Pandas groupby mean absolute deviation - python-2.7

Question

I have a pandas dataframe like this:
Product Group Product ID Units Sold Revenue Rev/Unit
A 451 8 $16 $2
A 987 15 $40 $2.67
A 311 2 $5 $2.50
B 642 6 $18 $3.00
B 251 4 $28 $7.00
I want to transform it to look like this:
Product Group Units Sold Revenue Rev/Unit Mean Abs Deviation
A 25 $61 $2.44 $0.24
B 10 $46 $4.60 $2.00
The Mean Abs Deviation column is to be performed on the Rev/Unit column in the first table. The tricky thing is taking into account the respective weights behind the Rev/Unit calculation.
For example taking a straight MAD of Product Group A's Rev/Unit would yield $0.26. However after taking weight into consideration, the MAD would be $0.24.
I know to use groupby to get the simple summation for units sold and revenue, but I'm a bit lost on how to do the more complicated calculations of the next 2 columns.
Also while we're giving advice/help---is there any easier way to create/paste tables into SO posts??
UPDATE:
Would a solution like this work? I know it will for the summation fields, but not sure how to implement for the latter 2 fields.
grouped_df=df.groupby("Product Group")
grouped_df.agg({
'Units Sold':'sum',
'Revenue':'sum',
'Rev/Unit':'Revenue'/'Units Sold',
'MAD':some_function})

Answer 1

you need to clarify what the "weights" are, I assumed the weights are the number of units sold, but that gives a different results from yours:
pv = df.pivot_table( rows='Product Group',
values=[ 'Units Sold', 'Revenue' ],
aggfunc=sum )
pv[ 'Rev/Unit' ] = pv.Revenue / pv[ 'Units Sold' ]
this gives:
Revenue Units Sold Rev/Unit
Product Group
A 61 25 2.44
B 46 10 4.60
As for WMAD:
def wmad( prod ):
idx = df[ 'Product Group' ] == prod
w = df[ 'Units Sold' ][ idx ]
abs_dev = np.abs ( df[ 'Rev/Unit' ][ idx ] - pv[ 'Rev/Unit' ][ prod ] )
return sum( abs_dev * w ) / sum( w )
pv[ 'Mean Abs Deviation' ] = [ wmad( idx ) for idx in pv.index ]
which as I mentioned gives different result
Revenue Units Sold Rev/Unit Mean Abs Deviation
Product Group
A 61 25 2.44 0.2836
B 46 10 4.60 1.9200

Answer 2

From your suggested solution, you can use a lambda function to operate on each row e.g:
'Rev/Unit': lambda x: calculate_revenue_per_unit(x)
Bear in mind that x is a tuple for each row, so you'll need to unpack that within your calculate_revenue_per_unit function.