Integrating with GeoPandas#

As of version 0.4.0, VerticaPy features GeoPandas integration. This allows you to easily export a vDataFrame as a GeoPandas DataFrame, giving you more control over geospatial data.

This example demonstrates the advantages of GeoPandas integration with the ‘world’ dataset.

[1]:

from verticapy.datasets import load_world
world = load_world()
display(world)

	123 pop_est Int	Abc continent Varchar(32)	Abc country Varchar(82)	🌎 Geometry(1048576)
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

The ‘apply’ function of the VerticaPy stats module allows you to apply any Vertica function to the data. Let’s compute the area of each country.

[2]:

import verticapy.stats as st
world["geography"] = st.apply("stv_geography", world["geometry"])
world["geography"].astype("geography")
world["area"] = st.apply("st_area", world["geography"])
display(world)

	123 pop_est Int	Abc continent Varchar(32)	Abc country Varchar(82)	🌎 Geometry(1048576)	🌎 Geography	123 area Float
1	140	Seven seas (open ocean)	Fr. S. Antarctic Lands			11565103230.1973
2	2931	South America	Falkland Is.			16301980841.1126
3	4050	Antarctica	Antarctica			12236252243917.1
4	57713	North America	Greenland			2189751414857.83
5	265100	Asia	N. Cyprus			3786381407.62268
6	279070	Oceania	New Caledonia			23282206866.0818
7	282814	Oceania	Vanuatu			7516451865.1763
8	329988	North America	Bahamas			15615897741.1302
9	339747	Europe	Iceland			107031853684.148
10	360346	North America	Belize			22118506248.1485
11	443593	Asia	Brunei			10747411137.2812
12	591919	South America	Suriname			144905905345.091
13	594130	Europe	Luxembourg			2408816376.51917
14	603253	Africa	W. Sahara			96485945529.7125
15	642550	Europe	Montenegro			13420753080.0055
16	647581	Oceania	Solomon Is.			24831146940.7946
17	737718	South America	Guyana			210721444770.898
18	758288	Asia	Bhutan			39441951914.4718
19	778358	Africa	Eq. Guinea			27242029434.5546
20	865267	Africa	Djibouti			21966242368.5798
21	920938	Oceania	Fiji			19353579665.1023
22	1218208	North America	Trinidad and Tobago			7769152795.90834
23	1221549	Asia	Cyprus			6207615256.1115
24	1251581	Europe	Estonia			44441790595.9614
25	1291358	Asia	Timor-Leste			14776389256.2834
26	1467152	Africa	eSwatini			18151531542.9276
27	1772255	Africa	Gabon			270683290581.716
28	1792338	Africa	Guinea-Bissau			36333129902.2658
29	1895250	Europe	Kosovo			11211660926.8689
30	1944643	Europe	Latvia			63610085256.8848
31	1958042	Africa	Lesotho			27538833464.2252
32	1972126	Europe	Slovenia			19070599769.6852
33	2051363	Africa	Gambia			14084033590.0507
34	2103721	Europe	Macedonia			25026416110.0201
35	2214858	Africa	Botswana			593439013734.129
36	2314307	Asia	Qatar			11350868333.6489
37	2484780	Africa	Namibia			827008137847.344
38	2823859	Europe	Lithuania			63539126862.9918
39	2875422	Asia	Kuwait			16673251066.4255
40	2990561	North America	Jamaica			12500311840.4183
41	3045191	Asia	Armenia			28624801795.8552
42	3047987	Europe	Albania			29655575543.2255
43	3068243	Asia	Mongolia			1540264832849.55
44	3351827	North America	Puerto Rico			9253937792.64593
45	3360148	South America	Uruguay			176951396583.201
46	3424386	Asia	Oman			310171982406.478
47	3474121	Europe	Moldova			32231982684.4869
48	3500000	Africa	Somaliland			168036707875.77
49	3753142	North America	Panama			75581062586.3847
50	3758571	Africa	Mauritania			1057124493617.98
51	3856181	Europe	Bosnia and Herz.			50502366239.6244
52	4292095	Europe	Croatia			57402973305.489
53	4510327	Oceania	New Zealand			277235027306.03
54	4543126	Asia	Palestine			5040777637.52613
55	4689021	Africa	Liberia			98630883299.1132
56	4926330	Asia	Georgia			68940478573.1465
57	4930258	North America	Costa Rica			54052091511.1307
58	4954674	Africa	Congo			341198516478.001
59	5011102	Europe	Ireland			58217540791.3509
60	5320045	Europe	Norway			395342753995.479
61	5351277	Asia	Turkmenistan			480403612700.594
62	5445829	Europe	Slovakia			46922595108.0112
63	5491218	Europe	Finland			339068193512.288
64	5605948	Europe	Denmark			42557150895.5181
65	5625118	Africa	Central African Rep.			624537721851.14
66	5789122	Asia	Kyrgyzstan			195599523413.606
67	5918919	Africa	Eritrea			119741273687.089
68	6025951	North America	Nicaragua			130035380109.286
69	6072475	Asia	United Arab Emirates			80064396581.0864
70	6163195	Africa	Sierra Leone			76298870280.0899
71	6172011	North America	El Salvador			20971345487.6227
72	6229794	Asia	Lebanon			10102103572.6877
73	6653210	Africa	Libya			1636524223070.76
74	6909701	Oceania	Papua New Guinea			466521361114.311
75	6943739	South America	Paraguay			402294650536.476
76	7101510	Europe	Bulgaria			110029924011.404
77	7111024	Europe	Serbia			76232737912.4884
78	7126706	Asia	Laos			229798138060.241
79	7531386	Africa	Somalia			486443125709.23
80	7965055	Africa	Togo			61222637781.2825
81	8236303	Europe	Switzerland			46063257324.0393
82	8299706	Asia	Israel			23010251034.8654
83	8468555	Asia	Tajikistan			138010387019.761
84	8754413	Europe	Austria			84824085671.1678
85	9038741	North America	Honduras			114200451680.527
86	9549747	Europe	Belarus			208097501687.898
87	9850845	Europe	Hungary			92223563486.7973
88	9960487	Europe	Sweden			447872323030.887
89	9961396	Asia	Azerbaijan			91012352138.8303
90	10248069	Asia	Jordan			89284998139.1287
91	10646714	North America	Haiti			28628396092.6457
92	10674723	Europe	Czechia			80936955801.5446
93	10734247	North America	Dominican Rep.			48306262335.7236
94	10768477	Europe	Greece			131854192993.303
95	10839514	Europe	Portugal			93318040232.8807
96	11038805	Africa	Benin			117478842076.64
97	11138234	South America	Bolivia			1088907530803.22
98	11147407	North America	Cuba			115172058712.546
99	11403800	Africa	Tunisia			156277140541.173
100	11466756	Africa	Burundi			26355519798.0136

