Keeping some data from duplicates and adding to existing python dataframe - python-2.7

Question

I have an issue with keeping some data from duplicates and wanting to add valuable information to a new column in the dataframe.
import pandas as pd
data = {'id':[1,1,2,2,3],'key':[1,1,2,2,1],'value0':['a', 'b', 'x', 'y', 'a']}
frame = pd.DataFrame(data, columns = ['id','key','value0'])
print frame
Yields:
id key value0
0 1 1 a
1 1 1 b
2 2 2 x
3 2 2 y
4 3 1 a
Desired Output:
key value0_0 value0_1 value1_0
0 1 a b a
1 2 x y None
The "id" column isn't important to keep but could help with iteration and grouping.
I think this could be adapted to other projects where you don't know how many values exist for a set of keys.

Answer 1

set_index including a cumcount and unstack
frame.set_index(
['key', frame.groupby('key').cumcount()]
).value0.unstack().add_prefix('value0_').reset_index()
key value0_0 value0_1 value0_2
0 1 a b a
1 2 x y None
I'm questioning your column labeling but here is an approach using binary
frame.set_index(
['key', frame.groupby('key').cumcount()]
).value0.unstack().rename(
columns='{:02b}'.format
).add_prefix('value_').reset_index()
key value_00 value_01 value_10
0 1 a b a
1 2 x y None