分组并应用功能

Question

I would like to groupby by the variable of my df "cod_id" and then apply this function:

[dd.loc[dd['dt_op'].between(d, d + pd.Timedelta(days = 7)), 'quantity'].sum() \
                        for d in data_1['dt_op']]

Moving from this df:

print(dd)
dt_op      quantity      cod_id
20/01/18      1            613
21/01/18      8            611
21/01/18      1            613 
...

To this one:

print(final_dd)
n = 7

dt_op      quantity   product_code     Final_Quantity
20/01/18      1            613               2
21/01/18      8            611               8
25/01/18      1            613               1
...

I tried with:

dd.groupby(['cod_id']).apply([dd.loc[dd['dt_op'].between(d, d + pd.Timedelta(days = 7)), 'quantity'].sum() \
                            for d in data_1['dt_op']])

But it raises:

TypeError: unhashable type: 'list'

Answer 1

This is a cumbersome but working solution:

def lookforward(x):
    L = [x.loc[x['dt_op'].between(row.dt_op, row.dt_op + pd.Timedelta(days=7)), \
         'quantity'].sum() for row in x.itertuples(index=False)]
    return pd.Series(L, index=x.index)

s = df.groupby('cod_id').apply(lookforward)
s.index = s.index.droplevel(0)

df['Final_Quantity'] = s

print(df)

       dt_op  quantity  cod_id  Final_Quantity
0 2018-01-20         1     613               2
1 2018-01-21         8     611               8
2 2018-01-21         1     613               1