In [ ]:

vDataFrame.recommend(unique_id: str,
                     item_id: str,
                     method: str = "count",
                     rating: (str, tuple) = "",
                     ts: str = "",
                     start_date: (str, datetime.datetime, datetime.date, float,) = "",
                     end_date: (str, datetime.datetime, datetime.date, float,) = "",)

Recommends items based on the collaborative filtering (CF) technique. The implementation of this function is the identical to that of the APRIORI algorithm, but is limited to pairs of items.

Parameters¶

Name	Type	Optional	Description
unique_id	str	❌	Input vcolumn corresponding to a unique ID. It is a primary key.
item_id	str	❌	Input vcolumn corresponding to an item ID. It is a secondary key used to compute the different pairs.
method	str	✓	Method used to recommend. count : Each item will be recommended based on frequencies of the different items pair. avg : Each item will be recommended based on the average rating of the different items pair second element. median : Each item will be recommended based on the median rating of the different items pair second element.
rating	str / tuple	✓	Input vcolumn including the items rating. If the 'rating' type is 'tuple', it must composed of 3 elements: r_vdf, r_item_id, r_name) where: r_vdf is an input vDataFrame. r_item_id is an input vcolumn which must includes the same id as 'item_id'. r_name is an input vcolumn including the items rating.
ts	str	✓	TS (Time Series) vcolumn to use to order the data. The vcolumn type must be date like (date, datetime, timestamp...) or numerical.
start_date	str / date / float	✓	Input Start Date. For example, time = '03-11-1993' will filter the data when 'ts' is lesser than November 1993 the 3rd.
end_date	str / date / float	✓	Input End Date. For example, time = '03-11-1993' will filter the data when 'ts' is greater than November 1993 the 3rd.

Returns¶

vDataFrame : The vDataFrame of the recommendation.

Example¶

In [53]:

from verticapy import vDataFrame
order_items = vDataFrame("olist.order_items")
order_reviews = vDataFrame("olist.order_reviews")
products = vDataFrame("olist.products")
product_category_name_translation = vDataFrame("olist.product_category_name_translation")
# Orders
display(order_items)
# Reviews
display(order_reviews)
# Products
display(products)
# Products Name Translation
display(product_category_name_translation)

