verticapy.sql.geo.coordinate_converter#

verticapy.sql.geo.coordinate_converter(vdf: str | vDataFrame, x: str, y: str, x0: float = 0.0, earth_radius: int | float | Decimal = 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	Abidjan
2	Abu Dhabi
3	Abuja
4	Accra
5	Addis Ababa
6	Algiers
7	Amman
8	Amsterdam
9	Andorra
10	Ankara
11	Antananarivo
12	Apia
13	Ashgabat
14	Asmara
15	Astana
16	Asuncion
17	Athens
18	Baghdad
19	Baku
20	Bamako
21	Bandar Seri Begawan
22	Bangkok
23	Bangui
24	Banjul
25	Basseterre
26	Beijing
27	Beirut
28	Belgrade
29	Belmopan
30	Berlin
31	Bern
32	Bishkek
33	Bissau
34	Bloemfontein
35	Bogota
36	Brasilia
37	Bratislava
38	Brazzaville
39	Bridgetown
40	Brussels
41	Bucharest
42	Budapest
43	Buenos Aires
44	Bujumbura
45	Cairo
46	Canberra
47	Cape Town
48	Caracas
49	Castries
50	Chisinau
51	Colombo
52	Conakry
53	Cotonou
54	Dakar
55	Damascus
56	Dar es Salaam
57	Dhaka
58	Dili
59	Djibouti
60	Doha
61	Dublin
62	Dushanbe
63	Freetown
64	Funafuti
65	Gaborone
66	Georgetown
67	Guatemala
68	Hanoi
69	Harare
70	Hargeysa
71	Havana
72	Helsinki
73	Honiara
74	Islamabad
75	Jakarta
76	Jerusalem
77	Johannesburg
78	Juba
79	Kabul
80	Kampala
81	Kathmandu
82	Khartoum
83	Kiev
84	Kigali
85	Kingston
86	Kingstown
87	Kinshasa
88	Kuala Lumpur
89	Kuwait
90	København
91	La Paz
92	Libreville
93	Lilongwe
94	Lima
95	Lisbon
96	Ljubljana
97	Lome
98	London
99	Luanda
100	Lusaka

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	Abidjan	...		5.32194282609856
2	Abu Dhabi	...		24.4666835723799
3	Abuja	...		9.0852790077542
4	Accra	...		5.55198046444593
5	Addis Ababa	...		9.03525622129575
6	Algiers	...		36.7650106566281
7	Amman	...		31.9519711058275
8	Amsterdam	...		52.3519145466644
9	Andorra	...		42.5000014435459
10	Ankara	...		39.9291844440755
11	Antananarivo	...		-18.9146914920322
12	Apia	...		-13.8415450424484
13	Ashgabat	...		37.949994933111
14	Asmara	...		15.3333392526819
15	Astana	...		51.1811253042576
16	Asuncion	...		-25.2944571170577
17	Athens	...		37.9852720905523
18	Baghdad	...		33.3405943561586
19	Baku	...		40.397217891343
20	Bamako	...		12.6519605263233

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	Abidjan	...		591.773042154565
2	Abu Dhabi	...		2720.57108506641
3	Abuja	...		1010.23693281258
4	Accra	...		617.352060476089
5	Addis Ababa	...		1004.67465274177
6	Algiers	...		4088.08266305019
7	Amman	...		3552.89708326154
8	Amsterdam	...		5821.26729771856
9	Andorra	...		4725.78454290873
10	Ankara	...		4439.92273523603
11	Antananarivo	...		-2103.21773296097
12	Apia	...		-1539.10958564241
13	Ashgabat	...		4219.84690274865
14	Asmara	...		1704.9895334181
15	Astana	...		5691.08147379289
16	Asuncion	...		-2812.61530364517
17	Athens	...		4223.76954368256
18	Baghdad	...		3707.30494371905
19	Baku	...		4491.96568007214
20	Bamako	...		1406.83382263437

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	Abidjan	...		5.32194282609856
2	Abu Dhabi	...		24.4666835723799
3	Abuja	...		9.0852790077542
4	Accra	...		5.55198046444593
5	Addis Ababa	...		9.03525622129575
6	Algiers	...		36.7650106566281
7	Amman	...		31.9519711058275
8	Amsterdam	...		52.3519145466644
9	Andorra	...		42.5000014435459
10	Ankara	...		39.9291844440755
11	Antananarivo	...		-18.9146914920322
12	Apia	...		-13.8415450424484
13	Ashgabat	...		37.949994933111
14	Asmara	...		15.3333392526819
15	Astana	...		51.1811253042576
16	Asuncion	...		-25.2944571170577
17	Athens	...		37.9852720905523
18	Baghdad	...		33.3405943561586
19	Baku	...		40.397217891343
20	Bamako	...		12.6519605263233

Note

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