🔌 Quick Usage
Note
Be sure to check out the installation section before.
First of all, activate the pandas extension
1from italy_geopop.pandas_extension import pandas_activate
2pandas_activate(include_geometry=True, data_year=2022)
Municipalities
Then you can use italy-geopop to get data for your pd.Series municipalities.
3data = pd.Series(["Torino", "Agliè", "Airasca"])
4data.italy_geopop.from_municipality(population_limits='total')
municipality_code |
municipality |
cadastral_code |
province_code |
province |
province_short |
region |
region_code |
geometry |
population_F |
population_M |
population |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
1272 |
Torino |
L219 |
1 |
Torino |
TO |
Piemonte |
1 |
MULTIPOLYGON |
441686.0 |
407062.0 |
848748.0 |
1 |
1001 |
Agliè |
A074 |
1 |
Torino |
TO |
Piemonte |
1 |
MULTIPOLYGON |
1347.0 |
1215.0 |
2562.0 |
2 |
1002 |
Airasca |
A109 |
1 |
Torino |
TO |
Piemonte |
1 |
MULTIPOLYGON |
1793.0 |
1867.0 |
3660.0 |
Provinces
You can also use italy-geopop to get data for your pd.Series provinces.
5data = pd.Series(["Torino", "Milano", "Venezia"])
6data.italy_geopop.from_province(population_limits=[50], population_labels=['below_50', 'above_equal_50'])
province_code |
province |
province_short |
municipalities |
region |
region_code |
geometry |
below_50_F |
above_equal_50_F |
below_50_M |
above_equal_50_M |
below_50 |
above_equal_50 |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
1 |
Torino |
TO |
[{‘cadastral_code’: ‘A074 |
Piemonte |
1 |
POLYGON |
550793.0 |
586366.0 |
572143.0 |
499068.0 |
1122936.0 |
1085434.0 |
1 |
15 |
Milano |
MI |
[{‘cadastral_code’: ‘A010 |
Lombardia |
3 |
MULTIPOLYGON |
857481.0 |
792711.0 |
898004.0 |
666434.0 |
1755485.0 |
1459145.0 |
2 |
27 |
Venezia |
VE |
[{‘cadastral_code’: ‘A302 |
Veneto |
5 |
POLYGON |
205100.0 |
224401.0 |
214116.0 |
193299.0 |
419216.0 |
417700.0 |
Regions
Or you can use italy-geopop to get data for your pd.Series regions.
7data = pd.Series(["Piemonte", "Lombardia", "Veneto"])
8data.italy_geopop.from_region()
region_code |
region |
provinces |
geometry |
<3_F |
3-11_F |
11-19_F |
19-25_F |
25-50_F |
50-65_F |
65-75_F |
>=75_F |
<3_M |
3-11_M |
11-19_M |
19-25_M |
25-50_M |
50-65_M |
65-75_M |
>=75_M |
<3 |
3-11 |
11-19 |
19-25 |
25-50 |
50-65 |
65-75 |
>=75 |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
1 |
Piemonte |
[{‘municipalities’: array |
POLYGON |
40122.0 |
131269.0 |
149768.0 |
112474.0 |
614252.0 |
506764.0 |
279224.0 |
348632.0 |
42361.0 |
138788.0 |
159618.0 |
123911.0 |
629878.0 |
490464.0 |
251918.0 |
236907.0 |
82483.0 |
270057.0 |
309386.0 |
236385.0 |
1244130.0 |
997228.0 |
531142.0 |
585539.0 |
1 |
3 |
Lombardia |
[{‘municipalities’: array |
MULTIPOLYGON |
103867.0 |
336353.0 |
378153.0 |
274455.0 |
1520576.0 |
1144338.0 |
586818.0 |
716916.0 |
109087.0 |
356547.0 |
403719.0 |
303888.0 |
1572013.0 |
1135834.0 |
524720.0 |
475720.0 |
212954.0 |
692900.0 |
781872.0 |
578343.0 |
3092589.0 |
2280172.0 |
1111538.0 |
1192636.0 |
2 |
5 |
Veneto |
[{‘municipalities’: array |
POLYGON |
48285.0 |
157284.0 |
182441.0 |
136850.0 |
718105.0 |
578543.0 |
291166.0 |
354328.0 |
51390.0 |
166176.0 |
194064.0 |
149055.0 |
737009.0 |
573454.0 |
267403.0 |
242192.0 |
99675.0 |
323460.0 |
376505.0 |
285905.0 |
1455114.0 |
1151997.0 |
558569.0 |
596520.0 |
Smart functionalities
smart_from_municipality, smart_from_region and smart_from_province methods are also available.
Those methods will try to guess from the input data and will return the data only if the match is unequivocal.
9data = pd.Series(["Regione Lombardia", "Regione del Veneto", "Veneto o Lombardia", 15])
10data.italy_geopop.smart_from_region()
region_code |
region |
provinces |
geometry |
<3_F |
3-11_F |
11-19_F |
19-25_F |
25-50_F |
50-65_F |
65-75_F |
>=75_F |
<3_M |
3-11_M |
11-19_M |
19-25_M |
25-50_M |
50-65_M |
65-75_M |
>=75_M |
<3 |
3-11 |
11-19 |
19-25 |
25-50 |
50-65 |
65-75 |
>=75 |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
3.0 |
Lombardia |
[{‘municipalities’: array |
MULTIPOLYGON |
103867.0 |
336353.0 |
378153.0 |
274455.0 |
1520576.0 |
1144338.0 |
586818.0 |
716916.0 |
109087.0 |
356547.0 |
403719.0 |
303888.0 |
1572013.0 |
1135834.0 |
524720.0 |
475720.0 |
212954.0 |
692900.0 |
781872.0 |
578343.0 |
3092589.0 |
2280172.0 |
1111538.0 |
1192636.0 |
1 |
5.0 |
Veneto |
[{‘municipalities’: array |
POLYGON |
48285.0 |
157284.0 |
182441.0 |
136850.0 |
718105.0 |
578543.0 |
291166.0 |
354328.0 |
51390.0 |
166176.0 |
194064.0 |
149055.0 |
737009.0 |
573454.0 |
267403.0 |
242192.0 |
99675.0 |
323460.0 |
376505.0 |
285905.0 |
1455114.0 |
1151997.0 |
558569.0 |
596520.0 |
2 |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
nan |
3 |
15.0 |
Campania |
[{‘municipalities’: array |
MULTIPOLYGON |
65798.0 |
201345.0 |
239653.0 |
185798.0 |
909861.0 |
641838.0 |
320637.0 |
311913.0 |
69298.0 |
213525.0 |
253444.0 |
200452.0 |
907541.0 |
602405.0 |
288497.0 |
212415.0 |
135096.0 |
414870.0 |
493097.0 |
386250.0 |
1817402.0 |
1244243.0 |
609134.0 |
524328.0 |
More
Check out the complete guide for more informations.