	123 Uuid	123 order_item_id Int	123 Uuid	123 Uuid	📅 shipping_limit_date Timestamp	123 price Numeric(9,3)	123 freight_value Numeric(8,3)
1		1			2017-09-19 09:45:35	58.9	13.29
2		1			2017-05-03 11:05:13	239.9	19.93
3		1			2018-01-18 14:48:30	199.0	17.87
4		1			2018-08-15 10:10:18	12.99	12.79
5		1			2017-02-13 13:57:51	199.9	18.14
6		1			2017-05-23 03:55:27	21.9	12.69
7		1			2017-12-14 12:10:31	19.9	11.85
8		1			2018-07-10 12:30:45	810.0	70.75
9		1			2018-03-26 18:31:29	145.95	11.65
10		1			2018-07-06 14:10:56	53.99	11.4
11		1			2018-03-29 22:28:09	59.99	8.88
12		1			2018-07-31 17:30:39	45.0	12.98
13		1			2018-07-26 17:24:20	74.0	23.32
14		1			2018-02-21 02:55:52	49.9	13.37
15		2			2018-02-21 02:55:52	49.9	13.37
16		1			2018-08-17 12:15:10	99.9	27.65
17		1			2018-05-02 09:31:53	639.0	11.34
18		1			2018-05-16 20:57:03	144.0	8.77
19		1			2017-08-21 03:33:13	99.0	13.71
20		1			2018-02-28 12:08:37	25.0	16.11
21		1			2018-03-29 20:07:49	47.9	8.88
22		1			2017-11-27 19:09:02	21.99	11.85
23		1			2018-03-21 11:10:11	119.99	44.4
24		1			2018-03-05 09:35:41	49.0	15.1
25		1			2017-09-15 18:04:37	48.9	16.6
26		1			2017-08-11 00:35:12	219.9	16.98
27		1			2018-01-29 21:51:25	289.0	26.33
28		1			2017-03-29 13:05:42	109.9	25.51
29		1			2018-06-14 05:16:24	27.9	7.94
30		1			2017-12-13 03:16:36	119.9	17.32
31		1			2017-11-30 06:30:55	397.0	24.65
32		1			2018-04-12 19:08:28	59.9	23.35
33		1			2017-10-20 16:07:52	21.33	15.1
34		2			2017-10-20 16:07:52	21.33	15.1
35		3			2017-10-20 16:07:52	21.33	15.1
36		1			2017-05-29 03:15:24	16.5	14.1
37		1			2018-01-18 09:11:24	21.9	15.1
38		1			2018-01-23 03:35:47	39.0	16.11
39		1			2017-05-05 10:05:12	49.75	20.8
40		1			2017-04-24 22:25:19	809.1	44.29
41		1			2018-02-13 19:10:37	99.99	13.72
42		1			2018-03-12 06:50:26	59.9	19.95
43		1			2018-01-04 02:33:42	24.89	17.63
44		2			2018-01-04 02:33:42	24.89	17.63
45		3			2018-01-04 02:33:42	24.89	17.63
46		1			2018-07-10 02:51:10	54.0	8.54
47		1			2018-04-02 17:50:17	81.99	13.01
48		1			2017-11-29 22:38:47	99.0	13.71
49		1			2017-05-18 17:35:11	18.99	7.78
50		2			2017-05-18 17:35:11	18.99	7.78
51		1			2017-05-25 02:15:20	38.33	16.79
52		1			2017-02-01 13:17:57	69.9	18.0
53		1			2017-03-23 11:47:37	195.0	18.21
54		1			2018-05-22 10:59:50	35.9	15.2
55		1			2017-12-04 09:58:07	79.5	21.05
56		1			2017-08-21 03:03:44	29.99	15.1
57		1			2018-04-26 12:30:57	109.9	13.21
58		1			2018-04-10 16:50:11	74.9	16.49
59		1			2018-04-02 16:35:20	14.95	18.23
60		1			2018-05-14 22:14:46	149.9	43.11
61		1			2018-01-03 17:59:35	19.99	14.1
62		1			2018-07-10 09:30:09	390.0	29.39
63		1			2018-03-07 02:49:09	135.0	21.75
64		1			2017-02-06 21:18:03	104.0	16.43
65		1			2017-11-10 21:26:45	149.0	15.8
66		1			2017-12-04 03:32:47	279.9	13.46
67		1			2018-02-27 11:55:57	44.9	23.22
68		1			2018-07-26 17:44:36	37.99	19.18
69		1			2018-08-21 03:35:17	29.99	15.31
70		1			2017-12-22 15:51:31	120.0	15.59
71		1			2018-09-03 03:50:23	492.75	21.62
72		1			2018-05-08 12:14:52	94.9	18.54
73		1			2018-02-22 19:46:50	88.34	21.11
74		1			2017-06-26 22:10:14	199.99	65.56
75		1			2018-03-15 10:35:37	99.9	19.67
76		1			2018-05-21 17:32:03	78.0	8.9
77		2			2018-05-21 17:32:03	78.0	8.9
78		1			2017-11-27 03:47:23	155.0	14.84
79		1			2018-07-04 17:11:12	249.9	34.23
80		1			2018-03-27 13:15:27	53.98	23.31
81		1			2017-08-10 09:30:15	8.99	32.57
82		2			2017-08-10 09:30:15	44.9	7.16
83		1			2017-11-29 23:13:38	60.6	17.67
84		1			2018-04-06 17:10:21	225.0	67.24
85		1			2018-08-17 15:04:25	39.9	15.38
86		1			2018-04-17 09:12:11	76.0	34.07
87		1			2018-03-15 19:28:51	159.0	27.19
88		1			2017-05-24 19:22:34	102.9	14.45
89		2			2017-05-24 19:22:34	102.9	14.45
90		1			2017-07-28 03:45:26	63.9	16.89
91		1			2018-05-02 22:10:29	168.8	8.21
92		1			2017-09-29 17:50:16	59.9	9.94
93		2			2017-09-29 17:50:16	59.9	9.94
94		1			2018-07-18 04:25:09	232.75	28.58
95		1			2018-08-23 15:11:40	48.9	18.44
96		1			2017-08-23 01:25:39	87.0	12.11
97		1			2017-12-12 01:29:45	131.9	18.54
98		1			2018-02-27 03:31:08	19.9	14.1
99		1			2018-08-08 15:10:11	136.99	66.04
100		1			2017-10-19 01:07:30	399.99	23.64

Rows: 1-100 | Columns: 7

	123 Uuid	123 Uuid	123 review_score Int	Abc review_comment_title Varchar(70)	Abc Varchar(538)	📅 review_creation_date Timestamp	📅 review_answer_timestamp Timestamp
1			5	[null]		2018-03-20 00:00:00	2018-03-20 18:36:04
2			5	[null]		2018-06-22 00:00:00	2018-06-26 13:51:29
3			5	Entrega rápida!		2018-04-25 00:00:00	2018-04-26 14:55:36
4			5	[null]		2017-05-30 00:00:00	2017-06-01 23:28:55
5			5	[null]		2017-08-12 00:00:00	2017-08-13 19:56:53
6			5	[null]		2018-02-06 00:00:00	2018-02-06 20:54:37
7			1	[null]		2018-05-24 00:00:00	2018-05-24 12:45:38
8			5	SUPER RECOMENDO		2018-07-01 00:00:00	2018-07-02 12:32:24
9			5	[null]		2017-09-01 00:00:00	2017-09-02 13:04:08
10			5	[null]		2017-11-25 00:00:00	2017-11-27 11:37:22
11			5	[null]		2017-06-20 00:00:00	2017-06-22 23:19:41
12			5	[null]		2017-10-15 01:00:00	2017-10-15 17:53:22
13			5	[null]		2017-11-15 00:00:00	2017-11-18 02:22:01
14			5	Bom		2018-06-19 00:00:00	2018-06-20 04:48:13
15			5	[null]		2017-08-26 00:00:00	2017-08-30 12:03:31
16			5	[null]		2018-06-14 00:00:00	2018-06-16 19:15:24
17			4	[null]		2017-04-06 00:00:00	2017-04-07 18:00:25
18			5	[null]		2017-08-03 00:00:00	2017-08-05 23:47:13
19			1	[null]		2018-03-25 00:00:00	2018-03-26 11:38:15
20			4	[null]		2017-07-26 00:00:00	2017-07-26 23:31:23
21			5	[null]		2018-01-18 00:00:00	2018-01-19 01:51:32
22			5	[null]		2018-06-13 00:00:00	2018-06-14 00:24:36
23			4	[null]		2018-03-26 00:00:00	2018-04-02 14:03:42
24			5	[null]		2018-04-14 00:00:00	2018-04-16 23:11:12
25			4	[null]		2017-05-17 00:00:00	2017-05-18 11:39:45
26			5	[null]		2017-06-03 00:00:00	2017-06-07 04:00:21
27			4	[null]		2017-05-30 00:00:00	2017-05-30 23:08:24
28			4	[null]		2018-08-12 00:00:00	2018-08-14 11:35:39
29			3	[null]		2017-11-14 00:00:00	2017-11-16 19:23:19
30			3	[null]		2017-12-22 00:00:00	2017-12-22 23:49:40
31			4	[null]		2018-08-24 00:00:00	2018-08-24 11:56:51
32			5	ENTREGA DENTRO DO PRAZO		2018-05-16 00:00:00	2018-05-25 17:57:16
33			4	[null]		2018-03-13 00:00:00	2018-03-13 20:34:04
34			3	[null]		2018-06-15 00:00:00	2018-06-15 19:20:57
35			5	[null]		2018-04-27 00:00:00	2018-04-28 02:28:01
36			5	[null]		2017-09-27 00:00:00	2017-09-27 23:54:04
37			4	[null]		2017-12-20 00:00:00	2017-12-22 22:25:40
38			4	[null]		2017-02-14 00:00:00	2017-02-15 14:28:07
39			5	[null]		2018-03-15 00:00:00	2018-03-16 11:18:48
40			5	[null]		2018-07-21 00:00:00	2018-07-24 11:57:27
41			4	[null]		2017-08-01 00:00:00	2017-08-04 11:08:08
42			5	[null]		2018-06-22 00:00:00	2018-06-25 14:30:21
43			5	[null]		2017-11-25 00:00:00	2017-11-26 13:21:30
44			5	Recomendo		2018-04-28 00:00:00	2018-04-30 23:48:44
45			5	[null]		2017-07-15 00:00:00	2017-07-16 16:33:50
46			1	[null]		2018-05-01 00:00:00	2018-05-01 21:25:23
47			5	[null]		2017-06-09 00:00:00	2017-06-12 19:36:32
48			5	[null]		2017-04-20 00:00:00	2017-04-26 10:44:16
