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 01:15:00	0.037	2
2	2014-01-01 02:30:00	0.08	5
3	2014-01-01 03:00:00	0.081	1
4	2014-01-01 05:00:00	1.489	3
5	2014-01-01 06:00:00	0.072	5
6	2014-01-01 07:15:00	2.306	9
7	2014-01-01 07:45:00	0.102	4
8	2014-01-01 10:45:00	0.097	8
9	2014-01-01 11:00:00	0.029	0
10	2014-01-01 11:00:00	0.506	6
11	2014-01-01 11:15:00	0.129	5
12	2014-01-01 13:00:00	0.622	4
13	2014-01-01 13:45:00	0.277	0
14	2014-01-01 15:30:00	0.235	9
15	2014-01-01 16:45:00	0.623	7
16	2014-01-01 17:00:00	1.35	5
17	2014-01-01 17:15:00	0.559	1
18	2014-01-01 19:15:00	0.375	1
19	2014-01-01 22:30:00	0.54	9
20	2014-01-02 00:30:00	0.358	2
21	2014-01-02 01:30:00	0.139	3
22	2014-01-02 02:45:00	0.055	3
23	2014-01-02 03:00:00	0.086	6
24	2014-01-02 03:30:00	0.044	1
25	2014-01-02 03:45:00	0.073	8
26	2014-01-02 04:45:00	0.1	7
27	2014-01-02 05:30:00	0.044	1
28	2014-01-02 06:45:00	0.048	1
29	2014-01-02 06:45:00	0.055	5
30	2014-01-02 10:15:00	0.082	1
31	2014-01-02 10:45:00	0.321	0
32	2014-01-02 11:15:00	0.305	0
33	2014-01-02 12:30:00	0.397	5
34	2014-01-02 13:45:00	0.358	0
35	2014-01-02 14:30:00	0.254	4
36	2014-01-02 15:30:00	0.115	0
37	2014-01-02 15:30:00	0.185	7
38	2014-01-02 16:00:00	0.524	8
39	2014-01-02 17:45:00	0.871	4
40	2014-01-02 19:30:00	1.038	9
41	2014-01-02 19:45:00	1.478	6
42	2014-01-02 20:15:00	1.776	8
43	2014-01-03 00:30:00	0.094	8
44	2014-01-03 00:45:00	0.313	6
45	2014-01-03 01:45:00	0.133	9
46	2014-01-03 02:45:00	0.06	6
47	2014-01-03 03:15:00	0.085	9
48	2014-01-03 04:30:00	0.066	3
49	2014-01-03 04:30:00	0.068	1
50	2014-01-03 05:45:00	0.067	8
51	2014-01-03 06:30:00	0.032	7
52	2014-01-03 07:45:00	0.084	9
53	2014-01-03 07:45:00	0.272	2
54	2014-01-03 08:30:00	0.071	0
55	2014-01-03 09:15:00	1.506	4
56	2014-01-03 10:30:00	0.074	9
57	2014-01-03 11:00:00	2.108	4
58	2014-01-03 12:15:00	0.103	8
59	2014-01-03 19:45:00	0.489	7
60	2014-01-03 21:30:00	0.672	7
61	2014-01-03 22:15:00	0.591	5
62	2014-01-03 22:15:00	1.938	1
63	2014-01-03 23:30:00	0.284	4
64	2014-01-04 01:15:00	0.131	6
65	2014-01-04 01:30:00	1.546	1
66	2014-01-04 01:45:00	0.361	6
67	2014-01-04 02:15:00	0.383	6
68	2014-01-04 02:45:00	0.185	5
69	2014-01-04 05:45:00	0.062	8
70	2014-01-04 06:00:00	0.267	5
71	2014-01-04 06:45:00	0.077	8
72	2014-01-04 07:30:00	0.068	2
73	2014-01-04 07:45:00	0.309	4
74	2014-01-04 10:00:00	0.153	8
75	2014-01-04 10:45:00	0.545	7
76	2014-01-04 11:45:00	1.268	8
77	2014-01-04 12:00:00	0.076	2
78	2014-01-04 13:30:00	1.36	8
79	2014-01-04 17:15:00	0.285	2
80	2014-01-04 17:15:00	0.447	9
81	2014-01-04 18:00:00	0.641	4
82	2014-01-04 22:30:00	0.827	4
83	2014-01-04 23:45:00	0.323	0
84	2014-01-05 02:15:00	0.305	6
85	2014-01-05 04:00:00	0.111	9
86	2014-01-05 06:30:00	0.075	9
87	2014-01-05 08:00:00	0.09	4
88	2014-01-05 08:45:00	0.16	9
89	2014-01-05 10:00:00	0.281	3
90	2014-01-05 10:15:00	0.58	6
91	2014-01-05 11:30:00	1.132	6
92	2014-01-05 17:30:00	0.625	4
93	2014-01-05 19:45:00	0.537	4
94	2014-01-05 19:45:00	0.546	1
95	2014-01-05 23:30:00	0.539	9
96	2014-01-06 01:15:00	0.085	0
97	2014-01-06 02:45:00	0.087	8
98	2014-01-06 05:00:00	0.069	3
99	2014-01-06 07:45:00	0.027	3
100	2014-01-06 07:45:00	0.53	2

Rows: 1-100 | Columns: 3