Rows: 1-100 | Columns: 6

We can now export our vDataFrame as a GeoPandas DataFrame.

[3]:

df = world.to_geopandas(geometry = "geometry")
display(df)

	pop_est	continent	country	geography	area	geometry
0	140	Seven seas (open ocean)	Fr. S. Antarctic Lands	b'\\251UY\\305\\300\\000\\000\\000\\000\\000\\...	1.156510e+10	POLYGON ((68.93500 -48.62500, 69.58000 -48.940...
1	2931	South America	Falkland Is.	b'\\203\\377\\363\\304\\000\\000\\000\\000\\00...	1.630198e+10	POLYGON ((-61.20000 -51.85000, -60.00000 -51.2...
2	4050	Antarctica	Antarctica	b'\\200\\000\\000\\000\\000\\000\\000\\000\\00...	1.223625e+13	MULTIPOLYGON (((-48.66062 -78.04702, -48.15140...
3	57713	North America	Greenland	b'\\226\\252\\240\\000\\000\\000\\000\\000\\00...	2.189751e+12	POLYGON ((-46.76379 82.62796, -43.40644 83.225...
4	265100	Asia	N. Cyprus	b'\\274\\000\\014\\000\\000\\000\\000\\000\\00...	3.786381e+09	POLYGON ((32.73178 35.14003, 32.80247 35.14550...
...	...	...	...	...	...	...
172	207353391	South America	Brazil	b'\\200\\000\\000\\000\\000\\000\\000\\000\\00...	8.540950e+12	POLYGON ((-53.37366 -33.76838, -53.65054 -33.2...
173	260580739	Asia	Indonesia	b'\\200\\000\\000\\000\\000\\000\\000\\000\\00...	1.827304e+12	MULTIPOLYGON (((141.00021 -2.60015, 141.01706 ...
174	326625791	North America	United States of America	b'\\226\\252\\240\\000\\000\\000\\000\\000\\00...	9.494301e+12	MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
175	1281935911	Asia	India	b'\\274\\000\\000\\000\\000\\000\\000\\000\\00...	3.150427e+12	POLYGON ((97.32711 28.26158, 97.40256 27.88254...
176	1379302771	Asia	China	b'\\274\\000\\000\\000\\000\\000\\000\\000\\00...	9.408026e+12	MULTIPOLYGON (((109.47521 18.19770, 108.65521 ...

177 rows × 6 columns

From there, we can draw any geospatial object.

[4]:

ax = df.plot(edgecolor = "black",
             color = "white",
             figsize = (10, 9))

../../../_images/notebooks_full_stack_geopandas_index_8_0.png

[5]:

from verticapy.datasets import load_cities
# Loading the cities dataset
cities = load_cities()

import verticapy.stats as st
import matplotlib.pyplot as plt

# Creating a Matplotlib figure
fig, ax = plt.subplots()
fig.set_size_inches(11, 8)

# Extracting longitude and latitude
cities["lon"] = st.apply("st_x", cities["geometry"])
cities["lat"] = st.apply("st_y", cities["geometry"])

# Drawing the data on a Map
ax = cities.scatter(["lon", "lat"], ax = ax)
df.plot(edgecolor = "black",
        color = "white",
        ax = ax)

[5]:

<AxesSubplot:xlabel='"lon"', ylabel='"lat"'>

../../../_images/notebooks_full_stack_geopandas_index_9_1.png

You can also draw maps using the ‘geo_plot’ method. The dataset used in the following cell is available here.

[6]:

import verticapy as vp

# Africa Dataset
africa = vp.read_csv("data/africa_education.csv")
africa_world = load_world()
africa_world = africa_world[africa_world["continent"] == "Africa"]
ax = africa_world["geometry"].geo_plot(color = "white",
                                       edgecolor='black',)

# displaying schools in Africa
africa.scatter(["lon", "lat"],
               catcol = "country_long",
               ax = ax,
               with_others = False,
               max_cardinality = 20)

[6]:

<AxesSubplot:xlabel='"lon"', ylabel='"lat"'>

../../../_images/notebooks_full_stack_geopandas_index_11_1.png