49			2	[null]		2018-04-21 00:00:00	2018-04-21 17:05:31
50			4	[null]		2018-07-06 00:00:00	2018-07-09 15:03:17
51			2	[null]		2017-11-29 00:00:00	2017-11-29 09:31:07
52			4	[null]		2017-11-09 00:00:00	2017-11-10 20:22:03
53			5	[null]		2017-11-09 00:00:00	2017-11-09 14:45:42
54			4	[null]		2018-02-06 00:00:00	2018-02-07 12:04:30
55			4	[null]		2018-04-11 00:00:00	2018-04-11 11:09:23
56			5	recomendo		2018-07-10 00:00:00	2018-07-11 11:29:17
57			4	[null]		2018-02-03 00:00:00	2018-02-05 23:58:28
58			5	[null]		2017-03-07 00:00:00	2017-03-08 08:49:45
59			5	[null]		2017-01-13 00:00:00	2017-01-14 11:20:25
60			5	[null]		2018-06-14 00:00:00	2018-06-17 23:58:20
61			4	[null]		2018-06-13 00:00:00	2018-06-14 16:41:13
62			2	[null]		2017-05-21 00:00:00	2017-05-21 23:49:03
63			5	[null]		2017-03-12 00:00:00	2017-03-13 20:56:20
64			5	[null]		2017-10-08 00:00:00	2017-10-10 02:03:18
65			5	[null]		2018-01-05 00:00:00	2018-01-06 21:46:11
66			2	[null]		2018-01-13 00:00:00	2018-01-17 13:19:36
67			5	[null]		2017-08-22 00:00:00	2017-08-22 19:52:17
68			1	[null]		2017-11-22 00:00:00	2017-11-22 14:21:08
69			5	super recomendado		2018-05-18 00:00:00	2018-05-21 15:47:15
70			5	[null]		2017-11-29 00:00:00	2017-11-30 01:07:38
71			5	[null]		2018-03-29 00:00:00	2018-04-02 01:16:46
72			3	[null]		2018-05-16 00:00:00	2018-05-16 18:16:57
73			5	[null]		2017-11-02 00:00:00	2017-11-07 14:45:12
74			5	[null]		2017-03-08 00:00:00	2017-03-09 10:00:37
75			1	[null]		2018-03-21 00:00:00	2018-03-21 09:36:01
76			4	[null]		2018-02-20 00:00:00	2018-02-26 12:44:42
77			5	[null]		2017-10-09 00:00:00	2017-10-09 20:44:01
78			5	[null]		2018-05-17 00:00:00	2018-05-20 16:12:30
79			5	[null]		2018-02-10 00:00:00	2018-02-12 13:05:05
80			5	[null]		2017-06-22 00:00:00	2017-06-23 00:49:50
81			5	[null]		2017-12-21 00:00:00	2017-12-23 18:46:59
82			5	[null]		2017-03-17 00:00:00	2017-03-20 00:39:40
83			3	[null]		2017-05-04 00:00:00	2017-05-08 22:18:18
84			5	[null]		2018-03-03 00:00:00	2018-03-05 11:56:52
85			1	[null]		2018-07-29 00:00:00	2018-07-30 05:09:09
86			1	[null]		2018-08-10 00:00:00	2018-08-10 07:31:10
87			5	[null]		2018-05-18 00:00:00	2018-05-20 16:15:11
88			5	[null]		2018-05-06 00:00:00	2018-05-07 15:00:11
89			5	[null]		2018-05-30 00:00:00	2018-05-30 22:10:23
90			3	[null]		2018-04-18 00:00:00	2018-06-14 23:46:31
91			5	[null]		2018-04-06 00:00:00	2018-04-07 15:57:53
92			3	[null]		2017-05-10 00:00:00	2017-05-13 00:26:19
93			3	[null]		2018-03-09 00:00:00	2018-03-10 23:32:33
94			5	[null]		2018-08-11 00:00:00	2018-08-11 18:03:41
95			4	[null]		2018-08-17 00:00:00	2018-08-18 01:32:06
96			5	[null]		2018-07-13 00:00:00	2018-07-14 12:34:42
97			3	[null]		2017-02-14 00:00:00	2017-02-15 03:13:03
98			5	[null]		2018-08-26 00:00:00	2018-08-29 19:11:50
99			5	[null]		2017-11-14 00:00:00	2017-11-16 13:25:12
100			5	[null]		2017-12-08 00:00:00	2017-12-10 19:07:17

