I've got a table of clients (coper) and asset allocation (asset)
A = [[1,2],[3,4],[5,6]]
idx = ['coper1','coper2','coper3']
cols = ['asset1','asset2']
df = pd.DataFrame(A,index = idx, columns = cols)
so my data look like
asset1 asset2
coper1 1 2
coper2 3 4
coper3 5 6
and I want to run them through a linear optimization (i've got constraints- somtehing like sum of all of asset_i <= amount_on_hand_i
and sum of coper_j = price_j
)
so I have to turn this 2D matrix into a 1D vector. Which is easy with melt
df2 = pd.melt(df,value_vars=['asset1','asset2'])
But now, when I try to unmelt it, I get a 6-row array with lots of blanks!
df2.pivot(columns = 'variable', values = 'value')
variable asset1 asset2
0 1.0 NaN
1 3.0 NaN
2 5.0 NaN
3 NaN 2.0
4 NaN 4.0
5 NaN 6.0
Is there any way to preserve the 'coper' part of my indexing while using melt?
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