!pip install fiona

Requirement already satisfied: fiona in c:\users\omen\anaconda3\envs\dataespacial_311\lib\site-packages (1.10.1)
Requirement already satisfied: attrs>=19.2.0 in c:\users\omen\anaconda3\envs\dataespacial_311\lib\site-packages (from fiona) (24.3.0)
Requirement already satisfied: certifi in c:\users\omen\anaconda3\envs\dataespacial_311\lib\site-packages (from fiona) (2025.4.26)
Requirement already satisfied: click~=8.0 in c:\users\omen\anaconda3\envs\dataespacial_311\lib\site-packages (from fiona) (8.2.1)
Requirement already satisfied: click-plugins>=1.0 in c:\users\omen\anaconda3\envs\dataespacial_311\lib\site-packages (from fiona) (1.1.1)
Requirement already satisfied: cligj>=0.5 in c:\users\omen\anaconda3\envs\dataespacial_311\lib\site-packages (from fiona) (0.7.2)
Requirement already satisfied: colorama in c:\users\omen\anaconda3\envs\dataespacial_311\lib\site-packages (from click~=8.0->fiona) (0.4.6)

linkWorldMap="https://github.com/CienciaDeDatosEspacial/intro_geodataframe/raw/main/maps/worldMaps.gpkg"

import os
import geopandas as gpd
from  fiona import listlayers
listlayers(linkWorldMap)

['indicators', 'countries', 'rivers', 'cities']

countries=gpd.read_file(linkWorldMap,layer='countries')
rivers=gpd.read_file(linkWorldMap,layer='rivers')
cities=gpd.read_file(linkWorldMap,layer='cities')
indicators=gpd.read_file(linkWorldMap,layer='indicators')

# prompt: quiero proyectar countries, rives, cities e indicators

print(countries.head())
print(rivers.head())
print(cities.head())
print(indicators.head())

               COUNTRY                                           geometry
0  Aruba (Netherlands)  MULTIPOLYGON (((-69.88223 12.41111, -69.94695 ...
1  Antigua and Barbuda  MULTIPOLYGON (((-61.73889 17.54055, -61.75195 ...
2          Afghanistan  MULTIPOLYGON (((61.27656 35.60725, 61.29638 35...
3              Algeria  MULTIPOLYGON (((-5.15213 30.18047, -5.13917 30...
4           Azerbaijan  MULTIPOLYGON (((46.54037 38.87559, 46.49554 38...
        NAME  SYSTEM                                           geometry
0      Aldan    Lena  MULTILINESTRING ((124.00678 56.47258, 123.2595...
1     Amazon  Amazon  MULTILINESTRING ((-61.2773 -3.60706, -60.68466...
2  Amu Darya    None  MULTILINESTRING ((73.98818 37.49952, 73.52595 ...
3       Amur    None  MULTILINESTRING ((122.63956 49.9973, 120.47874...
4     Angara    None  MULTILINESTRING ((105.07841 51.93053, 103.9295...
               NAME COUNTRY CAPITAL                   geometry
0          Murmansk  Russia       N  POINT (33.08604 68.96355)
1       Arkhangelsk  Russia       N  POINT (40.64616 64.52067)
2  Saint Petersburg  Russia       N  POINT (30.45333 59.95189)
3           Magadan  Russia       N   POINT (150.78001 59.571)
4             Perm'  Russia       N  POINT (56.23246 58.00024)
               COUNTRY                            Officialstatename  \
0  ANTIGUA AND BARBUDA                          Antigua and Barbuda   
1          AFGHANISTAN          The Islamic Republic of Afghanistan   
2              ALGERIA  The People's Democratic Republic of Algeria   
3           AZERBAIJAN                   The Republic of Azerbaijan   
4              ALBANIA                      The Republic of Albania   