Rows: 1-100 | Columns: 7

	123 Uuid	Abc Varchar(92)	123 product_name_lenght Int	123 product_description_lenght Int	123 product_photos_qty Int	123 product_weight_g Int	123 product_length_cm Int	123 product_height_cm Int	123 product_width_cm Int
1			53	596	6	300	20	16	16
2			56	752	4	1225	55	10	26
3			50	266	2	300	45	15	35
4			25	364	3	550	19	24	12
5			48	613	4	250	22	11	15
6			58	177	1	100	16	15	16
7			42	2461	1	700	25	5	15
8			53	274	1	600	30	20	20
9			42	253	1	6000	40	4	30
10			45	520	3	600	26	8	22
11			50	381	1	1800	47	21	41
12			56	3893	6	300	30	15	15
13			55	344	3	1850	36	37	16
14			58	946	6	650	25	10	15
15			53	631	2	750	30	5	35
16			18	263	1	355	22	16	12
17			54	1750	3	2600	105	3	70
18			44	629	3	600	40	30	40
19			43	112	5	350	45	30	30
20			59	1295	1	200	20	11	11
21			36	707	3	150	20	10	15
22			28	502	4	800	25	25	25
23			40	437	1	1550	26	6	20
24			59	1100	6	1550	16	10	16
25			53	866	3	200	18	5	13
26			52	420	2	5050	50	8	50
27			55	214	1	1000	16	46	36
28			55	900	4	1850	45	12	45
29			55	705	2	21100	100	20	60
30			43	1213	1	1800	30	22	22
31			51	1470	3	150	35	2	25
32			60	1883	3	175	23	11	13
33			36	1105	1	380	18	12	14
34			59	1691	1	20400	30	30	30
35			51	695	1	200	20	18	18
36			50	319	1	900	100	11	12
37			46	647	1	550	30	10	25
38			44	776	2	10000	65	35	35
39			54	942	1	50	16	5	11
40			33	384	1	400	17	7	20
41			59	3029	1	1200	20	20	20
42			60	382	1	500	25	2	17
43			50	984	2	246	22	15	19
44			45	569	1	150	16	7	15
45			59	412	1	200	16	2	11
46			33	223	2	950	60	15	15
47			59	703	6	4550	47	19	23
48			49	326	1	250	20	20	20
49			37	670	2	600	17	15	22
50			50	329	2	150	16	18	16
51			48	808	1	2800	30	30	30
52			56	841	1	1340	32	14	27
53			[null]	[null]	[null]	500	24	4	15
54			58	593	1	1800	40	8	30
55			44	216	1	2200	37	10	32
56			59	1631	4	2550	38	42	25
57			48	516	1	690	17	17	13
58			57	676	2	633	19	12	17
59			59	1735	5	1550	40	20	20
60			59	253	1	3875	46	12	43
61			55	197	1	1133	16	16	11
62			39	169	4	450	16	24	11
63			56	279	2	2800	50	35	15
64			48	1411	1	350	16	16	16
65			55	373	1	800	40	5	11
66			58	916	3	700	20	35	20
67			39	991	3	150	20	20	20
68			45	916	1	23250	50	20	20
69			57	338	3	400	23	7	15
70			38	581	1	150	16	23	13
71			58	734	3	250	17	4	12
72			26	345	1	700	16	16	16
73			[null]	[null]	[null]	2100	50	30	30
74			38	440	1	600	22	33	16
75			59	351	2	250	16	16	16
76			53	786	4	1125	28	15	23
77			57	319	2	250	16	5	11
78			52	418	1	7850	28	62	28
79			27	406	4	200	18	7	12
80			36	1241	3	12100	60	60	20
81			34	249	1	200	16	11	11
82			52	396	1	900	45	15	35
83			56	1027	1	187	17	13	14
84			58	596	4	512	30	12	18
85			56	183	2	650	35	15	28
86			41	569	1	350	17	15	17
87			63	326	4	150	16	4	13
88			47	596	1	300	19	10	15
89			30	253	1	350	24	5	17
90			60	935	1	9050	56	26	28
91			41	312	1	18550	54	35	45
92			49	998	2	200	16	16	11
93			52	857	1	225	16	4	14
94			[null]	[null]	[null]	400	24	4	15
95			53	646	1	300	16	30	20
96			54	197	8	3400	42	16	26
97			42	1202	6	1550	42	17	17
98			59	600	1	200	19	4	11
99			59	642	5	1650	60	8	33
100			47	816	2	300	16	5	12

