205 - Simple Maps: World Cities and Countries

For this exercise we will use the (Basic) CSV data from Simple Maps World Cities and Simple Maps Countries.

To do this you can execute the following commands in your working area (e.g. your cloned repository on BinderHub or your laptop):

mkdir -p assets
curl -O http://research-software-collaborations.org/python-june2025/_static/assets/simplemaps_countries_basicv1.0.zip
curl -O http://research-software-collaborations.org/python-june2025/_static/assets/simplemaps_worldcities_basicv1.901.zip
unzip simplemaps_countries_basicv1.0.zip
unzip simplemaps_worldcities_basicv1.901.zip
cd ..

After you have done that you should have files:

• assets/simplemaps_countries_basicv1.0/countries.csv

• assets/simplemaps_worldcities_basicv1.901/worldcities.csv

You should also get the following file and unpack it in the same directory where you will be creating files for your exercises:

curl -O http://research-software-collaborations.org/python-june2025/_static/assets/hsflib.zip
unzip hsflib.zip

At that point you can start the exercise.

import pandas as pd
import matplotlib.pyplot as plt

The data we have includes information on latitude and longitude of various cities. There is a large infrastructure (in Python and beyond) for managing geographic information, maps, etc. (Often referred to as Geogrpahic Information System (GIS).) We don’t intend for you to need to learn about that here, so we have encapsulated a simplified version of that in some functions that you can use to get a plotting surface set up with x and y limits set for longitude and latitude and with a faint background showing the coastlines of the various continents and islands.

To use that, you need to do the following one time in your notebook or program.

import hsflib
all_polygons_coords = hsflib.load_shapefile()

Then each time you want to plot something, call hsflib.setup_map(all_polygons_coords) as you would normally call plt.figure(...).

Here we show to plot with no data overlaid.

fig, ax = hsflib.setup_map(all_polygons_coords)
plt.show()

For this exercise you should load the city data:

df_cities = pd.read_csv('../assets/simplemaps_worldcities_basicv1.901/worldcities.csv')
df_cities.head(10)

	city	city_ascii	lat	lng	country	iso2	iso3	admin_name	capital	population	id
0	Tokyo	Tokyo	35.6870	139.7495	Japan	JP	JPN	Tōkyō	primary	37785000.0	1392685764
1	Jakarta	Jakarta	-6.1750	106.8275	Indonesia	ID	IDN	Jakarta	primary	33756000.0	1360771077
2	Delhi	Delhi	28.6100	77.2300	India	IN	IND	Delhi	admin	32226000.0	1356872604
3	Guangzhou	Guangzhou	23.1300	113.2600	China	CN	CHN	Guangdong	admin	26940000.0	1156237133
4	Mumbai	Mumbai	19.0761	72.8775	India	IN	IND	Mahārāshtra	admin	24973000.0	1356226629
5	Manila	Manila	14.5958	120.9772	Philippines	PH	PHL	Manila	primary	24922000.0	1608618140
6	Shanghai	Shanghai	31.2286	121.4747	China	CN	CHN	Shanghai	admin	24073000.0	1156073548
7	São Paulo	Sao Paulo	-23.5504	-46.6339	Brazil	BR	BRA	São Paulo	admin	23086000.0	1076532519
8	Seoul	Seoul	37.5667	126.9833	Korea, South	KR	KOR	Seoul	primary	23016000.0	1410836482
9	Mexico City	Mexico City	19.4333	-99.1333	Mexico	MX	MEX	Ciudad de México	primary	21804000.0	1484247881

And you should load the country data:

df_countries = pd.read_csv('../assets/simplemaps_countries_basicv1.0/countries.csv')
df_countries.head(10)

	country	demonym	id	iso2	tld	currency	population	density	area	gdp	median_age	language	website	calling_code	driving_side	continent	un_member	religion
0	China	Chinese	CN	CN	.cn	Chinese Yuan	1.413143e+09	147.2	9596960.0	17963170.0	39.8	Chinese	https://www.gov.cn	86.0	right	Asia	True	No Religion
1	India	Indian	IN	IN	.in	Indian Rupee	1.399180e+09	425.6	3287263.0	3465541.0	29.5	Hindi	http://india.gov.in	91.0	left	Asia	True	Hinduism
2	United States	American	US	US	.us	United States Dollar	3.396651e+08	34.5	9833517.0	25744100.0	38.5	English	https://www.usa.gov	1.0	right	North America	True	Christianity
3	Indonesia	Indonesian	ID	ID	.id	Rupiah	2.794763e+08	146.7	1904569.0	1319100.0	31.2	Indonesian	https://indonesia.go.id	62.0	left	Asia	True	Islam
4	Pakistan	Pakistani	PK	PK	.pk	Pakistani Rupee	2.476536e+08	311.1	796095.0	326796.0	22.7	Urdu	http://www.pakistan.gov.pk	92.0	left	Asia	True	Islam
5	Nigeria	Nigerian	NG	NG	.ng	Naira	2.308427e+08	249.9	923768.0	15414.0	19.2	English	https://nigeria.gov.ng	234.0	right	Africa	True	Christianity
6	Brazil	Brazilian	BR	BR	.br	Brazilian Real	2.186898e+08	25.7	8515770.0	1920095.0	34.7	Portuguese	https://www.gov.br	55.0	right	South America	True	Christianity
7	Bangladesh	Bangladeshi	BD	BD	.bd	Bangladeshi Taka	1.671845e+08	1126.1	148460.0	432677.0	29.2	Bengali	http://www.bangladesh.gov.bd	880.0	left	Asia	True	Islam
8	Russia	Russian	RU	RU	.ru	Russian Ruble	1.416989e+08	8.3	17098242.0	2240422.0	41.5	Russian	http://gov.ru	7.0	right	Europe	True	Christianity
9	Mexico	Mexican	MX	MX	.mx	Mexican Peso	1.298755e+08	66.1	1964375.0	1463323.0	30.6	Spanish	https://www.gob.mx	52.0	right	North America	True	Christianity

Not that for invididual cities, you can get the iso2 code for the country in which they are located and then look up various bits of information for that country. In this exercise, you should try to make plots of all cities satisfying various criteria that require you to look up things in the country dataframe. For example:

• all cities in countries where Spanish is the (main) language

• all cities in countries which are in countries that have a population greater than 50 million people

Try also combining criteria and also creating plots which focus on particular continents. Make at least 8 such plots. (Don’t forget to plot first the underlying continent/coastline plot for most of these, but also make one plot in which you plot all cities in the list, but without the continent/coastline graphic.)