问题
This question is about a function behaving in an unexpected manner when applied on two different dataframes - more precisely, groupby objects. Either I'm missing something that is obviously wrong or there's a bug in pandas.
I wrote the below function to append a row to each group in a groupby object.This question is another question that is related to the function.
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
We'll run two tests to test the function.
TEST 1
It works as expected on the dataframe a in the linked question(demonstrated in the above question). Here's a slightly augmented a re-run for more clarity (mostly copy-pasted from the linked question).
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
Applying the function,
a = a.reset_index().groupby(['first', 'second']).apply(lambda x: myfunction(x, now))
It has appended a new row to each group. A new DTM column was added because it wasn't in the original a. A group is a first - second pair.
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
Some refinement,
a = a.reset_index(level = 2).drop(('level_2', 'first', 'second')).loc[:,(0,'DTM')]
this gives final 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
So far so good. This is the expected behavior. A new row has been appended to each first-second pair and the DTM column of that row has been populated with the current timestamp.
TEST 2
Surprisingly, I couldn't reproduce that behavior in the below dataframe df. A group is an ID-SEQ combination
This df can be reproduced using:
1.Copying the below
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.And then executing these,
df = pd.read_clipboard(sep = '[ ]{2,}')
df.columns = ['ID', 'SEQ', 'DTM', 'STATUS']
Setting a multi-index
d = df.set_index(['ID', 'SEQ', 'DTM']) # I have three index levels this time in the original dataframe
What d looks like,
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
Applying the function,
dd = d.reset_index().groupby(['ID', 'SEQ']).apply(lambda x: myfunction(x, now)) # a group is a unique combination of ID-SEQ pairs
This returns, (notice the fourth row)
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
some refinement,
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
Problem
A new row containing the current timestamp has been appended to each ID-SEQ group with the exception of C1-579 group! (4th row in dd and ddd)
Questions
- What caused this problem?
- What is the additional index level introduced in
dd?
回答1:
After a lot debugging problem was noticed.
There is problem with same number in level 3 - in your last sample is shape of group 2, but this value exist before, so new row was no added onlu row was overwritten.
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
First sample was nice because second group has only one row.
But if has 2 rows:
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
same problem.
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
So if function is a bit changed all works perfectly:
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
来源：https://stackoverflow.com/questions/44887892/strange-behavior-when-trying-to-append-a-row-to-each-group-in-a-group-by-object