Rows: 1-100 | Columns: 9

	Abc Varchar(92)	Abc Varchar(78)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71

Rows: 1-71 | Columns: 2

In [55]:

# Recommendation based on the Frequencies

order_items.recommend("order_id",
                      "product_id",)

Out[55]:

	123 Uuid	123 Uuid	123 cnt Int	123 rank Integer
1			2	1
2			2	2
3			4	1
4			2	2
5			2	3
6			2	1
7			6	1
8			2	1
9			2	2
10			2	1
11			2	2
12			3	1
13			2	1
14			2	1
15			2	1
16			4	1
17			3	1
18			2	1
19			2	1
20			2	1
21			3	1
22			2	1
23			6	1
24			3	1
25			2	2
26			9	1
27			3	1
28			100	1
29			10	2
30			3	1
31			2	1
32			6	1
33			4	1
34			3	1
35			2	1
36			2	1
37			4	1
38			4	2
39			16	1
40			2	1
41			3	1
42			2	2
43			2	3
44			4	1
45			4	2
46			4	3
47			3	1
48			2	1
49			2	1
50			3	1
51			3	2
52			2	1
53			2	1
54			2	1
55			9	1
56			2	2
57			2	1
58			2	1
59			2	1
60			5	1
61			8	1
62			2	1
63			2	1
64			2	1
65			13	1
66			7	2
67			7	3
68			7	4
69			7	5
70			4	6
71			2	7
72			2	8
73			2	9
74			2	1
75			2	2
76			3	1
77			2	1
78			4	1
79			2	2
80			2	1
81			2	1
82			2	2
83			2	1
84			2	2
85			3	1
86			6	1
87			3	1
88			24	1
89			3	1
90			4	1
91			2	2
92			2	3
93			2	4
94			2	5
95			2	6
96			2	7
97			9	1
98			4	2
99			2	1
100			2	1

Rows: 1-100 | Columns: 4

In [54]:

# Recommendation based on the Score

vdf = order_items[["order_id", "product_id"]].join(order_reviews,
                                                   how = "left",
                                                   on = {"order_id": "order_id"},
                                                   expr2 = ["review_score"])
recommend = vdf.recommend("order_id",
                          "product_id",
                          method = "avg",
                          rating = "review_score")
recommend.join(products,
               how = "left",
               on = {"item1": "product_id"},
               expr2 = ["product_category_name AS product_category_name_1"]).join(
               products,
               how = "left",
               on = {"item2": "product_id"},
               expr2 = ["product_category_name AS product_category_name_2"]).join(
               product_category_name_translation,
               how = "left",
               on = {"product_category_name_1": "product_category_name"},
               expr2 = ["product_category_name_english AS product_category_name_english_1"]).join(
               product_category_name_translation,
               how = "left",
               on = {"product_category_name_2": "product_category_name"},
               expr2 = ["product_category_name_english AS product_category_name_english_2"])

