verticapy.sql.geo.coordinate_converter¶

verticapy.sql.geo.coordinate_converter(vdf: Annotated[str | vDataFrame, ''], x: str, y: str, x0: float = 0.0, earth_radius: Annotated[int | float | Decimal, 'Python Numbers'] = 6371, reverse: bool = False) → vDataFrame¶

Converts between geographic coordinates (latitude and longitude) and Euclidean coordinates (x,y).

Parameters¶

vdf: SQLRelation: Input vDataFrame.
x: str: vDataColumn used as the abscissa (longitude).
y: str: vDataColumn used as the ordinate (latitude).
x0: float, optional: The initial abscissa.
earth_radius: PythonNumber, optional: Earth radius in km.
reverse: bool, optional: If set to True, the Euclidean coordinates are converted to latitude and longitude.

Returns¶

vDataFrame: result of the transformation.

Examples¶

For this example, we will use the Cities dataset.

import verticapy.datasets as vpd

cities = vpd.load_cities()

	Abc city Varchar(82)	Abc Long varchar(2411724)
1	Abu Dhabi
2	Amsterdam
3	Apia
4	Ashgabat
5	Bangui
6	Budapest
7	Bujumbura
8	Castries
9	Colombo
10	Conakry
11	Cotonou
12	Dakar
13	Dublin
14	Georgetown
15	Havana
16	Kuala Lumpur
17	København
18	Lilongwe
19	Ljubljana
20	London
21	Monrovia
22	Oslo
23	Panama City
24	Paris
25	Phnom Penh
26	Podgorica
27	Pretoria
28	Rangoon
29	San Marino
30	San Salvador
31	Seoul
32	Suva
33	Tegucigalpa
34	Tehran
35	Thimphu
36	Vatican City
37	Warsaw
38	Yaounde
39	Zagreb
40	Andorra
41	Athens
42	Banjul
43	Basseterre
44	Belgrade
45	Bloemfontein
46	Bogota
47	Brasilia
48	Accra
49	Addis Ababa
50	Ankara
51	Antananarivo
52	Asmara
53	Astana
54	Asuncion
55	Bamako
56	Bangkok
57	Beijing
58	Berlin
59	Brussels
60	Buenos Aires
61	Chisinau
62	Damascus
63	Dar es Salaam
64	Dhaka
65	Djibouti
66	Doha
67	Jakarta
68	Jerusalem
69	Kampala
70	Kinshasa
71	Kuwait
72	La Paz
73	Lisbon
74	Luxembourg
75	Madrid
76	Manila
77	Maputo
78	Mbabane
79	Melekeok
80	Minsk
81	Nairobi
82	Naypyidaw
83	New Delhi
84	Nicosia
85	Nouakchott
86	Palikir
87	Port-au-Prince
88	Praia
89	Quito
90	Rome
91	Saint George's
92	San Jose
93	Sarajevo
94	Taipei
95	Tbilisi
96	Tokyo
97	Tripoli
98	Valletta
99	Vienna
100	Vilnius

Rows: 1-100 | Columns: 2

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

Let’s extract the latitude and longitude.

cities["lat"] = "ST_X(geometry)"
cities["lon"] = "ST_Y(geometry)"
display(cities)

	Abc city Varchar(82) 100%	...	🌎 Geometry(1048576) 100%	🌎 lon Float(22) 100%
1	Andorra	...		42.5000014435459
2	Athens	...		37.9852720905523
3	Banjul	...		13.4538764603159
4	Basseterre	...		17.3020304554894
5	Belgrade	...		44.8205913044467
6	Bloemfontein	...		-29.1199938773787
7	Bogota	...		4.59836942114782
8	Brasilia	...		-15.781394372879
9	Bridgetown	...		13.1020025827511
10	Cairo	...		30.0519062051037
11	Cape Town	...		-33.9180651086288
12	Dili	...		-8.55938840854645
13	Funafuti	...		-8.51665199904107
14	Gaborone	...		-24.6463134574389
15	Guatemala	...		14.6230805214482
16	Harare	...		-17.8158438357778
17	Helsinki	...		60.1775092325681
18	Johannesburg	...		-26.1680988813841
19	Juba	...		4.82997519827796
20	Khartoum	...		15.5900240842777

Let’s leverage the coordinate_converter function to calculate Euclidean distances. We’ll project the latitude and longitude into x, y coordinates.

from verticapy.sql.geo import coordinate_converter

convert_xy = coordinate_converter(cities, "lon", "lat")
display(convert_xy)

	Abc city Varchar(82) 100%	...	🌎 Geometry(1048576) 100%	🌎 lon Float(22) 100%
1	Andorra	...		4725.78454290873
2	Athens	...		4223.76954368256
3	Banjul	...		1496.00280608979
4	Basseterre	...		1923.89800730006
5	Belgrade	...		4983.8223622637
6	Bloemfontein	...		-3237.99558308512
7	Bogota	...		511.315350469114
8	Brasilia	...		-1754.81098964113
9	Bridgetown	...		1456.87621608583
10	Cairo	...		3341.61950600567
11	Cape Town	...		-3771.51676167934
12	Dili	...		-951.760566210609
13	Funafuti	...		-947.008494290607
14	Gaborone	...		-2740.54501695872
15	Guatemala	...		1626.0123658999
16	Harare	...		-1981.03144843023
17	Abu Dhabi	...		2720.57108506641
18	Amsterdam	...		5821.26729771856
19	Apia	...		-1539.10958564241
20	Ashgabat	...		4219.84690274865

We can effortlessly reverse the operation.

convert_reverse_xy = coordinate_converter(convert_xy, "lon", "lat", reverse = True)
display(convert_reverse_xy)

	Abc city Varchar(82) 100%	...	🌎 Geometry(1048576) 100%	🌎 lon Float(22) 100%
1	Andorra	...		42.5000014435459
2	Athens	...		37.9852720905523
3	Banjul	...		13.4538764603159
4	Basseterre	...		17.3020304554894
5	Belgrade	...		44.8205913044467
6	Bloemfontein	...		-29.1199938773787
7	Bogota	...		4.59836942114782
8	Brasilia	...		-15.781394372879
9	Abu Dhabi	...		24.4666835723799
10	Amsterdam	...		52.3519145466644
11	Apia	...		-13.8415450424484
12	Ashgabat	...		37.949994933111
13	Bangui	...		4.36664430634909
14	Budapest	...		47.5019521849913
15	Bujumbura	...		-3.37608722037464
16	Castries	...		14.0019734893303
17	Colombo	...		6.93196575818212
18	Conakry	...		9.53346870502179
19	Cotonou	...		6.40195442278247
20	Dakar	...		14.7177775836233

Note

This function can be employed to operate on the Euclidean plane instead of a sphere, significantly improving computation speed.