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verticapy.datasets.load_smart_meters

verticapy.datasets.load_smart_meters(schema: str | None = None, name: str = 'smart_meters') → vDataFrame

Ingests the smart meters dataset into the Vertica 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 vDataFrame 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

vDataFrame

the smart meters vDataFrame.

Examples

If you call this loader without any arguments, the dataset is loaded using the default schema (public).

from verticapy.datasets import load_smart_meters

load_smart_meters()

	📅 time Timestamp(29)	123 val Numeric(13)	123 id Integer
1	2014-01-01 07:15:00	2.306	9
2	2014-01-01 17:00:00	1.35	5
3	2014-01-02 02:45:00	0.055	3
4	2014-01-02 04:45:00	0.1	7
5	2014-01-02 06:45:00	0.048	1
6	2014-01-03 06:30:00	0.032	7
7	2014-01-03 07:45:00	0.084	9
8	2014-01-03 10:30:00	0.074	9
9	2014-01-03 22:15:00	0.591	5
10	2014-01-04 01:15:00	0.131	6
11	2014-01-04 02:45:00	0.185	5
12	2014-01-04 05:45:00	0.062	8
13	2014-01-04 07:30:00	0.068	2
14	2014-01-04 07:45:00	0.309	4
15	2014-01-04 13:30:00	1.36	8
16	2014-01-04 18:00:00	0.641	4
17	2014-01-04 22:30:00	0.827	4
18	2014-01-05 08:00:00	0.09	4
19	2014-01-05 10:00:00	0.281	3
20	2014-01-05 10:15:00	0.58	6
21	2014-01-05 17:30:00	0.625	4
22	2014-01-06 07:45:00	0.53	2
23	2014-01-06 17:45:00	0.336	5
24	2014-01-06 20:15:00	0.23	9
25	2014-01-07 01:30:00	0.189	4
26	2014-01-07 03:45:00	0.021	2
27	2014-01-07 08:15:00	0.034	7
28	2014-01-07 11:15:00	0.315	6
29	2014-01-07 13:15:00	0.087	3
30	2014-01-07 17:45:00	0.341	7
31	2014-01-08 08:00:00	4.652	8
32	2014-01-08 21:45:00	0.471	7
33	2014-01-08 23:15:00	0.571	8
34	2014-01-09 11:00:00	0.139	4
35	2014-01-09 14:00:00	0.125	8
36	2014-01-09 15:00:00	0.928	0
37	2014-01-09 22:45:00	0.538	8
38	2014-01-10 05:30:00	0.076	3
39	2014-01-10 08:15:00	0.067	5
40	2014-01-10 09:30:00	0.204	4
41	2014-01-10 11:00:00	0.833	3
42	2014-01-10 14:30:00	0.387	3
43	2014-01-10 18:00:00	0.492	2
44	2014-01-10 18:00:00	1.337	9
45	2014-01-10 18:15:00	0.161	5
46	2014-01-10 19:15:00	0.479	5
47	2014-01-10 19:45:00	0.307	6
48	2014-01-11 10:45:00	1.045	0
49	2014-01-11 15:30:00	0.147	6
50	2014-01-12 05:15:00	0.112	7
51	2014-01-12 10:30:00	0.002	4
52	2014-01-12 12:15:00	11.405	6
53	2014-01-12 14:15:00	1.935	7
54	2014-01-12 17:00:00	1.002	6
55	2014-01-13 21:45:00	0.907	2
56	2014-01-14 00:30:00	0.121	1
57	2014-01-14 03:30:00	0.053	2
58	2014-01-14 05:00:00	0.091	7
59	2014-01-14 07:15:00	0.019	4
60	2014-01-14 15:15:00	0.128	5
61	2014-01-14 20:30:00	1.298	4
62	2014-01-14 23:15:00	0.909	1
63	2014-01-15 03:30:00	0.013	1
64	2014-01-16 02:30:00	0.077	8
65	2014-01-16 02:45:00	0.11	4
66	2014-01-16 03:15:00	0.679	2
67	2014-01-16 04:45:00	0.036	8
68	2014-01-16 15:00:00	0.264	8
69	2014-01-16 17:00:00	1.807	7
70	2014-01-16 17:15:00	1.507	2
71	2014-01-16 20:15:00	0.355	4
72	2014-01-17 14:30:00	0.23	3
73	2014-01-18 00:00:00	0.4215	2
74	2014-01-18 03:00:00	0.152	3
75	2014-01-18 05:15:00	0.264	8
76	2014-01-18 07:45:00	0.124	2
77	2014-01-18 09:30:00	0.079	9
78	2014-01-18 15:00:00	0.377	2
79	2014-01-19 05:00:00	0.092	5
80	2014-01-19 06:15:00	0.048	0
81	2014-01-19 06:15:00	0.088	1
82	2014-01-19 07:45:00	0.106	9
83	2014-01-19 12:00:00	0.02	4
84	2014-01-19 14:00:00	0.12	2
85	2014-01-19 14:00:00	0.139	8
86	2014-01-19 15:15:00	0.357	3
87	2014-01-19 15:30:00	13.3	0
88	2014-01-19 17:15:00	0.184	2
89	2014-01-19 21:15:00	0.231	1
90	2014-01-20 11:30:00	1.025	6
91	2014-01-20 12:45:00	1.085	7
92	2014-01-21 01:15:00	0.118	2
93	2014-01-21 03:00:00	0.07	3
94	2014-01-21 12:00:00	0.144	7
95	2014-01-21 12:30:00	0.327	0
96	2014-01-21 23:00:00	0.485	6
97	2014-01-22 13:30:00	0.197	2
98	2014-01-22 14:30:00	0.181	8
99	2014-01-22 22:00:00	0.505	6
100	2014-01-23 00:45:00	0.574	9

Rows: 1-100 | Columns: 3