verticapy.vDataFrame.to_shp¶

vDataFrame.to_shp(name: str, path: str, usecols: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | 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	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 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.