Sorting by data in another Dataframe

Question

I've been stuck with an engineering problem thats Python/Pandas related. I'd appreciate any help given. I've simplified the numbers so I can better explain myself. I have something similar to the following:

	positioning(x-axis)	Calculated difference
1	0.25	0.05
2	0.75	0.06
3	1.25	0.02
4	0.25	0.05
5	0.75	0.05
6	1.25	0.02
7	0.25	0.09
8	0.75	0.01
9	1.25	0.02
10	0.25	0.05

What I need to do is re-organise the calculated difference based on the x-axis positioning. So it looks something like this:

(0.25)	(0.75)	(1.25)
0.05	0	0
0	0.06	0
0	0	0.02
0.5	0	0
0	0.5	0
0	0	0.02
0.09	0	0
0	0.01	0
0	0	0.02
0.05	0	0

As you can see, I need to organize everything based on the x-positioning. What is the best approach to this problem? Keep in mind I have 2000+ rows and the x positioning is dynamic but I'm currently working till up to 50(so a lot of columns).

I hope I've clarified the question.

Answer 1

Use pd.get_dummies:

In [10]: pd.get_dummies(df['positioning(x-axis)']).mul(df['Calculated difference'],axis=0)
Out[10]: 
    0.25  0.75  1.25
1   0.05  0.00  0.00
2   0.00  0.06  0.00
3   0.00  0.00  0.02
4   0.05  0.00  0.00
5   0.00  0.05  0.00
6   0.00  0.00  0.02
7   0.09  0.00  0.00
8   0.00  0.01  0.00
9   0.00  0.00  0.02
10  0.05  0.00  0.00

Answer 2

Just do pivot

df.pivot(columns='positioning(x-axis)',values='Calculated difference').fillna(0)
Out[363]: 
Calculated  0.25  0.75  1.25
0           0.05  0.00  0.00
1           0.00  0.06  0.00
2           0.00  0.00  0.02
3           0.05  0.00  0.00
4           0.00  0.05  0.00
5           0.00  0.00  0.02
6           0.09  0.00  0.00
7           0.00  0.01  0.00
8           0.00  0.00  0.02
9           0.05  0.00  0.00