In [1]:
# read the shape file world_administrative_boundaries.shp
import geopandas as gpd 
  
# Reading the world shapefile 
world_data = gpd.read_file('world-administrative-boundaries.shp') 
  
world_data
Out[1]:
iso3 status color_code name continent region iso_3166_1_ french_shor geometry
0 None Adm. by EGY EGY Ma'tan al-Sarra Africa Northern Africa None Ma'tan al-Sarra POLYGON ((33.25104 21.99977, 34.15064 21.99603...
1 CHE Member State CHE Switzerland Europe Western Europe CH Suisse POLYGON ((9.56672 47.54045, 9.55980 47.50209, ...
2 None UK Territory GBR Jersey Europe Northern Europe None Jersey POLYGON ((-2.01500 49.21417, -2.02111 49.17721...
3 AUT Member State AUT Austria Europe Western Europe AT Autriche POLYGON ((16.94618 48.61907, 16.94333 48.57333...
4 PRT Member State PRT Portugal Europe Southern Europe PT Portugal POLYGON ((-7.43185 37.25319, -7.41903 37.18055...
... ... ... ... ... ... ... ... ... ...
251 SVK Member State SVK Slovakia Europe Eastern Europe SK Slovaquie POLYGON ((22.55805 49.07944, 22.55166 49.03943...
252 MLI Member State MLI Mali Africa Western Africa ML Mali POLYGON ((-4.80611 25.00027, -4.52528 24.82500...
253 ARM Member State ARM Armenia Asia Western Asia AM Arménie POLYGON ((46.54038 38.87559, 46.51639 38.87804...
254 ALB Member State ALB Albania Europe Southern Europe AL Albanie POLYGON ((20.07142 42.56091, 20.10208 42.53347...
255 GIB UK Non-Self-Governing Territory GBR Gibraltar Europe Southern Europe GI Gibraltar POLYGON ((-5.35580 36.16331, -5.33451 36.16256...

256 rows × 9 columns

In [2]:
import geopandas as gpd 
  
# Reading the world shapefile 
world_data = gpd.read_file(r'world-administrative-boundaries.shp') 
  
world_data.plot()
Out[2]:
<Axes: >
In [3]:
# Reading the world shapefile 
world_data = gpd.read_file(r'world-administrative-boundaries.shp') 
  
world_data2 = world_data[['name', 'geometry']]
In [4]:
#GeoSeries.area
# Reading the world shapefile 
world_data = gpd.read_file(r'world-administrative-boundaries.shp') 
  
world_data2 = world_data[['name', 'geometry']] 
  
# Calculating the area of each country 
world_data2['area'] = world_data.area 

C:\Users\Ummesalma\AppData\Local\Temp\ipykernel_3720\1599538959.py:8: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  world_data2['area'] = world_data.area
C:\Users\Ummesalma\AppData\Roaming\Python\Python311\site-packages\geopandas\geodataframe.py:1525: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
In [5]:
world_data.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 256 entries, 0 to 255
Data columns (total 9 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   iso3         238 non-null    object  
 1   status       255 non-null    object  
 2   color_code   255 non-null    object  
 3   name         256 non-null    object  
 4   continent    256 non-null    object  
 5   region       252 non-null    object  
 6   iso_3166_1_  236 non-null    object  
 7   french_shor  256 non-null    object  
 8   geometry     256 non-null    geometry
dtypes: geometry(1), object(8)
memory usage: 18.1+ KB
In [6]:
world_data2.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 256 entries, 0 to 255
Data columns (total 3 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   name      256 non-null    object  
 1   geometry  256 non-null    geometry
 2   area      256 non-null    float64 
dtypes: float64(1), geometry(1), object(1)
memory usage: 6.1+ KB
In [7]:
world_data2.head()
Out[7]:
name geometry area
0 Ma'tan al-Sarra POLYGON ((33.25104 21.99977, 34.15064 21.99603... 0.160197
1 Switzerland POLYGON ((9.56672 47.54045, 9.55980 47.50209, ... 4.889680
2 Jersey POLYGON ((-2.01500 49.21417, -2.02111 49.17721... 0.015395
3 Austria POLYGON ((16.94618 48.61907, 16.94333 48.57333... 10.039437
4 Portugal POLYGON ((-7.43185 37.25319, -7.41903 37.18055... 9.302478
In [8]:
import geopandas as gpd 
  
# Reading the world shapefile 
world_data = gpd.read_file(r'world-administrative-boundaries.shp') 
  
world_data2 = world_data[['name', 'geometry']] 
  
# Calculating the area of each country 
world_data2['area'] = world_data.area 
  
# Removing Antarctica from GeoPandas GeoDataframe 
world_data2 = world_data[world_data['name'] != 'Antarctica'] 
world_data2.plot() 

C:\Users\Ummesalma\AppData\Local\Temp\ipykernel_3720\3310628924.py:9: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  world_data2['area'] = world_data.area
C:\Users\Ummesalma\AppData\Roaming\Python\Python311\site-packages\geopandas\geodataframe.py:1525: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[8]:
<Axes: >
In [9]:
world_data2[world_data2.name=="India"].plot()
Out[9]:
<Axes: >
In [10]:
# Changing the projection 
current_crs = world_data.crs 
world_data.to_crs(epsg=3857, inplace=True) 
  
world_data.plot(column='name', cmap='hsv')
Out[10]:
<Axes: >
In [11]:
# Re-calculate the areas in Sq. Km. 
world_data2['area'] = world_data.area/1000000
  
# Adding a legend 
world_data2.plot(column='area', cmap='hsv', legend=True, 
                legend_kwds={'label': "Area of the country (Sq. Km.)"},  
                figsize=(7, 7))
Out[11]:
<Axes: >
In [ ]: