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verticapy.datasets.load_pop_growth

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

Ingests the population growth dataset into the Vertica database. This dataset is ideal for time series and geospatial 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 pop growth vDataFrame.

Examples

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

from verticapy.datasets import load_pop_growth

load_pop_growth()

	123 year Integer	Abc continent Varchar(100)	Abc country Varchar(100)	Abc city Varchar(100)	123 population Float(22)	🌎 lat Float(22)	🌎 lon Float(22)
1	1500	Asia	China	Beijing	672.0	39.9075	116.39723
2	1500	Asia	China	Guangzhou	150.0	23.11667	113.25
3	1500	Asia	China	Hangzhou	250.0	30.25	120.16
4	1500	Asia	China	Nanjing	147.0	32.05	118.766667
5	1500	Asia	India	Cuttack	140.0	20.46497	85.87927
6	1500	Asia	India	Gauda	200.0	24.866667	88.133333
7	1500	Asia	India	Vijayanagar	500.0	15.335	76.462
8	1500	Europe	France	Paris	185.0	48.85341	2.3488
9	1500	Europe	Turkey	Istanbul	200.0	41.005275	28.9497
10	1500	Middle East	Egypt	Cairo	400.0	30.04	31.24
11	1500	Middle East	Iran	Tabriz	250.0	38.08	46.2919
12	1500	Middle East	Morocco	Fez	130.0	34.033333	-5.01
13	1501	Asia	China	Beijing	672.36	39.9075	116.39723
14	1501	Asia	China	Guangzhou	150.2	23.11667	113.25
15	1501	Asia	China	Hangzhou	250.2	30.25	120.16
16	1501	Asia	China	Nanjing	147.000866666667	32.05	118.766667
17	1501	Asia	India	Cuttack	139.0	20.46497	85.87927
18	1501	Asia	India	Gauda	200.0	24.866667	88.133333
19	1501	Asia	India	Vijayanagar	499.6	15.335	76.462
20	1501	Europe	France	Paris	185.5	48.85341	2.3488
21	1501	Europe	Turkey	Istanbul	205.0	41.005275	28.9497
22	1501	Middle East	Egypt	Cairo	399.2	30.04	31.24
23	1501	Middle East	Iran	Tabriz	248.0	38.08	46.2919
24	1501	Middle East	Morocco	Fez	130.076923076923	34.033333	-5.01
25	1502	Asia	China	Beijing	672.72	39.9075	116.39723
26	1502	Asia	China	Guangzhou	150.4	23.11667	113.25
27	1502	Asia	China	Hangzhou	250.4	30.25	120.16
28	1502	Asia	China	Nanjing	147.001733333333	32.05	118.766667
29	1502	Asia	India	Cuttack	138.0	20.46497	85.87927
30	1502	Asia	India	Gauda	200.0	24.866667	88.133333
31	1502	Asia	India	Vijayanagar	499.2	15.335	76.462
32	1502	Europe	France	Paris	186.0	48.85341	2.3488
33	1502	Europe	Turkey	Istanbul	210.0	41.005275	28.9497
34	1502	Middle East	Egypt	Cairo	398.4	30.04	31.24
35	1502	Middle East	Iran	Tabriz	246.0	38.08	46.2919
36	1502	Middle East	Morocco	Fez	130.153846153846	34.033333	-5.01
37	1503	Asia	China	Beijing	673.08	39.9075	116.39723
38	1503	Asia	China	Guangzhou	150.6	23.11667	113.25
39	1503	Asia	China	Hangzhou	250.6	30.25	120.16
40	1503	Asia	China	Nanjing	147.0026	32.05	118.766667
41	1503	Asia	India	Cuttack	137.0	20.46497	85.87927
42	1503	Asia	India	Gauda	200.0	24.866667	88.133333
43	1503	Asia	India	Vijayanagar	498.8	15.335	76.462
44	1503	Europe	France	Paris	186.5	48.85341	2.3488
45	1503	Europe	Turkey	Istanbul	215.0	41.005275	28.9497
