Question

我的数据框如下：

ref, type, amount
001, foo, 10
001, foo, 5
001, bar, 50
001, bar, 5
001, test, 100
001, test, 90
002, foo, 20
002, foo, 35
002, bar, 75
002, bar, 80
002, test, 150
002, test, 110

这就是我想要的：

ref, type, amount, foo, bar, test
001, foo, 10, 15, 55, 190
001, foo, 5, 15, 55, 190
001, bar, 50, 15, 55, 190
001, bar, 5, 15, 55, 190
001, test, 100, 15, 55, 190
001, test, 90, 15, 55, 190
002, foo, 20, 55, 155, 260
002, foo, 35, 55, 155, 260
002, bar, 75, 55, 155, 260
002, bar, 80, 55, 155, 260
002, test, 150, 55, 155, 260
002, test, 110, 55, 155, 260

所以我有这个：

df.groupby('ref')['amount'].transform(sum)

但我如何过滤它，以便上述内容仅适用于type=foo或bar或test的行？

Answer 1

使用pivot table的解决方案：

>>> b = pd.pivot_table(df, values='amount', index=['ref'], columns=['type'], aggfunc=np.sum)
>>> b
type  bar  foo  test
ref
1      55   15   190
2     155   55   260

>>> pd.merge(df, b, left_on='ref', right_index=True)
    ref  type  amount  bar  foo  test
0     1   foo      10   55   15   190
1     1   foo       5   55   15   190
2     1   bar      50   55   15   190
3     1   bar       5   55   15   190
4     1  test     100   55   15   190
5     1  test      90   55   15   190
6     2   foo      20  155   55   260
7     2   foo      35  155   55   260
8     2   bar      75  155   55   260
9     2   bar      80  155   55   260
10    2  test     150  155   55   260
11    2  test     110  155   55   260

Answer 2

我认为您需要groupby与unstack，然后merge与原始DataFrame：

df1 = df.groupby(['ref','type'])['amount'].sum().unstack().reset_index()
print (df1)
type  ref  bar  foo  test
0     001   55   15   190
1     002  155   55   260

df = pd.merge(df, df1, on='ref')
print (df)
    ref  type  amount  sums  bar  foo  test
0   001   foo      10    15   55   15   190
1   001   foo       5    15   55   15   190
2   001   bar      50    55   55   15   190
3   001   bar       5    55   55   15   190
4   001  test     100   190   55   15   190
5   001  test      90   190   55   15   190
6   002   foo      20    55  155   55   260
7   002   foo      35    55  155   55   260
8   002   bar      75   155  155   55   260
9   002   bar      80   155  155   55   260
10  002  test     150   260  155   55   260
11  002  test     110   260  155   55   260

<强>计时：

In [506]: %timeit (pd.merge(df, df.groupby(['ref','type'])['amount'].sum().unstack().reset_index(), on='ref'))
100 loops, best of 3: 3.4 ms per loop

In [507]: %timeit (pd.merge(df, pd.pivot_table(df, values='amount', index=['ref'], columns=['type'], aggfunc=np.sum), left_on='ref', right_index=True))
100 loops, best of 3: 4.99 ms per loop

熊猫组合总和

2 个答案: