将两个 Pandas 按对象分组

Question

我有两个按对象分组的熊猫，我想对它们的值求和。我无法弄清楚如何合并这两个数据框，以便CALL_BLOCK 列具有该DOW 的所有十个调用块，并对这些值求和。我尝试了几种方法，例如重置索引和合并两个数据帧，但我仍然无法获得列CALL_BLOCKS 的所有十个调用块。我会感谢你的帮助。提前非常感谢。

已编辑

df1 = {('1-100019B', 'a_8:00AM to 9:00AM'): 0.6493506493506493,
 ('1-100019B', 'b_9:00AM to 10:00AM'): 0.7272727272727273,
 ('1-100019B', 'c_10:00AM to 11:00AM'): 0.16883116883116883,
 ('1-100019B', 'd_11:00AM to 12:00PM'): 0.025974025974025976,
 ('1-100019B', 'e_12:00PM to 1:00PM'): 0.38961038961038963,
 ('1-100019B', 'f_1:00PM to 2:00PM'): 0.14285714285714285,
 ('1-100019B', 'g_2:00PM to 3:00PM'): 0.0,
 ('1-100019B', 'h_3:00PM to 4:00PM'): 0.12987012987012986,
 ('1-100019B', 'i_4:00PM to 5:00PM'): 0.0,
 ('1-100019B', 'j_After 5PM'): 0.0}

df2 = 
{('1-100019B', 0, 'a_8:00AM to 9:00AM'): 0.5,
 ('1-100019B', 0, 'b_9:00AM to 10:00AM'): 0.6666666666666666,
 ('1-100019B', 0, 'c_10:00AM to 11:00AM'): 0.25,
 ('1-100019B', 0, 'e_12:00PM to 1:00PM'): 0.3333333333333333,
 ('1-100019B', 0, 'f_1:00PM to 2:00PM'): 0.0,
 ('1-100019B', 0, 'h_3:00PM to 4:00PM'): 1.0}

预期输出：

df = 
CONTACT_ID  DOW  CALL_BLOCKS         
1-100019B   0    a_8:00AM to 9:00AM      1.149
                 b_9:00AM to 10:00AM     1.380
                 c_10:00AM to 11:00AM    0.410
                 d_11:00AM to 12:00PM    0.026
                 e_12:00PM to 1:00PM     0.710
                 f_1:00PM to 2:00PM      0.140
                 g_2:00PM to 3:00PM      0.000
                 h_3:00PM to 4:00PM      1.120
                 i_4:00PM to 5:00PM      0.000
                 j_After 5PM             0.000

Answer 1

使用@jpp 设置，

df1.merge(df2.reset_index('DOW'), on=['CONTACTS_ID','CALL_BLOCKS'], how='outer')\
   .set_index('DOW', append=True).sum(1)

输出：

CONTACTS_ID  CALL_BLOCKS           DOW
1-100019B    a_8:00AM to 9:00AM    0.0    1.149351
             b_9:00AM to 10:00AM   0.0    1.393939
             c_10:00AM to 11:00AM  0.0    0.418831
             d_11:00AM to 12:00PM  NaN    0.025974
             e_12:00PM to 1:00PM   0.0    0.722944
             f_1:00PM to 2:00PM    0.0    0.142857
             g_2:00PM to 3:00PM    NaN    0.000000
             h_3:00PM to 4:00PM    0.0    1.129870
             i_4:00PM to 5:00PM    NaN    0.000000
             j_After 5PM           NaN    0.000000
dtype: float64

Answer 2

从第二个数据帧中删除未使用的MultiIndex 级别，然后使用pd.Series.add：

df2.index = df2.index.droplevel(1)

res = df1.add(df2, fill_value=0)

print(res)

                                0
idx1      idx3                          
1-100019B a_8:00AM to 9:00AM    1.149351
          b_9:00AM to 10:00AM   1.393939
          c_10:00AM to 11:00AM  0.418831
          d_11:00AM to 12:00PM  0.025974
          e_12:00PM to 1:00PM   0.722944
          f_1:00PM to 2:00PM    0.142857
          g_2:00PM to 3:00PM    0.000000
          h_3:00PM to 4:00PM    1.129870
          i_4:00PM to 5:00PM    0.000000
          j_After 5PM           0.000000

设置

这是我用来从您的输入字典中获取到MultiIndex 系列的代码，这是您将看到的groupby 操作的输出。

df1 = pd.DataFrame.from_dict(df1, orient='index').reset_index()
df1 = df1.join(pd.DataFrame(df1['index'].values.tolist(), columns=['idx1', 'idx3'])).drop('index', 1)
df1 = df1.set_index(['idx1', 'idx3'])

df2 = pd.DataFrame.from_dict(df2, orient='index').reset_index()
df2 = df2.join(pd.DataFrame(df2['index'].values.tolist(), columns=['idx1', 'idx2', 'idx3'])).drop('index', 1)
df2 = df2.set_index(['idx1', 'idx2', 'idx3'])