pandas - sum objects in DataFrame column and join with DataFrame

Question

I have a DataFrame like this:

d = {'buy': Series([1., 0., 1., 0., 1., 1., 0., 0., 1., 1., 1., 0., 1., 0.]),
'id': Series([1., 2., 4., 2., 3., 4., 1., 1., 2., 1., 3., 3., 2., 3.]), 'datetime': Series(['01.02.2015',
'01.02.2015', '01.03.2015', '03.01.2015', '06.02.2015', '01.09.2015', '18.03.2015', '02.02.2015', '03.02.2015',
'06.04.2015', '01.04.2015', '03.04.2015', '02.04.2015', '20.03.2015'])}

df = DataFrame(d)
print(df)

    buy    datetime  id
0     1  01.02.2015   1
1     0  01.02.2015   2
2     1  01.03.2015   4
3     0  03.01.2015   2
4     0  06.02.2015   3
5     1  01.09.2015   4
6     0  18.03.2015   1
7     0  02.02.2015   1
8     1  03.02.2015   2
9     1  06.04.2015   1
10    1  01.04.2015   3
11    0  03.04.2015   3
12    1  02.04.2015   2
13    0  20.03.2015   3

Firstly, I group it by 'id' and receive only the latest 'datetime' from each 'id':

df1 = df.sort(columns=['datetime']).drop_duplicates(subset='id', take_last=True)
print(df1)

    buy    datetime  id
5     1  01.09.2015   4
8     1  03.02.2015   2
6     0  18.03.2015   1
13    0  20.03.2015   3

And next I need to sum every id's 'buy' and join the new column (I named it buy_count') with my DataFrame. I have smth like this:

buys = df.groupby(by='id')['buy'].sum()

print(buys)

id
1    2
2    2
3    1
4    2

But I can't insert 'buy_count' to the DataFrame:

df1['buys_count'] = buys
print(df1)

    buy    datetime  id  buys_count
5     1  01.09.2015   4         NaN
8     1  03.02.2015   2         NaN
6     0  18.03.2015   1         NaN
13    0  20.03.2015   3         NaN

As I guess there is some trouble with indexes. Tried to change indexes, try use loops, but all were unsuccessful. How can I get this?

Answer 1

You can call map against 'id' column of df1 and pass buys to perform a lookup:

In [270]:
df1['buy_count'] = df1['id'].map(buys)
df1

Out[270]:
    buy    datetime  id  buy_count
5     1  01.09.2015   4          2
8     1  03.02.2015   2          2
6     0  18.03.2015   1          2
13    0  20.03.2015   3          2

By the way I don't get the same output as you for buys