
               "2first", "2second",
               "3first", "3second", "3third", "3fourth",
               "4first", "4second",
               "5first",
               "6first", "6second", "6third",
               "7first", "7second"],
    'upc': [str(x) for x in range(100, 117)]
})
firsts_df = df.groupby(['id1', 'id2']).first()
concat_upcs_df = df[['id1', 'id2', 'upc']].groupby(['id1', 'id2']).apply(lambda x: '|'.join(x.upc))
firsts_df.merge(concat_upcs_df, how='inner',left_on=['id1', 'id2'], right_on=['id1', 'id2'])

这会导致此错误：

ValueError：无法将DataFrame与类型为“pandas.core.series.Series”的实例合并

如何将聚合结果与数据帧合并？我可以用更低成本的操作获得相同的结果吗？

Answer 1

我认为您需要as_index=False到first并为reset_index()添加concat_upcs_df DataFrame：

firsts_df = df.groupby(['id1', 'id2'], as_index=False).first()
concat_upcs_df = df[['id1', 'id2', 'upc']].groupby(['id1', 'id2']).apply(lambda x: '|'.join(x.upc)).reset_index(name='val')
firsts_df.merge(concat_upcs_df, how='inner',left_on=['id1', 'id2'], right_on=['id1', 'id2'])
print (df)
   id1  id2  upc   value1              val
0    1   11  100   1first          100|102
1    1   22  101  1second              101
2    2   11  103   2first              103
3    2   22  104  2second              104
4    3   33  105   3first  105|106|107|108
5    4   44  109   4first          109|110
6    5   55  111   5first              111
7    6   22  114   6third              114
8    6   66  112   6first          112|113
9    7   77  115   7first          115|116

您还可以使用drop_duplicates代替first和apply而lambda，merge使用on，因为左右联合列是相同的：

firsts_df = df.drop_duplicates(['id1', 'id2'])
concat_upcs_df = df.groupby(['id1', 'id2'])['upc'].apply('|'.join).reset_index(name='val')
df = firsts_df.merge(concat_upcs_df, on=['id1', 'id2'])

将数据框与聚合

1 个答案: