Manipulating Pandas dataframes and pivoting

import pandas
from numpy import *

f = pandas.read_csv('fruit_sales_log.csv')
f.head()

	Date	Seller	Item	Amount
0	07/01/17	Yiqing	watermelons	6.21
1	07/01/17	Sakar	watermelons	12.33
2	07/01/17	Yiqing	apples	18.02
3	07/02/17	Sakar	watermelons	1.95
4	07/04/17	Yiqing	watermelons	14.88

Having selected and copied the data in a spreadsheet, for example:

# fix headers
f.columns = [item.strip() for item in f.columns]
f.columns

Index(['Date', 'Seller', 'Item', 'Amount'], dtype='object')

f[['Seller','Amount']]

	Seller	Amount
0	Yiqing	6.21
1	Sakar	12.33
2	Yiqing	18.02
3	Sakar	1.95
4	Yiqing	14.88
5	Samuel	11.73
6	Jonathan	5.11
7	Anthony	14.68
8	Samuel	4.79
9	Jonathan	13.06
10	Jonathan	3.61
11	Yiqing	4.54
12	Sakar	5.16
13	Samuel	3.17
14	Anthony	11.75
15	Yiqing	11.19
16	Katherine	2.93
17	Samuel	1.91
18	Samuel	1.89
19	Sakar	7.59
20	Anthony	2.85
21	Sakar	15.35
22	Katherine	13.92
23	Yiqing	17.35
24	Yiqing	11.87
25	Yiqing	2.14
26	Jonathan	12.57
27	Jonathan	11.68
28	Yiqing	13.83
29	Anthony	7.26
30	Megan	7.07
31	Jonathan	18.64
32	Samuel	17.73
33	Samuel	2.86
34	Megan	4.99
35	Samuel	18.06
36	Sakar	10.53
37	Anthony	6.15
38	Sakar	11.17
39	Anthony	14.95
40	Megan	13.39
41	Anthony	9.58
42	Yiqing	14.23
43	Anthony	19.06
44	Megan	16.69
45	Megan	14.89
46	Jonathan	6.96
47	Sakar	8.68
48	Jonathan	1.29
49	Samuel	18.20

f.pivot_table(values='Amount',index='Seller',columns='Item',aggfunc=sum,margins=True).fillna(0)

Item	apples	peaches	watermelons	All
Seller
Anthony	34.64	17.90	33.74	86.28
Jonathan	5.11	12.57	55.24	72.92
Katherine	2.93	0.00	13.92	16.85
Megan	12.06	16.69	28.28	57.03
Sakar	18.12	16.33	38.31	72.76
Samuel	6.66	29.46	44.22	80.34
Yiqing	58.43	34.74	21.09	114.26
All	137.95	127.69	234.80	500.44

Does this remind you of GROUP BY in sql?