verticapy.vDataFrame.to_geopandas#

vDataFrame.to_geopandas(geometry: str) → GeoDataFrame#

Converts the vDataFrame to a Geopandas DataFrame.

Warning

The data will be loaded in memory.

Parameters#

geometry: str: Geometry object used to create the GeoDataFrame. It can also be a Geography object, which will be casted to Geometry.

Returns#

geopandas.GeoDataFrame: The geopandas.GeoDataFrame of the current vDataFrame relation.

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 World dataset.

import verticapy.datasets as vpd

data = vpd.load_world()

	123 pop_est Integer	Abc continent Varchar(32)	Abc country Varchar(82)	Abc Long varchar(2411724)
1	140	Seven seas (open ocean)	Fr. S. Antarctic Lands
2	2931	South America	Falkland Is.
3	4050	Antarctica	Antarctica
4	57713	North America	Greenland
5	265100	Asia	N. Cyprus
6	279070	Oceania	New Caledonia
7	282814	Oceania	Vanuatu
8	329988	North America	Bahamas
9	339747	Europe	Iceland
10	360346	North America	Belize
11	443593	Asia	Brunei
12	591919	South America	Suriname
13	594130	Europe	Luxembourg
14	603253	Africa	W. Sahara
15	642550	Europe	Montenegro
16	647581	Oceania	Solomon Is.
17	737718	South America	Guyana
18	758288	Asia	Bhutan
19	778358	Africa	Eq. Guinea
20	865267	Africa	Djibouti
21	920938	Oceania	Fiji
22	1218208	North America	Trinidad and Tobago
23	1221549	Asia	Cyprus
24	1251581	Europe	Estonia
25	1291358	Asia	Timor-Leste
26	1467152	Africa	eSwatini
27	1772255	Africa	Gabon
28	1792338	Africa	Guinea-Bissau
29	1895250	Europe	Kosovo
30	1944643	Europe	Latvia
31	1958042	Africa	Lesotho
32	1972126	Europe	Slovenia
33	2051363	Africa	Gambia
34	2103721	Europe	Macedonia
35	2214858	Africa	Botswana
36	2314307	Asia	Qatar
37	2484780	Africa	Namibia
38	2823859	Europe	Lithuania
39	2875422	Asia	Kuwait
40	2990561	North America	Jamaica
41	3045191	Asia	Armenia
42	3047987	Europe	Albania
43	3068243	Asia	Mongolia
44	3351827	North America	Puerto Rico
45	3360148	South America	Uruguay
46	3424386	Asia	Oman
47	3474121	Europe	Moldova
48	3500000	Africa	Somaliland
49	3753142	North America	Panama
50	3758571	Africa	Mauritania
51	3856181	Europe	Bosnia and Herz.
52	4292095	Europe	Croatia
53	4510327	Oceania	New Zealand
54	4543126	Asia	Palestine
55	4689021	Africa	Liberia
56	4926330	Asia	Georgia
57	4930258	North America	Costa Rica
58	4954674	Africa	Congo
59	5011102	Europe	Ireland
60	5320045	Europe	Norway
61	5351277	Asia	Turkmenistan
62	5445829	Europe	Slovakia
63	5491218	Europe	Finland
64	5605948	Europe	Denmark
65	5625118	Africa	Central African Rep.
66	5789122	Asia	Kyrgyzstan
67	5918919	Africa	Eritrea
68	6025951	North America	Nicaragua
69	6072475	Asia	United Arab Emirates
70	6163195	Africa	Sierra Leone
71	6172011	North America	El Salvador
72	6229794	Asia	Lebanon
73	6653210	Africa	Libya
74	6909701	Oceania	Papua New Guinea
75	6943739	South America	Paraguay
76	7101510	Europe	Bulgaria
77	7111024	Europe	Serbia
78	7126706	Asia	Laos
79	7531386	Africa	Somalia
80	7965055	Africa	Togo
81	8236303	Europe	Switzerland
82	8299706	Asia	Israel
83	8468555	Asia	Tajikistan
84	8754413	Europe	Austria
85	9038741	North America	Honduras
86	9549747	Europe	Belarus
87	9850845	Europe	Hungary
88	9960487	Europe	Sweden
89	9961396	Asia	Azerbaijan
90	10248069	Asia	Jordan
91	10646714	North America	Haiti
92	10674723	Europe	Czechia
93	10734247	North America	Dominican Rep.
94	10768477	Europe	Greece
95	10839514	Europe	Portugal
96	11038805	Africa	Benin
97	11138234	South America	Bolivia
98	11147407	North America	Cuba
99	11403800	Africa	Tunisia
100	11466756	Africa	Burundi

Rows: 1-100 | Columns: 4

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 convert the vDataFrame to a Geopandas DataFrame.

data.to_geopandas(geometry = "geometry")

	pop_est	continent	country	geometry
0	140	Seven seas (open ocean)	Fr. S. Antarctic Lands	POLYGON ((68.93500 -48.62500, 69.58000 -48.94000, 70.52500 -49.06500, 70.56000 -49.25500, 70.28000 -49.71000, 68.74500 -49.77500, 68.72000 -49.24250, 68.86750 -48.83000, 68.93500 -48.62500))
...	...	...	...	...
176	1379302771	Asia	China	MULTIPOLYGON (((109.47521 18.19770, 108.65521 18.50768, 108.62622 19.36789, 109.11906 19.82104, 110.21160 20.10125, 110.78655 20.07753, 111.01005 19.69593, 110.57065 19.25588, 110.33919 18.67840, 109.47521 18.19770)), ((80.25999 42.35000, 80.18015 42.92007, 80.86621 43.18036, 79.96611 44.91752, 81.94707 45.31703, 82.45893 45.53965, 83.18048 47.33003, 85.16429 47.00096, 85.72048 47.45297, 85.76823 48.45575, 86.59878 48.54918, 87.35997 49.21498, 87.75126 49.29720, 88.01383 48.59946, 88.85430 48.06908, 90.28083 47.69355, 90.97081 46.88815, 90.58577 45.71972, 90.94554 45.28607, 92.13389 45.11508, 93.48073 44.97547, 94.68893 44.35233, 95.30688 44.24133, 95.76245 43.31945, 96.34940 42.72564, 97.45176 42.74889, 99.51582 42.52469, 100.84587 42.66380, 101.83304 42.51487, 103.31228 41.90747, 104.52228 41.90835, 104.96499 41.59741, 106.12932 42.13433, 107.74477 42.48152, 109.24360 42.51945, 110.41210 42.87123, 111.12968 43.40683, 111.82959 43.74312, 111.66774 44.07318, 111.34838 44.45744, 111.87331 45.10208, 112.43606 45.01165, 113.46391 44.80889, 114.46033 45.33982, 115.98510 45.72724, 116.71787 46.38820, 117.42170 46.67273, 118.87433 46.80541, 119.66327 46.69268, 119.77282 47.04806, 118.86657 47.74706, 118.06414 48.06673, 117.29551 47.69771, 116.30895 47.85341, 115.74284 47.72654, 115.48528 48.13538, 116.19180 49.13460, 116.67880 49.88853, 117.87924 49.51098, 119.28846 50.14288, 119.27939 50.58292, 120.18208 51.64355, 120.73820 51.96411, 120.72579 52.51623, 120.17709 52.75389, 121.00308 53.25140, 122.24575 53.43173, 123.57147 53.45880, 125.06821 53.16104, 125.94635 52.79280, 126.56440 51.78426, 126.93916 51.35389, 127.28746 50.73980, 127.65740 49.76027, 129.39782 49.44060, 130.58229 48.72969, 130.98726 47.79013, 132.50669 47.78896, 133.37360 48.18344, 135.02631 48.47823, 134.50081 47.57845, 134.11235 47.21248, 133.76964 46.11693, 133.09712 45.14409, 131.88345 45.32116, 131.02519 44.96796, 131.28856 44.11152, 131.14469 42.92999, 130.63387 42.90301, 130.64000 42.39502, 129.99427 42.98539, 129.59667 42.42498, 128.05222 41.99428, 128.20843 41.46677, 127.34378 41.50315, 126.86908 41.81657, 126.18205 41.10734, 125.07994 40.56982, 124.26562 39.92849, 122.86757 39.63779, 122.13139 39.17045, 121.05455 38.89747, 121.58599 39.36085, 121.37676 39.75026, 122.16860 40.42244, 121.64036 40.94639, 120.76863 40.59339, 119.63960 39.89806, 119.02346 39.25233, 118.04275 39.20427, 117.53270 38.73764, 118.05970 38.06148, 118.87815 37.89733, 118.91164 37.44846, 119.70280 37.15639, 120.82346 37.87043, 121.71126 37.48112, 122.35794 37.45448, 122.51999 36.93061, 121.10416 36.65133, 120.63701 36.11144, 119.66456 35.60979, 119.15121 34.90986, 120.22752 34.36033, 120.62037 33.37672, 121.22901 32.46032, 121.90815 31.69217, 121.89192 30.94935, 121.26426 30.67627, 121.50352 30.14291, 122.09211 29.83252, 121.93843 29.01802, 121.68444 28.22551, 121.12566 28.13567, 120.39547 27.05321, 119.58550 25.74078, 118.65687 24.54739, 117.28161 23.62450, 115.89074 22.78287, 114.76383 22.66807, 114.15255 22.22376, 113.80678 22.54834, 113.24108 22.05137, 111.84359 21.55049, 110.78547 21.39714, 110.44404 20.34103, 109.88986 20.28246, 109.62766 21.00823, 109.86449 21.39505, 108.52281 21.71521, 108.05018 21.55238, 107.04342 21.81190, 106.56727 22.21820, 106.72540 22.79427, 105.81125 22.97689, 105.32921 23.35206, 104.47686 22.81915, 103.50451 22.70376, 102.70699 22.70880, 102.17044 22.46475, 101.65202 22.31820, 101.80312 21.17437, 101.27003 21.20165, 101.18001 21.43657, 101.15003 21.84998, 100.41654 21.55884, 99.98349 21.74294, 99.24090 22.11831, 99.53199 22.94904, 98.89875 23.14272, 98.66026 24.06329, 97.60472 23.89740, 97.72461 25.08364, 98.67184 25.91870, 98.71209 26.74354, 98.68269 27.50881, 98.24623 27.74722, 97.91199 28.33595, 97.32711 28.26158, 96.24883 28.41103, 96.58659 28.83098, 96.11768 29.45280, 95.40480 29.03172, 94.56599 29.27744, 93.41335 28.64063, 92.50312 27.89688, 91.69666 27.77174, 91.25885 28.04061, 90.73051 28.06495, 90.01583 28.29644, 89.47581 28.04276, 88.81425 27.29932, 88.73033 28.08686, 88.12044 27.87654, 86.95452 27.97426, 85.82332 28.20358, 85.01164 28.64277, 84.23458 28.83989, 83.89899 29.32023, 83.33712 29.46373, 82.32751 30.11527, 81.52580 30.42272, 81.11126 30.18348, 79.72137 30.88271, 78.73889 31.51591, 78.45845 32.61816, 79.17613 32.48378, 79.20889 32.99439, 78.81109 33.50620, 78.91227 34.32194, 77.83745 35.49401, 76.19285 35.89840, 75.89690 36.66681, 75.15803 37.13303, 74.98000 37.41999, 74.82999 37.99001, 74.86482 38.37885, 74.25751 38.60651, 73.92885 38.50582, 73.67538 39.43124, 73.96001 39.66001, 73.82224 39.89397, 74.77686 40.36643, 75.46783 40.56207, 76.52637 40.42795, 76.90448 41.06649, 78.18720 41.18532, 78.54366 41.58224, 80.11943 42.12394, 80.25999 42.35000)))

Warning

Exporting to an in-memory object can take time if the data is massive. It is recommended to downsample the data before using such a function.