  InternetccTLD iso2 iso3  fragility_date  fragility          co2  co2_date  \
0           .ag   AG  ATG            2019       54.4     729000.0      2019   
1           .af   AF  AFG            2019      105.0    7893000.0      2019   
2           .dz   DZ  DZA            2019       75.4  151633000.0      2019   
3           .az   AZ  AZE            2019       73.2   35389000.0      2019   
4           .al   AL  ALB            2019       58.9    3794000.0      2019   

                              region  ForestRev_gdp  ForestRev_date  \
0  CENTRAL AMERICA AND THE CARIBBEAN           0.00            2018   
1                         SOUTH ASIA           0.20            2018   
2                             AFRICA           0.10            2018   
3                        MIDDLE EAST           0.02            2018   
4                             EUROPE           0.18            2018   

   fragility_Qt  fragility_Qt_jc5 fragility_Qt_jc5_cat  \
0     -0.530113                 1               1_Good   
1      1.827146                 4               4_Poor   
2      0.304520                 3                3_Bad   
3      0.233695                 2             2_Middle   
4     -0.424447                 1               1_Good   

                                            geometry  
0  MULTIPOLYGON (((-61.73889 17.54055, -61.75195 ...  
1  POLYGON ((61.27656 35.60725, 61.29638 35.62853...  
2  POLYGON ((-5.15213 30.18047, -5.13917 30.19236...  
3  MULTIPOLYGON (((46.54037 38.87559, 46.49554 38...  
4  POLYGON ((20.79192 40.43154, 20.78722 40.39472...

india = countries[countries.COUNTRY=='India']

citiesIndia_clipped = gpd.clip(gdf=cities,
                          mask=india)
riversIndia_clipped = gpd.clip(gdf=rivers,
                               mask=india)

india_7755=india.to_crs(7755)

rivers_india_7755=riversIndia_clipped.to_crs(india_7755.crs)
cities_india_7755=citiesIndia_clipped.to_crs(india_7755.crs)

india_7755.centroid

104    POINT (3955049.307 3862608.372)
dtype: geometry

citiesbelow = cities_india_7755.cx[:3955049.307 ,:3862608.372]

riversabove = rivers_india_7755.cx[3955049.307: ,3862608.372:]

base = india_7755.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4,figsize=(5,5))
citiesbelow.plot(marker='+', color='red', markersize=15,
                    ax=base)

<Axes: >

base = india_7755.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4,figsize=(5,5))
riversabove.plot(edgecolor='blue', linewidth=0.5,
                    ax=base)

<Axes: >

Link = 'https://github.com/alonso-mendoza/introgeodf/raw/refs/heads/main/maps/indiaMaps_7755.gpkg'

listlayers(Link)

['country', 'cities', 'rivers', 'centroid', 'airports', 'states']

india_states=gpd.read_file(Link,layer='states')
india_states.plot(facecolor='lightgrey', edgecolor='black',linewidth=0.2)

<Axes: >

india_states.head()

len(india_states.NAME_1)

667

len(set(india_states.NAME_1))

36

india_states[india_states.NAME_1=='Andhra Pradesh'].plot()

<Axes: >

!pip show shapely

Name: shapely
Version: 2.0.6
Summary: Manipulation and analysis of geometric objects
Home-page: 
Author: Sean Gillies
Author-email: 
License: BSD 3-Clause
Location: C:\Users\OMEN\anaconda3\envs\dataEspacial_311\Lib\site-packages
Requires: numpy
Required-by: geopandas

india_states[india_states.NAME_1=='Andhra Pradesh'].union_all()

AndhraPradesh_union=india_states[india_states.NAME_1=='Andhra Pradesh'].union_all()

type(AndhraPradesh_union)

shapely.geometry.multipolygon.MultiPolygon

gpd.GeoDataFrame(index=[0],data={'NAME_1':'Andhra Pradesh'},
                 crs=india_states.crs,
                 geometry=[AndhraPradesh_union])

india_states[india_states.NAME_1=='Andhra Pradesh'].dissolve().plot()

