这个问题是关于一个函数在应用于两个不同的数据帧时以意想不到的方式运行 - 更准确地说，是groupby对象。我要么错过了一些明显错误的东西，要么就是大熊猫的错误。
我编写了下面的函数来为groupby对象中的每个组追加一行。This question是另一个与该函数相关的问题。
def myfunction(g, now):
'''This function appends a row to each group and populates the DTM column value of that row with the current timestamp. Other columns of the new row will have NaN s.
g: a groupby object
now: current timestamp
returns a dataframe that has the current timestamp appended in the DTM column for each group
'''
g.loc[g.shape[0], 'DTM'] = now # Appending the current timestamp to a DTM column in each group
return g
我们将运行两项测试来测试该功能。
TEST 1
它在链接问题中的数据框a上按预期工作（在上面的问题中演示）。这是一个稍微增强的重新运行以获得更清晰（主要是从链接问题中复制粘贴）。
arrays = [['bar', 'bar','bar', 'baz', 'baz', 'foo', 'foo', 'foo', 'qux', 'qux'],
['one', 'one','two', 'one', 'two', 'one', 'two', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
a = pd.DataFrame(np.random.random((10,)), index = index)
a
Out[50]:
0
first second
bar one 0.134379
one 0.967928
two 0.067502
baz one 0.182887
two 0.926932
foo one 0.806225
two 0.718322
two 0.932114
qux one 0.772494
two 0.141510
应用该功能，
a = a.reset_index().groupby(['first', 'second']).apply(lambda x: myfunction(x, now))
它为每个组添加了一个新行。添加了新的DTM列，因为它不在原始a中。一个群组是first - second对。
a
Out[52]:
first second 0 DTM
first second
bar one 0 bar one 0.134379 NaT
1 bar one 0.967928 NaT
2 NaN NaN NaN 2017-07-03 18:56:33.183
two 2 bar two 0.067502 NaT
1 NaN NaN NaN 2017-07-03 18:56:33.183
baz one 3 baz one 0.182887 NaT
1 NaN NaN NaN 2017-07-03 18:56:33.183
two 4 baz two 0.926932 NaT
1 NaN NaN NaN 2017-07-03 18:56:33.183
foo one 5 foo one 0.806225 NaT
1 NaN NaN NaN 2017-07-03 18:56:33.183
two 6 foo two 0.718322 NaT
7 foo two 0.932114 NaT
2 NaN NaN NaN 2017-07-03 18:56:33.183
qux one 8 qux one 0.772494 NaT
1 NaN NaN NaN 2017-07-03 18:56:33.183
two 9 qux two 0.141510 NaT
1 NaN NaN NaN 2017-07-03 18:56:33.183
一些改进，
a = a.reset_index(level = 2).drop(('level_2', 'first', 'second')).loc[:,(0,'DTM')]
这给最终a as，
a
Out[62]:
0 DTM
first second
bar one 0.371683 NaT
one 0.327870 NaT
one NaN 2017-07-03 18:56:33.183
two 0.048794 NaT
two NaN 2017-07-03 18:56:33.183
baz one 0.462747 NaT
one NaN 2017-07-03 18:56:33.183
two 0.758674 NaT
two NaN 2017-07-03 18:56:33.183
foo one 0.238607 NaT
one NaN 2017-07-03 18:56:33.183
two 0.156104 NaT
two 0.594270 NaT
two NaN 2017-07-03 18:56:33.183
qux one 0.091088 NaT
one NaN 2017-07-03 18:56:33.183
two 0.795864 NaT
two NaN 2017-07-03 18:56:33.183
到目前为止一切顺利。这是预期的行为。新行已附加到每个first - second对，并且该行的DTM列已填充当前时间戳。
TEST 2
令人惊讶的是，我无法在以下数据框df中重现该行为。一个群组是ID - SEQ组合
此df可以使用以下内容复制：
1.复制以下
C1 572 5/9/2017 10:13 PE
C1 572 5/9/2017 12:24 OK
C1 579 5/9/2017 10:19 PE
C1 579 5/9/2017 13:25 OK
C1 587 5/9/2017 10:20 PE
C1 587 5/9/2017 12:25 OK
C1 590 5/9/2017 10:21 PE
C1 590 5/9/2017 13:09 OK
C1 604 5/9/2017 10:38 PE
C1 604 5/9/2017 12:32 OK
C1 609 5/9/2017 10:39 PE
C1 609 5/9/2017 13:29 OK
C1 613 5/9/2017 10:39 PE
C1 613 5/9/2017 13:08 OK
C1 618 5/9/2017 10:40 PE
C1 618 5/9/2017 13:33 OK
C1 636 5/9/2017 10:54 PE
C1 636 5/9/2017 13:36 OK
C1 642 5/9/2017 10:55 PE
C1 642 5/9/2017 13:35 OK
C1 643 5/9/2017 10:56 PE
C1 643 5/9/2017 13:34 OK
C1 656 5/9/2017 10:55 PE
C1 656 5/9/2017 13:36 OK
C2 86 9/5/2016 19:45 PE
