groupby weighted average and sum in pandas dataframe

Question

I have a dataframe ,

    Out[78]: 
   contract month year  buys  adjusted_lots    price
0         W     Z    5  Sell             -5   554.85
1         C              


        

Answer 1

The solution that uses a dict of aggregation functions will be deprecated in a future version of pandas (version 0.22):

FutureWarning: using a dict with renaming is deprecated and will be removed in a future 
version return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)

Use a groupby apply and return a Series to rename columns as discussed in: Rename result columns from Pandas aggregation ("FutureWarning: using a dict with renaming is deprecated")

def my_agg(x):
    names = {'weighted_ave_price': (x['adjusted_lots'] * x['price']).sum()/x['adjusted_lots'].sum()}
    return pd.Series(names, index=['weighted_ave_price'])

produces the same result:

>df.groupby(["contract", "month", "year", "buys"]).apply(my_agg)

                          weighted_ave_price
contract month year buys                    
C        Z     5    Sell          424.828947
CC       U     5    Buy          3328.000000
SB       V     5    Buy            11.637500
W        Z     5    Sell          554.850000