python - 多索引 Pandas 数据框上的 cumsum

Question

我有下面给出的多索引数据集:

arr = np.array([12, 12, 12, 72, 72, 72, 26, 26, 26, 22, 22, 22, 46, 46, 46, 32, 32, 32])
df = pd.DataFrame({'date': ['1/1/2000', '1/1/2000', '1/1/2000',
                            '2/1/2000', '2/1/2000', '2/1/2000',
                            '3/1/2000', '3/1/2000', '3/1/2000',
                            '1/1/2000', '1/1/2000', '1/1/2000',
                            '2/1/2000', '2/1/2000', '2/1/2000',
                            '3/1/2000', '3/1/2000', '3/1/2000'],
                   'type': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
                            'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B'],
                   'lags': ['31/12/1999', '30/12/1999', '29/12/1999',
                            '1/1/2000', '31/12/1999', '30/12/1999',
                            '2/1/2000', '1/1/2000', '31/12/1999',
                            '31/12/1999', '30/12/1999', '29/12/1999',
                            '1/1/2000', '31/12/1999', '30/12/1999',
                            '2/1/2000', '1/1/2000', '31/12/1999']})
df["target"] = arr
df.set_index(['date', 'type', 'lags'], inplace=True)

我正在尝试对每种类型使用 cumsum 并运行代码:

for g_name, g_df in df.groupby("type"):
    df.loc[g_df.index, 'target'] = df.loc[g_df.index, 'target'].cumsum().astype('float64')

但是，它对每一行应用 cumsum 并输出结果:

                          target
date     type lags
1/1/2000 A    31/12/1999    12.0
              30/12/1999    24.0
              29/12/1999    36.0
2/1/2000 A    1/1/2000     108.0
              31/12/1999   180.0
              30/12/1999   252.0
3/1/2000 A    2/1/2000     278.0
              1/1/2000     304.0
              31/12/1999   330.0
1/1/2000 B    31/12/1999    22.0
              30/12/1999    44.0
              29/12/1999    66.0
2/1/2000 B    1/1/2000     112.0
              31/12/1999   158.0
              30/12/1999   204.0
3/1/2000 B    2/1/2000     236.0
              1/1/2000     268.0
              31/12/1999   300.0

预期的结果是:

                          target
date     type lags
1/1/2000 A    31/12/1999    12.0
              30/12/1999    12.0
              29/12/1999    12.0
2/1/2000 A    1/1/2000      84.0
              31/12/1999    84.0
              30/12/1999    84.0
3/1/2000 A    2/1/2000      110.0
              1/1/2000      110.0
              31/12/1999    110.0
1/1/2000 B    31/12/1999    22.0
              30/12/1999    22.0
              29/12/1999    22.0
2/1/2000 B    1/1/2000      68.0
              31/12/1999    68.0
              30/12/1999    68.0
3/1/2000 B    2/1/2000      100.0
              1/1/2000      100.0
              31/12/1999    100.0

如何获得预期结果并以 pythonic 方式更新原始数据？

Answer 1

您可以按date 和type 分组并提取第一个值，然后按type 分组以计算累计和。最后重新索引输出以广播所有值:

>>> df.groupby(['date', 'type']).first().groupby('type').cumsum().reindex(df.index)
                          target
date     type lags              
1/1/2000 A    31/12/1999      12
              30/12/1999      12
              29/12/1999      12
2/1/2000 A    1/1/2000        84
              31/12/1999      84
              30/12/1999      84
3/1/2000 A    2/1/2000       110
              1/1/2000       110
              31/12/1999     110
1/1/2000 B    31/12/1999      22
              30/12/1999      22
              29/12/1999      22
2/1/2000 B    1/1/2000        68
              31/12/1999      68
              30/12/1999      68
3/1/2000 B    2/1/2000       100
              1/1/2000       100
              31/12/1999     100