Out[54]:

	123 Uuid	123 Uuid	123 cnt Int	123 score1 Float	123 score2 Float	123 rank Int	Abc Varchar(92)	Abc Varchar(92)	Abc product_category_name_english_1 Varchar(78)	Abc product_category_name_english_2 Varchar(78)
1			2	4.61538461538461	3.67857142857143	1			perfumery	perfumery
2			2	4.61538461538461	3.25	2			perfumery	perfumery
3			2	3.5	4.0	1			housewares	garden_tools
4			4	3.5	1.0	2			housewares	housewares
5			2	3.5	1.0	3			housewares	housewares
6			2	4.71428571428571	4.2	1			consoles_games	consoles_games
7			6	1.5	1.75	1			cool_stuff	cool_stuff
8			2	4.11111111111111	4.69444444444444	1			baby	toys
9			2	4.11111111111111	4.63636363636364	2			baby	baby
10			2	4.01639344262295	4.45454545454545	1			bed_bath_table	bed_bath_table
11			2	4.01639344262295	3.59090909090909	2			bed_bath_table	bed_bath_table
12			3	3.41666666666667	3.25	1			perfumery	perfumery
13			2	1.75	1.33333333333333	1			bed_bath_table	bed_bath_table
14			2	3.57142857142857	4.33333333333333	1			auto	construction_tools_construction
15			2	1.0	4.12173913043478	1			housewares	bed_bath_table
16			4	1.0	1.0	1			construction_tools_safety	home_construction
17			3	5.0	5.0	1			pet_shop	pet_shop
18			2	3.8	5.0	1			bed_bath_table	bed_bath_table
19			2	4.375	3.57142857142857	1			bed_bath_table	bed_bath_table
20			2	[null]	[null]	1			computers_accessories	computers_accessories
21			3	4.0	1.0	1			stationery	stationery
22			2	5.0	5.0	1			sports_leisure	sports_leisure
23			6	5.0	5.0	1			office_furniture	office_furniture
24			2	3.14285714285714	4.33333333333333	1			bed_bath_table	bed_bath_table
25			3	3.14285714285714	3.76190476190476	2			bed_bath_table	bed_bath_table
26			9	5.0	3.66666666666667	1			cool_stuff	cool_stuff
27			3	3.625	4.17948717948718	1			stationery	stationery
28			100	1.0	1.0	1			health_beauty	health_beauty
29			10	1.0	1.0	2			health_beauty	health_beauty
30			3	2.0	4.69230769230769	1			furniture_decor	furniture_decor
31			2	4.0	3.0	1			bed_bath_table	bed_bath_table
32			6	4.4	4.52631578947368	1			auto	auto
33			4	3.4	3.74285714285714	1			watches_gifts	watches_gifts
34			4	4.71428571428571	4.0	1			sports_leisure	food_drink
35			3	4.0	4.09523809523809	1			furniture_decor	furniture_decor
36			2	1.0	4.2962962962963	1			furniture_decor	bed_bath_table
37			2	3.98507462686567	4.55555555555556	1			bed_bath_table	bed_bath_table
38			4	4.0	4.33333333333333	1			housewares	housewares
39			4	4.0	4.0	2			housewares	housewares
40			16	4.55555555555556	4.46153846153846	1			bed_bath_table	bed_bath_table
41			2	3.51724137931034	3.52380952380952	1			furniture_decor	furniture_decor
42			2	3.6	4.0	1			costruction_tools_garden	costruction_tools_garden
43			3	3.6	3.75	2			costruction_tools_garden	[null]
44			2	3.6	3.6	3			costruction_tools_garden	costruction_tools_garden
45			4	3.66666666666667	5.0	1			bed_bath_table	bed_bath_table
46			4	3.66666666666667	4.0	2			bed_bath_table	bed_bath_table
47			4	3.66666666666667	3.66666666666667	3			bed_bath_table	bed_bath_table
48			3	2.75	1.0	1			furniture_decor	housewares
49			2	4.5	4.71428571428571	1			housewares	housewares
50			2	4.25	4.0	1			bed_bath_table	bed_bath_table
51			3	4.09523809523809	4.0	1			furniture_decor	furniture_decor
52			3	4.09523809523809	3.5	2			furniture_decor	furniture_decor
53			2	3.85714285714286	4.05555555555556	1			furniture_decor	furniture_decor
