Extracting a specific city in Geopandas Dataset

In the following lines, i will try to show you how to extract your own city from geopandas dataset without uploading any other data.

In [1]:

import geopandas
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

In [4]:

# Getting world countries
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.head()

Out[4]:

	pop_est	continent	name	iso_a3	gdp_md_est	geometry
0	920938	Oceania	Fiji	FJI	8374.0	MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1	53950935	Africa	Tanzania	TZA	150600.0	POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2	603253	Africa	W. Sahara	ESH	906.5	POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3	35623680	North America	Canada	CAN	1674000.0	MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4	326625791	North America	United States of America	USA	18560000.0	MULTIPOLYGON (((-122.84000 49.00000, -120.0000...

In [5]:

world.columns.values

Out[5]:

array(['pop_est', 'continent', 'name', 'iso_a3', 'gdp_md_est', 'geometry'],
      dtype=object)

In [6]:

# Checking the info of our dataset 
world.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 177 entries, 0 to 176
Data columns (total 6 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   pop_est     177 non-null    int64   
 1   continent   177 non-null    object  
 2   name        177 non-null    object  
 3   iso_a3      177 non-null    object  
 4   gdp_md_est  177 non-null    float64 
 5   geometry    177 non-null    geometry
dtypes: float64(1), geometry(1), int64(1), object(3)
memory usage: 5.6+ KB

In [7]:

# Plotting 
world.plot()

Out[7]:

<AxesSubplot:>

We have global world map here, let's for example extracte only african contient from the global map

In [10]:

africa = world[world['continent'] == 'Africa']
africa.head()

Out[10]:

	pop_est	continent	name	iso_a3	gdp_md_est	geometry
1	53950935	Africa	Tanzania	TZA	150600.0	POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2	603253	Africa	W. Sahara	ESH	906.5	POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
11	83301151	Africa	Dem. Rep. Congo	COD	66010.0	POLYGON ((29.34000 -4.49998, 29.51999 -5.41998...
12	7531386	Africa	Somalia	SOM	4719.0	POLYGON ((41.58513 -1.68325, 40.99300 -0.85829...
13	47615739	Africa	Kenya	KEN	152700.0	POLYGON ((39.20222 -4.67677, 37.76690 -3.67712...

In [11]:

africa.plot()

Out[11]:

<AxesSubplot:>

I'm from Senegal and i want to extract my country, let's try it

In [14]:

senegal = africa[africa['name'] == 'Senegal']
senegal.head()

Out[14]:

	pop_est	continent	name	iso_a3	gdp_md_est	geometry
51	14668522	Africa	Senegal	SEN	39720.0	POLYGON ((-16.71373 13.59496, -17.12611 14.373...

We can see a 'POLYGON' in the 'geometry', because cities can be Point, countries are Polygons etc...

In [15]:

# Let see how Senegal looks like 
senegal.plot()

Out[15]:

<AxesSubplot:>

That the Senegal map i've know before and now. But Senegal has different cities and locations. I will see if i can show you the location of Dakar (the capital city of Senegal) in the Senegal map

In Geopandas dataset, there is a dataset named 'naturalearth_cities'. From there, i will try to extract 'Dakar'.

In [18]:

cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
cities.head()

Out[18]:

	name	geometry
0	Vatican City	POINT (12.45339 41.90328)
1	San Marino	POINT (12.44177 43.93610)
2	Vaduz	POINT (9.51667 47.13372)
3	Luxembourg	POINT (6.13000 49.61166)
4	Palikir	POINT (158.14997 6.91664)

In [19]:

cities.columns.values

Out[19]:

array(['name', 'geometry'], dtype=object)

In [20]:

cities.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 202 entries, 0 to 201
Data columns (total 2 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   name      202 non-null    object  
 1   geometry  202 non-null    geometry
dtypes: geometry(1), object(1)
memory usage: 1.6+ KB

In [21]:

cities.plot()

Out[21]:

<AxesSubplot:>

We have the cities in every country sated in geopandas data. Note: There might be some other countries who's not having specific status and might not be included in this dataset

So far, our figure is not showing mutch information, its only showing us scattered points. Our goal here is to locate only 'Dakar' on 'Senegal' map. To do that, we need to extracte Dakar from cities

In [24]:

dakar = cities[cities['name'] == 'Dakar']
dakar.head()

Out[24]:

	name	geometry
170	Dakar	POINT (-17.47508 14.71778)

The Point here describes a city as we explained above. Where is Dakar in Senegal?

In [25]:

ax = senegal.plot(color='white', edgecolor='black')
dakar.plot(ax=ax, color='red')

Out[25]:

<AxesSubplot:>

The red point is indicating the location of Dakar in Senegal map. My next tutorial will try to show all the main cities of Senegal in map

Conclusion: Geopandas is a great library that can help to do a lot of things. We could do all our work here, without uploading any data, that's also great. In fact, there are a lot of ideas behind the geopandas dataset, we can extract for example all african cities in world map or only AFRICA

In [ ]: