verticapy.vDataFrame.to_shp#

vDataFrame.to_shp(name: str, path: str, usecols: str | list[str] | None = None, overwrite: bool = True, shape: Literal['Point', 'Polygon', 'Linestring', 'Multipoint', 'Multipolygon', 'Multilinestring'] = 'Polygon') → vDataFrame#

Creates a SHP file of the current vDataFrame relation. For the moment, files will be exported in the Vertica server.

Parameters#

name: str: Name of the SHP file.
path: str: Absolute path where the SHP file is created.
usecols: list, optional: vDataColumn to select from the final vDataFrame relation. If empty, all vDataColumn are selected.
overwrite: bool, optional: If set to True, the function overwrites the index (if an index exists).
shape: str, optional: Must be one of the following spatial classes: Point, Polygon, Linestring, Multipoint, Multipolygon, Multilinestring. Polygons and Multipolygons always have a clockwise orientation.

Returns#

vDataFrame: self

Examples#

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the Cities dataset.

import verticapy.datasets as vpd

data = 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 create the SHP file of the current vDataFrame.

data.to_shp(
    name = "cities",
    path = "/home/dbadmin/",
    shape = "Point",
)

Note

It will create “cities.shp” file at provided path.