vastorbit.datasets.load_amazon

vastorbit.datasets.load_amazon(schema: str | None = None, name: str = 'amazon') → VastFrame

Ingests the amazon dataset into the VAST DataBase.

This dataset is ideal for time series and regression models. If a table with the same name and schema already exists, this function creates a VastFrame from the input relation.

Parameters:

• schema (str, optional) – Schema of the new relation. If empty, the temporary schema is used.

• name (str, optional) – Name of the new relation.

Returns:

The amazon VastFrame.

Return type:

VastFrame

Examples

from vastorbit.datasets import load_amazon

vdf = load_amazon()

	📅 date Date	Abc state Varchar(32)	123 number Integer
1	2016-01-01	PARAÍBA	18
2	2016-02-01	PARAÍBA	4
3	2016-03-01	PARAÍBA	1
4	2016-04-01	PARAÍBA	1
5	2016-05-01	PARAÍBA	1
6	2016-06-01	PARAÍBA	4
7	2016-07-01	PARAÍBA	22
8	2016-08-01	PARAÍBA	50
9	2016-09-01	PARAÍBA	131
10	2016-10-01	PARAÍBA	304
11	2016-11-01	PARAÍBA	132
12	2016-12-01	PARAÍBA	8
13	2016-01-01	PARÁ	1322
14	2016-02-01	PARÁ	430
15	2016-03-01	PARÁ	81
16	2016-04-01	PARÁ	66
17	2016-05-01	PARÁ	154
18	2016-06-01	PARÁ	502
19	2016-07-01	PARÁ	1579
20	2016-08-01	PARÁ	4863
21	2016-09-01	PARÁ	3953
22	2016-10-01	PARÁ	5281
23	2016-11-01	PARÁ	7879
24	2016-12-01	PARÁ	3300
25	2016-01-01	PERNAMBUCO	24
26	2016-02-01	PERNAMBUCO	19
27	2016-03-01	PERNAMBUCO	4
28	2016-04-01	PERNAMBUCO	8
29	2016-05-01	PERNAMBUCO	6
30	2016-06-01	PERNAMBUCO	4
31	2016-07-01	PERNAMBUCO	17
32	2016-08-01	PERNAMBUCO	42
33	2016-09-01	PERNAMBUCO	171
34	2016-10-01	PERNAMBUCO	319
35	2016-11-01	PERNAMBUCO	191
36	2016-12-01	PERNAMBUCO	161
37	2016-01-01	PIAUÍ	94
38	2016-02-01	PIAUÍ	97
39	2016-03-01	PIAUÍ	53
40	2016-04-01	PIAUÍ	35
41	2016-05-01	PIAUÍ	60
42	2016-06-01	PIAUÍ	153
43	2016-07-01	PIAUÍ	754
44	2016-08-01	PIAUÍ	1647
45	2016-09-01	PIAUÍ	1394
46	2016-10-01	PIAUÍ	2598
47	2016-11-01	PIAUÍ	1126
48	2016-12-01	PIAUÍ	374
49	2016-01-01	RIO DE JANEIRO	9
50	2016-02-01	RIO DE JANEIRO	16
51	2016-03-01	RIO DE JANEIRO	16
52	2016-04-01	RIO DE JANEIRO	45
53	2016-05-01	RIO DE JANEIRO	30
54	2016-06-01	RIO DE JANEIRO	37
55	2016-07-01	RIO DE JANEIRO	131
56	2016-08-01	RIO DE JANEIRO	241
57	2016-09-01	RIO DE JANEIRO	195
58	2016-10-01	RIO DE JANEIRO	30
59	2016-11-01	RIO DE JANEIRO	19
60	2016-12-01	RIO DE JANEIRO	5
61	2016-01-01	RIO GRANDE DO NORTE	15
62	2016-02-01	RIO GRANDE DO NORTE	2
63	2016-03-01	RIO GRANDE DO NORTE	1
64	2016-04-01	RIO GRANDE DO NORTE	0
65	2016-05-01	RIO GRANDE DO NORTE	1
66	2016-06-01	RIO GRANDE DO NORTE	4
67	2016-07-01	RIO GRANDE DO NORTE	13
68	2016-08-01	RIO GRANDE DO NORTE	24
69	2016-09-01	RIO GRANDE DO NORTE	44
70	2016-10-01	RIO GRANDE DO NORTE	129
71	2016-11-01	RIO GRANDE DO NORTE	93
72	2016-12-01	RIO GRANDE DO NORTE	75
73	2016-01-01	RIO GRANDE DO SUL	68
74	2016-02-01	RIO GRANDE DO SUL	55
75	2016-03-01	RIO GRANDE DO SUL	30
76	2016-04-01	RIO GRANDE DO SUL	32
77	2016-05-01	RIO GRANDE DO SUL	37
78	2016-06-01	RIO GRANDE DO SUL	261
79	2016-07-01	RIO GRANDE DO SUL	865
80	2016-08-01	RIO GRANDE DO SUL	1111
81	2016-09-01	RIO GRANDE DO SUL	628
82	2016-10-01	RIO GRANDE DO SUL	93
83	2016-11-01	RIO GRANDE DO SUL	105
84	2016-12-01	RIO GRANDE DO SUL	79
85	2016-01-01	RONDÔNIA	93
86	2016-02-01	RONDÔNIA	88
87	2016-03-01	RONDÔNIA	25
88	2016-04-01	RONDÔNIA	59
89	2016-05-01	RONDÔNIA	44
90	2016-06-01	RONDÔNIA	170
91	2016-07-01	RONDÔNIA	969
92	2016-08-01	RONDÔNIA	3675
93	2016-09-01	RONDÔNIA	4208
94	2016-10-01	RONDÔNIA	1844
95	2016-11-01	RONDÔNIA	401
96	2016-12-01	RONDÔNIA	148
97	2016-01-01	RORAIMA	1754
98	2016-02-01	RORAIMA	171
99	2016-03-01	RORAIMA	1081
100	2016-04-01	RORAIMA	126

Rows: 1-100 | Columns: 3