54			2	3.42857142857143	3.0	1			sports_leisure	sports_leisure
55			2	5.0	4.07299270072993	1			fashion_bags_accessories	fashion_bags_accessories
56			2	4.24390243902439	4.64102564102564	1			health_beauty	health_beauty
57			9	4.24390243902439	4.54135338345865	2			health_beauty	health_beauty
58			2	4.84210526315789	4.8	1			health_beauty	health_beauty
59			2	5.0	[null]	1			home_construction	home_construction
60			2	2.36363636363636	3.34545454545455	1			furniture_decor	office_furniture
61			5	4.20567375886525	3.89473684210526	1			consoles_games	consoles_games
62			8	5.0	5.0	1			construction_tools_construction	construction_tools_construction
63			2	5.0	3.94594594594595	1			furniture_decor	garden_tools
64			2	5.0	5.0	1			health_beauty	health_beauty
65			2	4.33333333333333	4.75	1			luggage_accessories	luggage_accessories
66			2	4.08181818181818	4.55555555555556	1			garden_tools	bed_bath_table
67			7	4.08181818181818	4.23275862068965	2			garden_tools	garden_tools
68			7	4.08181818181818	4.13941018766756	3			garden_tools	garden_tools
69			7	4.08181818181818	4.00852272727273	4			garden_tools	garden_tools
70			2	4.08181818181818	4.0	5			garden_tools	home_construction
71			19	4.08181818181818	3.96595744680851	6			garden_tools	garden_tools
72			10	4.08181818181818	3.94594594594595	7			garden_tools	garden_tools
73			4	4.08181818181818	3.33333333333333	8			garden_tools	baby
74			2	4.08181818181818	3.0	9			garden_tools	sports_leisure
75			2	4.07142857142857	4.29032258064516	1			watches_gifts	watches_gifts
76			2	4.07142857142857	4.0	2			watches_gifts	watches_gifts
77			3	4.33333333333333	4.16666666666667	1			bed_bath_table	bed_bath_table
78			2	4.0	4.0	1			housewares	furniture_decor
79			4	3.83333333333333	4.71428571428571	1			auto	auto
80			2	3.83333333333333	4.37647058823529	2			auto	auto
81			2	4.5	5.0	1			[null]	furniture_decor
82			2	3.0	3.0	1			construction_tools_construction	construction_tools_construction
83			2	3.0	2.66666666666667	2			construction_tools_construction	construction_tools_construction
84			2	2.93333333333333	3.73684210526316	1			baby	cool_stuff
85			2	2.93333333333333	3.0	2			baby	baby
86			3	5.0	5.0	1			books_general_interest	books_general_interest
87			6	4.13636363636364	3.5	1			computers_accessories	computers_accessories
88			3	2.75	2.0	1			telephony	telephony
89			24	2.75	1.42857142857143	1			office_furniture	office_furniture
90			3	5.0	5.0	1			furniture_decor	furniture_decor
91			2	3.0	4.0	1			construction_tools_construction	construction_tools_construction
92			2	3.0	4.0	2			construction_tools_construction	construction_tools_construction
93			2	3.0	3.66666666666667	3			construction_tools_construction	construction_tools_construction
94			2	3.0	3.66666666666667	4			construction_tools_construction	construction_tools_construction
95			2	3.0	3.0	5			construction_tools_construction	construction_tools_construction
96			4	3.0	2.66666666666667	6			construction_tools_construction	construction_tools_construction
97			2	3.0	1.66666666666667	7			construction_tools_construction	construction_tools_construction
98			9	3.4	3.66666666666667	1			housewares	housewares
99			4	3.4	3.0	2			housewares	bed_bath_table
100			2	3.78571428571429	2.5	1			furniture_decor	construction_tools_lights

Rows: 1-100 | Columns: 10

vDataFrame.recommend¶

Parameters¶

Returns¶

Example¶