扩展Pandas数据帧，类似于Pivot或Stack / Unstack

Question

My problem is probably best explained with an example:

What I have:

ID0,ID1,Time,Data0,Data1
 1   1   10  'A'    93
 1   2   10  'A'    55
 1   1   12  'A'    88
 1   2   12  'B'    66
 2   3   102 'C'    14
 2   4   102 'A'    22
 2   4   112 'D'    15
 2   3   112 'B'    43

What I would like:

ID0,ID1,Time,Data0,Data1,Data0.2,Data1.2
 1   1   10   'A'    93    'A'    55
 1   2   10   'A'    55    'A'    93
 1   1   12   'A'    88    'B'    66
 1   2   12   'B'    66    'A'    88
 2   3   102  'C'    14    'A'    22
 2   4   102  'A'    22    'C'    14
 2   4   112  'D'    15    'B'    43
 2   3   112  'B'    43    'D'    15

Essentially, there are 2 unique ID1s associated with every ID0.

Data is sampled periodically.

I would like to make the original data frame 'wider' by adding more columns so that each row contains information from the other ID1 from the same time period.

Answer 1

try:

grb = df.groupby(['ID0', 'Time'])
df['Data0.2'] = grb['Data0'].transform(lambda ts: ts[::-1])
df['Data1.2'] = grb['Data1'].transform(lambda ts: ts[::-1])

what is this basically doing is, based on your statement that

there are 2 unique ID1s associated with every ID0.

it groups the data-frame by ['ID0', 'Time'] and reverses the specific columns; if there are exactly 2 unique ID1s in each group, the data-frame will be expanded by the values from the other ID1 ;

>>> df
   ID0  ID1  Time Data0  Data1 Data0.2  Data1.2
0    1    1    10   'A'     93     'A'       55
1    1    2    10   'A'     55     'A'       93
2    1    1    12   'A'     88     'B'       66
3    1    2    12   'B'     66     'A'       88
4    2    3   102   'C'     14     'A'       22
5    2    4   102   'A'     22     'C'       14
6    2    4   112   'D'     15     'B'       43
7    2    3   112   'B'     43     'D'       15

[8 rows x 7 columns]

final edit : to do both columns together, you may try below; note that .values is necessary in here:

>>> grb = df.groupby(['ID0', 'Time'])
>>> df2 = grb['Data0', 'Data1'].transform(lambda obj: obj.values[::-1])
>>> df.join(df2, rsuffix='.2')