Python 将两个熊猫按对象分组求和

我有两只熊猫按对象分组，我想把它们的值相加。我不知道如何合并这两个数据帧，以便列

CALL_BLOCK

拥有该

DOW

的所有十个调用块，并对值求和。我尝试了几种方法，如重置索引和合并两个数据帧，但仍然无法获得column

call\u blocks

的所有十个调用块。我会感谢你的帮助。先谢谢你

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

---

从第二个数据帧中删除未使用的

多索引

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词典中获取到

multi-index

系列的代码，您可以将其视为

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'])

使用@jpp setup

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

---

你能把df1.to_dict（）和df2.to_dict（）加到这个问题上吗。这有用吗？谢谢你的回答。我不能删除

level=1（DOW）

，因为我希望特定于

DOW

列的值与我在预期输出下描述的值类似。使用

reset\u index（）会更简单吗

在这些分组对象上，将其转换为熊猫数据框并处理掉，在这种情况下，输出将是所述格式的数据框？这很有帮助。谢谢你。@KrishnangKDalal我很高兴这有帮助。不客气。快乐编码！