46	1503	Middle East	Egypt	Cairo	397.6	30.04	31.24
47	1503	Middle East	Iran	Tabriz	244.0	38.08	46.2919
48	1503	Middle East	Morocco	Fez	130.230769230769	34.033333	-5.01
49	1504	Asia	China	Beijing	673.44	39.9075	116.39723
50	1504	Asia	China	Guangzhou	150.8	23.11667	113.25
51	1504	Asia	China	Hangzhou	250.8	30.25	120.16
52	1504	Asia	China	Nanjing	147.003466666667	32.05	118.766667
53	1504	Asia	India	Cuttack	136.0	20.46497	85.87927
54	1504	Asia	India	Gauda	200.0	24.866667	88.133333
55	1504	Asia	India	Vijayanagar	498.4	15.335	76.462
56	1504	Europe	France	Paris	187.0	48.85341	2.3488
57	1504	Europe	Turkey	Istanbul	220.0	41.005275	28.9497
58	1504	Middle East	Egypt	Cairo	396.8	30.04	31.24
59	1504	Middle East	Iran	Tabriz	242.0	38.08	46.2919
60	1504	Middle East	Morocco	Fez	130.307692307692	34.033333	-5.01
61	1505	Asia	China	Beijing	673.8	39.9075	116.39723
62	1505	Asia	China	Guangzhou	151.0	23.11667	113.25
63	1505	Asia	China	Hangzhou	251.0	30.25	120.16
64	1505	Asia	China	Nanjing	147.004333333333	32.05	118.766667
65	1505	Asia	India	Cuttack	135.0	20.46497	85.87927
66	1505	Asia	India	Gauda	200.0	24.866667	88.133333
67	1505	Asia	India	Vijayanagar	498.0	15.335	76.462
68	1505	Europe	France	Paris	187.5	48.85341	2.3488
69	1505	Europe	Turkey	Istanbul	225.0	41.005275	28.9497
70	1505	Middle East	Egypt	Cairo	396.0	30.04	31.24
71	1505	Middle East	Iran	Tabriz	240.0	38.08	46.2919
72	1505	Middle East	Morocco	Fez	130.384615384615	34.033333	-5.01
73	1506	Asia	China	Beijing	674.16	39.9075	116.39723
74	1506	Asia	China	Guangzhou	151.2	23.11667	113.25
75	1506	Asia	China	Hangzhou	251.2	30.25	120.16
76	1506	Asia	China	Nanjing	147.0052	32.05	118.766667
77	1506	Asia	India	Cuttack	134.0	20.46497	85.87927
78	1506	Asia	India	Gauda	200.0	24.866667	88.133333
79	1506	Asia	India	Vijayanagar	497.6	15.335	76.462
80	1506	Europe	France	Paris	188.0	48.85341	2.3488
81	1506	Europe	Turkey	Istanbul	230.0	41.005275	28.9497
82	1506	Middle East	Egypt	Cairo	395.2	30.04	31.24
83	1506	Middle East	Iran	Tabriz	238.0	38.08	46.2919
84	1506	Middle East	Morocco	Fez	130.461538461538	34.033333	-5.01
85	1507	Asia	China	Beijing	674.52	39.9075	116.39723
86	1507	Asia	China	Guangzhou	151.4	23.11667	113.25
87	1507	Asia	China	Hangzhou	251.4	30.25	120.16
88	1507	Asia	China	Nanjing	147.006066666667	32.05	118.766667
89	1507	Asia	India	Cuttack	133.0	20.46497	85.87927
90	1507	Asia	India	Gauda	200.0	24.866667	88.133333
91	1507	Asia	India	Vijayanagar	497.2	15.335	76.462
92	1507	Europe	France	Paris	188.5	48.85341	2.3488
93	1507	Europe	Turkey	Edirne	130.64	41.681808	26.562269
94	1507	Europe	Turkey	Istanbul	235.0	41.005275	28.9497
95	1507	Middle East	Egypt	Cairo	394.4	30.04	31.24
96	1507	Middle East	Iran	Tabriz	236.0	38.08	46.2919
97	1508	Asia	China	Beijing	674.88	39.9075	116.39723
98	1508	Asia	China	Guangzhou	151.6	23.11667	113.25
99	1508	Asia	China	Hangzhou	251.6	30.25	120.16
100	1508	Asia	China	Nanjing	147.006933333333	32.05	118.766667

Rows: 1-100 | Columns: 7