<Axes: >

AndhraPradesh_dissolve=india_states[india_states.NAME_1=='Andhra Pradesh'].dissolve()

type(AndhraPradesh_dissolve)

geopandas.geodataframe.GeoDataFrame

AndhraPradesh_dissolve

AndhraPradesh_dissolve.drop(columns=['NAME_2','HASC_2','CCN_2','CCA_2','TYPE_2','ENGTYPE_2','NL_NAME_2','VARNAME_2'],inplace=True)

AndhraPradesh_dissolve

india_states.dissolve(by='NAME_1').plot(facecolor='yellow', edgecolor='black',linewidth=0.2)

<Axes: >

india_diss=india_states.dissolve(by='NAME_1')

india_diss

india_diss.drop(columns=['NAME_2','HASC_2','CCN_2','CCA_2','TYPE_2','ENGTYPE_2','NL_NAME_2','VARNAME_2'],inplace=True)
india_diss.reset_index(inplace=True)
india_diss.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 36 entries, 0 to 35
Data columns (total 7 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   NAME_1    36 non-null     object  
 1   geometry  36 non-null     geometry
 2   ID_0      36 non-null     int64   
 3   ISO       36 non-null     object  
 4   NAME_0    36 non-null     object  
 5   ID_1      36 non-null     int64   
 6   ID_2      36 non-null     int64   
dtypes: geometry(1), int64(3), object(3)
memory usage: 2.1+ KB

indicators.head()

indicators.dissolve(
     by="region",
     aggfunc={
         "COUNTRY": "count",
         "fragility": ["median"],
         "co2": ["median"],
         "ForestRev_gdp": ["median"]
     },as_index=False,
 )

indicatorsByRegion=indicators.dissolve(
     by="region",
     aggfunc={
         "COUNTRY": "count",
         "fragility": ["median"],
         "co2": ["median"],
         "ForestRev_gdp": ["median"]
     },as_index=False,
 )


indicatorsByRegion.plot(column = 'region')

<Axes: >

indicatorsByRegion.plot(column =('fragility', 'median'),scheme='quantiles', cmap='OrRd',
                        legend=True,
                        legend_kwds={"title": "Fragility",'loc': 'lower left'},
                        edgecolor='black',linewidth=0.2,
                        figsize=(15, 10))

<Axes: >

indicatorsByRegion.plot(column =('co2', 'median'),scheme='quantiles', cmap='OrRd',
                        legend=True,
                        legend_kwds={"title": "Fragility",'loc': 'lower left'},
                        edgecolor='black',linewidth=0.2,
                        figsize=(15, 10))

<Axes: >

indicatorsByRegion.plot(column =('ForestRev_gdp', 'median'),scheme='quantiles', cmap='OrRd',
                        legend=True,
                        legend_kwds={"title": "Fragility",'loc': 'lower left'},
                        edgecolor='black',linewidth=0.2,
                        figsize=(15, 10))

<Axes: >

india_7755=gpd.read_file(Link,layer='country')
india_7755.centroid

0    POINT (3955049.307 3862608.372)
dtype: geometry

india_7755.centroid.x.values[0],india_7755.centroid.y.values[0]

(np.float64(3955049.3071499947), np.float64(3862608.372164335))

airports_7755=gpd.read_file(Link,layer='airports')
airports_7755.head()

# coordinates
centroidX,centroidY=india_7755.centroid.x.values[0],india_7755.centroid.y.values[0]

# subsets of medium airports
India_AirTopLeft=airports_7755[airports_7755.kind=='heliport'].cx[:centroidX,centroidY:]
India_AirTopRight=airports_7755[airports_7755.kind=='heliport'].cx[centroidX:,centroidY:]
India_AirBottomLeft=airports_7755[airports_7755.kind=='heliport'].cx[:centroidX,:centroidY]
India_AirBottomRight=airports_7755[airports_7755.kind=='heliport'].cx[centroidX:,:centroidY]

base=India_AirTopLeft.plot(facecolor='grey', alpha=0.4)
India_AirTopRight.plot(ax=base,facecolor='orange', alpha=0.4)
India_AirBottomLeft.plot(ax=base,facecolor='green', alpha=0.4)
India_AirBottomRight.plot(ax=base,facecolor='red', alpha=0.4)

<Axes: >

India_AirTopLeft.union_all().convex_hull

hull_geom = India_AirTopLeft.union_all().convex_hull

Heliport_hull = gpd.GeoDataFrame(index=[0],
                                  crs=India_AirTopLeft.crs,
                                  geometry=[hull_geom])

Heliport_hull

base=india_7755.plot(facecolor='pink')
India_AirTopLeft.plot(ax=base)
Heliport_hull.plot(ax=base,facecolor='green',
                       edgecolor='white',alpha=0.4,
                       hatch='X')

<Axes: >

india_union = india_states.union_all()
india_centroid = india_union.centroid

x, y = india_centroid.x, india_centroid.y

# the north
StatesN_india=india_states.cx[:,y:]
# the south
StatesS_india=india_states.cx[:,:y]
# the west
StatesW_india=india_states.cx[:x,:]
# the east
StatesE_india=india_states.cx[x:,:]

base=StatesN_india.plot(facecolor='yellow', edgecolor='black',linewidth=0.2, alpha=0.6)
StatesS_india.plot(facecolor='grey', edgecolor='black',linewidth=0.2,ax=base, alpha=0.4)

<Axes: >

base=StatesE_india.plot(facecolor='yellow', edgecolor='black',linewidth=0.2, alpha=0.6)
StatesW_india.plot(facecolor='grey', edgecolor='black',linewidth=0.2,ax=base, alpha=0.4)

<Axes: >

StatesNS=StatesN_india.overlay(StatesS_india, how="intersection",keep_geom_type=True)
StatesNS.plot()

<Axes: >

StatesWE=StatesW_india.overlay(StatesE_india, how="intersection",keep_geom_type=True)
StatesWE.plot()

<Axes: >

StatesWE.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 65 entries, 0 to 64
Data columns (total 29 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   ID_0_1       65 non-null     int64   
 1   ISO_1        65 non-null     object  
 2   NAME_0_1     65 non-null     object  
 3   ID_1_1       65 non-null     int64   
 4   NAME_1_1     65 non-null     object  
 5   ID_2_1       65 non-null     int64   
 6   NAME_2_1     65 non-null     object  
 7   HASC_2_1     65 non-null     object  
 8   CCN_2_1      65 non-null     int64   
 9   CCA_2_1      0 non-null      object  
 10  TYPE_2_1     65 non-null     object  
 11  ENGTYPE_2_1  65 non-null     object  
 12  NL_NAME_2_1  0 non-null      object  
 13  VARNAME_2_1  13 non-null     object  
 14  ID_0_2       65 non-null     int64   
 15  ISO_2        65 non-null     object  
 16  NAME_0_2     65 non-null     object  
 17  ID_1_2       65 non-null     int64   
 18  NAME_1_2     65 non-null     object  
 19  ID_2_2       65 non-null     int64   
 20  NAME_2_2     65 non-null     object  
 21  HASC_2_2     65 non-null     object  
 22  CCN_2_2      65 non-null     int64   
 23  CCA_2_2      0 non-null      object  
 24  TYPE_2_2     65 non-null     object  
 25  ENGTYPE_2_2  65 non-null     object  
 26  NL_NAME_2_2  0 non-null      object  
 27  VARNAME_2_2  15 non-null     object  
 28  geometry     65 non-null     geometry
dtypes: geometry(1), int64(8), object(20)
memory usage: 14.9+ KB