C2 86 9/6/2016 11:55 OK
C3 10 4/17/2017 12:23 PE
C3 10 4/17/2017 14:51 OK
C4 38 3/25/2017 10:35 PE
C4 38 3/25/2017 10:51 OK
2.然后执行这些，
df = pd.read_clipboard(sep = '[ ]{2,}')
df.columns = ['ID', 'SEQ', 'DTM', 'STATUS']
设置多索引
d = df.set_index(['ID', 'SEQ', 'DTM']) # I have three index levels this time in the original dataframe
d看起来像什么，
d
Out[40]:
STATUS
ID SEQ DTM
C1 572 5/9/2017 10:13 PE
5/9/2017 12:24 OK
579 5/9/2017 10:19 PE
5/9/2017 13:25 OK
587 5/9/2017 10:20 PE
5/9/2017 12:25 OK
590 5/9/2017 10:21 PE
5/9/2017 13:09 OK
604 5/9/2017 10:38 PE
5/9/2017 12:32 OK
609 5/9/2017 10:39 PE
5/9/2017 13:29 OK
613 5/9/2017 10:39 PE
5/9/2017 13:08 OK
618 5/9/2017 10:40 PE
5/9/2017 13:33 OK
636 5/9/2017 10:54 PE
5/9/2017 13:36 OK
642 5/9/2017 10:55 PE
5/9/2017 13:35 OK
643 5/9/2017 10:56 PE
5/9/2017 13:34 OK
656 5/9/2017 10:55 PE
5/9/2017 13:36 OK
C2 86 9/5/2016 19:45 PE
9/6/2016 11:55 OK
C3 10 4/17/2017 12:23 PE
4/17/2017 14:51 OK
C4 38 3/25/2017 10:35 PE
3/25/2017 10:51 OK
应用该功能，
dd = d.reset_index().groupby(['ID', 'SEQ']).apply(lambda x: myfunction(x, now)) # a group is a unique combination of ID-SEQ pairs
返回，（注意第四行）
dd
Out[37]:
ID SEQ DTM STATUS
ID SEQ
C1 572 0 C1 572.0 5/9/2017 10:13 PE
1 C1 572.0 5/9/2017 12:24 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
579 2 C1 579.0 2017-07-03 18:56:33.183000 PE
3 C1 579.0 5/9/2017 13:25 OK
587 4 C1 587.0 5/9/2017 10:20 PE
5 C1 587.0 5/9/2017 12:25 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
590 6 C1 590.0 5/9/2017 10:21 PE
7 C1 590.0 5/9/2017 13:09 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
604 8 C1 604.0 5/9/2017 10:38 PE
9 C1 604.0 5/9/2017 12:32 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
609 10 C1 609.0 5/9/2017 10:39 PE
11 C1 609.0 5/9/2017 13:29 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
613 12 C1 613.0 5/9/2017 10:39 PE
13 C1 613.0 5/9/2017 13:08 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
618 14 C1 618.0 5/9/2017 10:40 PE
15 C1 618.0 5/9/2017 13:33 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
636 16 C1 636.0 5/9/2017 10:54 PE
17 C1 636.0 5/9/2017 13:36 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
642 18 C1 642.0 5/9/2017 10:55 PE
19 C1 642.0 5/9/2017 13:35 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
643 20 C1 643.0 5/9/2017 10:56 PE
21 C1 643.0 5/9/2017 13:34 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
656 22 C1 656.0 5/9/2017 10:55 PE
23 C1 656.0 5/9/2017 13:36 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
C2 86 24 C2 86.0 9/5/2016 19:45 PE
25 C2 86.0 9/6/2016 11:55 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
C3 10 26 C3 10.0 4/17/2017 12:23 PE
27 C3 10.0 4/17/2017 14:51 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
C4 38 28 C4 38.0 3/25/2017 10:35 PE
29 C4 38.0 3/25/2017 10:51 OK
2 NaN NaN 2017-07-03 18:56:33.183000 NaN
一些改进，
ddd = dd.reset_index(level = 2).drop(('level_2', 'ID', 'SEQ')).loc[:,('DTM','STATUS')]
ddd
Out[39]:
DTM STATUS
ID SEQ
C1 572 5/9/2017 10:13 PE
572 5/9/2017 12:24 OK
572 2017-07-03 18:56:33.183000 NaN
579 2017-07-03 18:56:33.183000 PE
579 5/9/2017 13:25 OK
587 5/9/2017 10:20 PE
587 5/9/2017 12:25 OK
