I have a dataframe.

import pandas as pd
df = pd.DataFrame(           
{'number': [0,0,0,1,1,2,2,2,2], 'id1': [100,100,100,300,400,700,700,800,700], 'id2': [100,100,200,500,600,700,800,900,1000]})id1   id2  number
0  100   100       0
1  100   100       0
2  100   200       0
3  300   500       1
4  400   600       1
5  700   700       2
6  700   800       2
7  800   900       2
8  700  1000       2

(This represents a much larger dataframe I am working with ~millions of rows).

I can apply a groupby().unique to one column:

df.groupby(['number'])['id1'].unique()number
0         [100]
1    [300, 400]
2    [700, 800]
Name: id1, dtype: objectdf.groupby(['number'])['id2'].unique()number
0               [100, 200]
1               [500, 600]
2    [700, 800, 900, 1000]
Name: id2, dtype: object

I want to do the unique over both columns simultaneously to get it ordered in a dataframe:

number
0               [100, 200]
1     [300, 400, 500, 600]
2    [700, 800, 900, 1000]

When I try and do this for both columns I get the error:

pd.Data.Frame(df.groupby(['number'])['id1', 'id2'].unique())Traceback (most recent call last):File "C:\Python34\lib\site-packages\IPython\core\interactiveshell.py", line 2885, in run_codeexec(code_obj, self.user_global_ns, self.user_ns)File "<ipython-input-15-bfc6026e241e>", line 9, in <module>df.groupby(['number'])['id1', 'id2'].unique()File "C:\Python34\lib\site-packages\pandas\core\groupby.py", line 498, in __getattr__(type(self).__name__, attr))
AttributeError: 'DataFrameGroupBy' object has no attribute 'unique'

What do? Is it preferable to use a multi-index?

Edit: In addition is it possible to get the output as follows:

number
0 100
0 200
1 300
1 400
1 500
1 600
2 700
2 800
2 900
2 1000

You can select all column by []:

s = (df.groupby(['number'])['id1', 'id2'].apply(lambda x: pd.unique(x.values.ravel()).tolist()))print (s)
number
0               [100, 200]
1     [300, 500, 400, 600]
2    [700, 800, 900, 1000]
dtype: object

Or:

s2 = (df.groupby(['number'])['id1', 'id2'].apply(lambda x: np.unique(x.values.ravel()).tolist()))
print (s2)
number
0               [100, 200]
1     [300, 400, 500, 600]
2    [700, 800, 900, 1000]
dtype: object

EDIT:

If need output as column, first reshape by stack and then drop_duplicates:

df1 = (df.set_index('number')[['id1', 'id2']].stack().reset_index(level=1, drop=True).reset_index(name='a').drop_duplicates())
print (df1)number     a
0        0   100
5        0   200
6        1   300
7        1   500
8        1   400
9        1   600
10       2   700
13       2   800
15       2   900
17       2  1000