StatesNS.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 71 entries, 0 to 70
Data columns (total 29 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   ID_0_1       71 non-null     int64   
 1   ISO_1        71 non-null     object  
 2   NAME_0_1     71 non-null     object  
 3   ID_1_1       71 non-null     int64   
 4   NAME_1_1     71 non-null     object  
 5   ID_2_1       71 non-null     int64   
 6   NAME_2_1     71 non-null     object  
 7   HASC_2_1     70 non-null     object  
 8   CCN_2_1      71 non-null     int64   
 9   CCA_2_1      0 non-null      object  
 10  TYPE_2_1     71 non-null     object  
 11  ENGTYPE_2_1  71 non-null     object  
 12  NL_NAME_2_1  0 non-null      object  
 13  VARNAME_2_1  20 non-null     object  
 14  ID_0_2       71 non-null     int64   
 15  ISO_2        71 non-null     object  
 16  NAME_0_2     71 non-null     object  
 17  ID_1_2       71 non-null     int64   
 18  NAME_1_2     71 non-null     object  
 19  ID_2_2       71 non-null     int64   
 20  NAME_2_2     71 non-null     object  
 21  HASC_2_2     69 non-null     object  
 22  CCN_2_2      71 non-null     int64   
 23  CCA_2_2      0 non-null      object  
 24  TYPE_2_2     71 non-null     object  
 25  ENGTYPE_2_2  71 non-null     object  
 26  NL_NAME_2_2  0 non-null      object  
 27  VARNAME_2_2  21 non-null     object  
 28  geometry     71 non-null     geometry
dtypes: geometry(1), int64(8), object(20)
memory usage: 16.2+ KB

StatesWE.head()

keep=['NAME_0_1','NAME_1_1','NAME_2_1','geometry']
StatesWE=StatesWE.loc[:,keep]
StatesNS=StatesNS.loc[:,keep]

StatesNS.overlay(StatesWE, how="union",keep_geom_type=True).dissolve().plot()

<Axes: >

StatesMid=StatesNS.overlay(StatesWE, how="union",keep_geom_type=True).dissolve()
StatesMid

StatesMid['zone']='middles'
StatesMid=StatesMid.loc[:,['NAME_0_1_1','zone','geometry']]
StatesMid

india_states.overlay(StatesMid, how='difference').plot()

<Axes: >

StatesN_india.overlay(StatesS_india, how="symmetric_difference",keep_geom_type=True).plot()

<Axes: >

StatesW_india.overlay(StatesE_india, how="symmetric_difference",keep_geom_type=True).plot()