587 2017-07-03 18:56:33.183000 NaN
590 5/9/2017 10:21 PE
590 5/9/2017 13:09 OK
590 2017-07-03 18:56:33.183000 NaN
604 5/9/2017 10:38 PE
604 5/9/2017 12:32 OK
604 2017-07-03 18:56:33.183000 NaN
609 5/9/2017 10:39 PE
609 5/9/2017 13:29 OK
609 2017-07-03 18:56:33.183000 NaN
613 5/9/2017 10:39 PE
613 5/9/2017 13:08 OK
613 2017-07-03 18:56:33.183000 NaN
618 5/9/2017 10:40 PE
618 5/9/2017 13:33 OK
618 2017-07-03 18:56:33.183000 NaN
636 5/9/2017 10:54 PE
636 5/9/2017 13:36 OK
636 2017-07-03 18:56:33.183000 NaN
642 5/9/2017 10:55 PE
642 5/9/2017 13:35 OK
642 2017-07-03 18:56:33.183000 NaN
643 5/9/2017 10:56 PE
643 5/9/2017 13:34 OK
643 2017-07-03 18:56:33.183000 NaN
656 5/9/2017 10:55 PE
656 5/9/2017 13:36 OK
656 2017-07-03 18:56:33.183000 NaN
C2 86 9/5/2016 19:45 PE
86 9/6/2016 11:55 OK
86 2017-07-03 18:56:33.183000 NaN
C3 10 4/17/2017 12:23 PE
10 4/17/2017 14:51 OK
10 2017-07-03 18:56:33.183000 NaN
C4 38 3/25/2017 10:35 PE
38 3/25/2017 10:51 OK
38 2017-07-03 18:56:33.183000 NaN
问题
除了ID - SEQ群组之外，每个C1 - 579群组都附加了一个包含当前时间戳的新行！ （dd和ddd中的第4行）
问题
dd中引入的其他索引级别是什么？答案 0 :(得分：2)
注意到很多调试问题后。
级别3中存在相同编号的问题 - 在您的上一个示例中是组2的形状，但此值之前存在，因此新行未添加onlu行被覆盖。
ID SEQ DTM STATUS
ID SEQ
C1 572 0 C1 572.0 2017-05-09 10:13:00.000000 PE
1 C1 572.0 2017-05-09 12:24:00.000000 OK
2 NaN NaN 2017-07-06 08:46:02.341472 NaN
579 2 C1 579.0 2017-07-06 08:46:02.341472 PE <- ovetwritten values in row
3 C1 579.0 2017-05-09 13:25:00.000000 OK
587 4 C1 587.0 2017-05-09 10:20:00.000000 PE
5 C1 587.0 2017-05-09 12:25:00.000000 OK
2 NaN NaN 2017-07-06 08:46:02.341472 NaN
第一个样本很不错，因为第二组只有一行。
但如果有2行：
arrays = [['bar', 'bar','bar', 'baz', 'baz', 'foo', 'foo', 'foo', 'qux', 'qux'],
['one', 'two','two', 'one', 'two', 'one', 'two', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
a = pd.DataFrame(np.random.random((10,)), index = index)
print (a)
0
first second
bar one 0.366258
two 0.583205
two 0.159388
baz one 0.598198
two 0.274027
foo one 0.086461
two 0.353577
two 0.823377
qux one 0.098737
two 0.128470
同样的问题。
print (a)
first second 0 DTM
first second
bar one 0 bar one 0.366258 NaT
1 NaN NaN NaN 2017-07-06 08:47:55.610671
two 1 bar two 0.583205 NaT
2 bar two 0.159388 2017-07-06 08:47:55.610671 <- ovetwritten
baz one 3 baz one 0.598198 NaT
1 NaN NaN NaN 2017-07-06 08:47:55.610671
two 4 baz two 0.274027 NaT
因此，如果功能有点改变，那么一切都很完美：
now = pd.datetime.now()
def myfunction(g, now):
g.loc[str(g.shape[0]) + 'a', 'DTM'] = now
return g
arrays = [['bar', 'bar','bar', 'baz', 'baz', 'foo', 'foo', 'foo', 'qux', 'qux'],
['one', 'two','two', 'one', 'two', 'one', 'two', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
a = pd.DataFrame(np.random.random((10,)), index = index)
print (a)
a = a.reset_index().groupby(['first', 'second']).apply(lambda x: myfunction(x, now))
print (a)
first second 0 DTM
first second
bar one 0 bar one 0.677641 NaT
1a NaN NaN NaN 2017-07-06 08:54:47.481671
two 1 bar two 0.274588 NaT
2 bar two 0.524903 NaT
2a NaN NaN NaN 2017-07-06 08:54:47.481671
baz one 3 baz one 0.198272 NaT
1a NaN NaN NaN 2017-07-06 08:54:47.481671
two 4 baz two 0.787949 NaT
1a NaN NaN NaN 2017-07-06 08:54:47.481671
foo one 5 foo one 0.484197 NaT
1a NaN NaN NaN 2017-07-06 08:54:47.481671