<Axes: >

	geometry	ID_0	ISO	NAME_0	ID_1	ID_2	NAME_2	HASC_2	CCN_2	CCA_2	TYPE_2	ENGTYPE_2	NL_NAME_2	VARNAME_2
NAME_1
Andaman and Nicobar	MULTIPOLYGON (((2440676.316 1218490.959, 24406...	105	IND	India	1	1	Nicobar Islands	IN.AN.NI	0	None	District	District	None	None
Andhra Pradesh	MULTIPOLYGON (((1043909.395 1665486.707, 10439...	105	IND	India	2	4	Anantapur	IN.AD.AN	0	None	District	District	None	Anantpur, Ananthapur
Arunachal Pradesh	POLYGON ((2326876.361 3115449.201, 2323156.804...	105	IND	India	3	17	Anjaw	IN.AR.AJ	0	None	District	District	None	Lohit Frontier Division
Assam	MULTIPOLYGON (((2005284.039 2907253.611, 20055...	105	IND	India	4	35	Baksa	IN.AS.BK	0	None	District	District	None	Kampur, Ramrup
Bihar	MULTIPOLYGON (((1550902.77 2752332.239, 155026...	105	IND	India	5	62	Araria	IN.BR.AR	0	None	District	District	None	Bhagaipur
Chandigarh	POLYGON ((672712.762 3395024.698, 671903.523 3...	105	IND	India	6	100	Chandigarh	IN.CH.CH	0	None	District	District	None	None
Chhattisgarh	POLYGON ((1133703.074 1996301.488, 1133424.411...	105	IND	India	7	101	Baloda Bazar	IN.CT.BP	0	None	District	District	None	Baster
Dadra and Nagar Haveli	POLYGON ((293465.795 2223361.565, 292940.246 2...	105	IND	India	8	128	Dadra and Nagar Haveli	IN.DN.DN	0	None	District	District	None	None
Daman and Diu	MULTIPOLYGON (((278710.017 2253937.145, 278224...	105	IND	India	9	129	Daman	IN.DD.DA	0	None	District	District	None	None
Goa	MULTIPOLYGON (((421963.805 1709916.71, 422746....	105	IND	India	10	131	North Goa	IN.GA.NG	0	None	District	District	None	None
Gujarat	MULTIPOLYGON (((-167994.937 2598021.306, -1679...	105	IND	India	11	133	Ahmadabad	IN.GJ.AH	0	None	District	District	None	Amereli
Haryana	POLYGON ((681579.955 3123699.258, 679908.733 3...	105	IND	India	12	166	Ambala	IN.HR.AM	0	None	District	District	None	Mahendergarh, Mahendragarth, Mohindargarh, Moh...
Himachal Pradesh	POLYGON ((691878.822 3404428.116, 691981.109 3...	105	IND	India	13	187	Bilaspur	IN.HP.BI	0	None	District	District	None	Kalpa
Jammu and Kashmir	POLYGON ((482956.143 3595436.793, 481101.599 3...	105	IND	India	14	199	Anantnag	IN.JK.AG	0	None	District	District	None	Punch
Jharkhand	POLYGON ((1536350.793 2520360.915, 1536138.063...	105	IND	India	15	221	Bokaro	IN.JH.BO	0	None	District	District	None	Devghar
Karnataka	MULTIPOLYGON (((464468.392 1459169.078, 464528...	105	IND	India	16	245	Bagalkot	IN.KA.BK	0	None	District	District	None	Chikmaglur\|Kadur
Kerala	MULTIPOLYGON (((615054.538 1165018.888, 615023...	105	IND	India	17	275	Alappuzha	IN.KL.AL	0	None	District	District	None	Alleppey
Lakshadweep	MULTIPOLYGON (((202889.631 1111998.736, 202889...	105	IND	India	18	289	Lakshadweep	IN.LD.LD	0	None	District	District	None	None
Madhya Pradesh	POLYGON ((671201.563 2350601.248, 670445.272 2...	105	IND	India	19	290	Agar Malwa	IN.MP.AR	0	None	District	District	None	Chhatapur\|Chatarpur
Maharashtra	MULTIPOLYGON (((297020.466 1971079.703, 297049...	105	IND	India	20	341	Ahmadnagar	IN.MH.AH	0	None	District	District	None	Ahmadnagar
Manipur	POLYGON ((2491056.307 2851687.345, 2491450.866...	105	IND	India	21	377	Bishnupur	IN.MN.BI	0	None	District	District	None	Bishenpur\|Sishnupur
Meghalaya	POLYGON ((2085465.897 2877774.002, 2084967.317...	105	IND	India	22	386	East Garo Hills	IN.ML.EG	0	None	District	District	None	None
Mizoram	POLYGON ((2378914.796 2638351.029, 2380224.323...	105	IND	India	23	396	Aizawl	IN.MZ.AI	0	None	District	District	None	Aizawal
NCT of Delhi	POLYGON ((727380.001 3175322.059, 727214.981 3...	105	IND	India	25	415	West	IN.DL.WE	0	None	District	District	None	None
Nagaland	POLYGON ((2362734.163 2958761.724, 2362994.991...	105	IND	India	24	404	Dimapur	IN.NL.DI	0	None	District	District	None	Tuenasang
Odisha	MULTIPOLYGON (((1209299.203 1989593.638, 12087...	105	IND	India	26	416	Anugul	IN.OR.AN	0	None	District	District	None	Balangir
Puducherry	MULTIPOLYGON (((1030230.798 1200986.771, 10302...	105	IND	India	27	446	Karaikal	IN.PY.KA	0	None	District	District	None	Karaikkal\|Karikal
Punjab	POLYGON ((531216.656 3282530.031, 530744.697 3...	105	IND	India	28	450	Amritsar	IN.PB.AM	0	None	District	District	None	Sangroor
Rajasthan	POLYGON ((473699.8 2601051.514, 473228.363 259...	105	IND	India	29	472	Ajmer	IN.RJ.AJ	0	None	District	District	None	Bhilavara
Sikkim	POLYGON ((1847710.231 3078344.431, 1847468.108...	105	IND	India	30	505	East Sikkim	IN.SK.ES	0	None	District	District	None	North District
Tamil Nadu	MULTIPOLYGON (((781733.875 893733.772, 781734....	105	IND	India	31	509	Ariyalur	IN.TN.AR	0	None	District	District	None	Madras
Telangana	POLYGON ((1068856.167 2064724.737, 1069250.805...	105	IND	India	32	541	Adilabad	IN.TG.AD	0	None	District	District	None	Khammamamet
Tripura	POLYGON ((2241033.667 2655690.09, 2240912.227 ...	105	IND	India	33	551	Dhalai	IN.TR.DH	0	None	District	District	None	None
Uttar Pradesh	POLYGON ((865211.154 2779987.076, 865672.592 2...	105	IND	India	34	559	Agra	IN.UP.AG	0	None	District	District	None	Balia
Uttarakhand	POLYGON ((872292.64 3255578.372, 872006.529 32...	105	IND	India	35	634	Almora	IN.UT.AL	0	None	District	District	None	Chamoli cum Gopeshwar
West Bengal	MULTIPOLYGON (((1856631.94 2442685.678, 185663...	105	IND	India	36	647	Alipurduar	IN.WB.BN	0	None	District	District	None	Dinajpur(South)

	region	geometry	(COUNTRY, count)	(fragility, median)	(co2, median)	(ForestRev_gdp, median)
0	AFRICA	MULTIPOLYGON (((-2.92813 5.10022, -2.93222 5.1...	51	87.40	4444000.0	2.470
1	AUSTRALIA AND OCEANIA	MULTIPOLYGON (((-176.51779 -43.90778, -176.532...	6	67.95	1023000.0	0.385
2	CENTRAL AMERICA AND THE CARIBBEAN	MULTIPOLYGON (((-81.71556 7.44681, -81.71362 7...	16	61.00	7873500.0	0.115
3	CENTRAL ASIA	MULTIPOLYGON (((50.03472 45.04833, 50.0425 45....	6	75.20	106001000.0	0.005
4	EAST AND SOUTHEAST ASIA	MULTIPOLYGON (((105.26361 -6.54472, 105.26138 ...	15	70.40	238983000.0	0.180
5	EUROPE	MULTIPOLYGON (((-18.13223 27.77264, -18.1132 2...	39	40.50	38739000.0	0.150
6	MIDDLE EAST	MULTIPOLYGON (((42.76819 14.06653, 42.7868 14....	16	72.60	68706500.0	0.000
7	NORTH AMERICA	MULTIPOLYGON (((-155.211 20.00166, -155.18335 ...	3	38.00	612084000.0	0.080
8	SOUTH AMERICA	MULTIPOLYGON (((-74.93431 -51.85501, -74.93251...	11	68.20	36051000.0	0.490
9	SOUTH ASIA	MULTIPOLYGON (((73.10193 -0.58431, 73.09915 -0...	8	84.35	15916000.0	0.120

	name	kind	latitude_deg	longitude_deg	elevation_ft	municipality	geometry
0	Helipad	heliport	29.946754	79.903173	NaN	Kedareshwar	POINT (3990784.292 4647272.746)
1	Siachen Base Camp Heliport	heliport	35.194275	77.213051	11693.0	Nubra	POINT (3746184.221 5227184.684)
2	Tawang Air Force Station	heliport	27.588732	91.877439	8756.0	Tawang	POINT (5150775.399 4438629.284)
3	Daulat Beg Oldi Advanced Landing Ground	small_airport	35.396467	77.928965	16200.0	Nubra	POINT (3811709.014 5248500.57)
4	Leh Heliport	heliport	34.129295	77.537205	10682.0	Leh	POINT (3773641.362 5108719.621)

Exercise 1¶

Exercise 2¶

Exercise 3¶

Exercise 4¶

	COUNTRY	Officialstatename	InternetccTLD	iso2	iso3	fragility_date	fragility	co2	co2_date	region	ForestRev_gdp	ForestRev_date	fragility_Qt	fragility_Qt_jc5	fragility_Qt_jc5_cat	geometry
0	ANTIGUA AND BARBUDA	Antigua and Barbuda	.ag	AG	ATG	2019	54.4	729000.0	2019	CENTRAL AMERICA AND THE CARIBBEAN	0.00	2018	-0.530113	1	1_Good	MULTIPOLYGON (((-61.73889 17.54055, -61.75195 ...
1	AFGHANISTAN	The Islamic Republic of Afghanistan	.af	AF	AFG	2019	105.0	7893000.0	2019	SOUTH ASIA	0.20	2018	1.827146	4	4_Poor	POLYGON ((61.27656 35.60725, 61.29638 35.62853...
2	ALGERIA	The People's Democratic Republic of Algeria	.dz	DZ	DZA	2019	75.4	151633000.0	2019	AFRICA	0.10	2018	0.304520	3	3_Bad	POLYGON ((-5.15213 30.18047, -5.13917 30.19236...
3	AZERBAIJAN	The Republic of Azerbaijan	.az	AZ	AZE	2019	73.2	35389000.0	2019	MIDDLE EAST	0.02	2018	0.233695	2	2_Middle	MULTIPOLYGON (((46.54037 38.87559, 46.49554 38...
4	ALBANIA	The Republic of Albania	.al	AL	ALB	2019	58.9	3794000.0	2019	EUROPE	0.18	2018	-0.424447	1	1_Good	POLYGON ((20.79192 40.43154, 20.78722 40.39472...

	ID_0_1	ISO_1	NAME_0_1	ID_1_1	NAME_1_1	ID_2_1	NAME_2_1	HASC_2_1	CCA_2_1	...	ID_2_2	NAME_2_2	HASC_2_2	CCA_2_2	TYPE_2_2	ENGTYPE_2_2	NL_NAME_2_2	VARNAME_2_2	geometry
0	105	IND	India	2	Andhra Pradesh	5	Chittoor	IN.AD.CH	None	...	5	Chittoor	IN.AD.CH	None	District	District	None	Chitoor\|Chittor	POLYGON ((876352.042 1542748.215, 876820.288 1...
1	105	IND	India	2	Andhra Pradesh	7	Guntur	IN.AD.GU	None	...	7	Guntur	IN.AD.GU	None	District	District	None	None	MULTIPOLYGON (((1120124.31 1759475.804, 112015...
2	105	IND	India	2	Andhra Pradesh	10	Nellore	IN.AD.NE	None	...	10	Nellore	IN.AD.NE	None	District	District	None	None	MULTIPOLYGON (((1062591.704 1500659.82, 106271...
3	105	IND	India	2	Andhra Pradesh	11	Prakasam	IN.AD.PR	None	...	11	Prakasam	IN.AD.PR	None	District	District	None	None	MULTIPOLYGON (((1044079.239 1686711.473, 10441...
4	105	IND	India	19	Madhya Pradesh	294	Balaghat	IN.MP.BL	None	...	294	Balaghat	IN.MP.BL	None	District	District	None	None	POLYGON ((1101601.933 2486802.723, 1101725.534...