**This article was published as a part of the Data Science Blogathon**

More often than not, while performing an EDA, we are faced with a situation to display information with respect to geographical locations. For example, for a COVID 19 dataset, one may want to show the number of cases in various areas. Here is where the python library GeoPandas comes to our rescue.

This article attempts to give a gentle introduction to the usage of GeoPandas to visualize geospatial data effectively.

**Geospatial** **data** describe objects, events, or other features with respect to a location (coordinates) of the earth.

**Spatial data** is represented by the fundamental types of geometric objects (implemented by shapely)

Geometry | Used to represent | |

Points | Centre point of plot locations etc. | |

Lines | Roads, Streams | |

Polygons | Boundaries of buildings, lakes, states, etc. |

**CRS/Coordinate Reference System** tells how (using a **projection** or a mathematical equation) a location (coordinates) on the **round** earth translates to the same location, on a **flattened**, 2-dimensional coordinate system (for example, your computer screens or a paper map). The most commonly used CRS is the “EPSG:4326”.

*Image 1*

Now let’s get to the topic of our interest.

GeoPandas is based on the pandas. It extends pandas data types to include **geometry** columns and perform **spatial operations**. So, anyone familiar with pandas can easily adopt GeoPandas.

GeoPandas – GeoDataFrame & GeoSeries

The main data structure in GeoPandas is the *GeoDataFrame* that extends the *pandas* *DataFrame*. So all the base *DataFrame* operations can be performed on the *GeoDataFrame*. The *GeoDataFrame* contains one or more *GeoSeries* (that extends *pandas* *Series*) each of which contains geometries in a different **projection (***GeoSeries*.*crs***). **Though *GeoDataFrame* can have multiple *GeoSeries* columns, only one of these will be the **active** **geometry** i.e. all geometric operations will be on that column.

In the next sections, we will understand how to use some common functions like *boundary*, *centroid,* and most importantly *plot* method. To illustrate the working of geospatial visualizations, let’s will use the Teams data from the Olympics 2021 dataset.

The team’s dataset has the team name, discipline, NOC (country), and event columns. We will be using only the NOC and discipline columns in this exercise.

**Python Code:**

Summarise the disciplines per each country and plot it.

df_teams_countries_disciplines = df_teams.groupby(by="NOC").agg({'Discipline':'count'}).reset_index().sort_values(by='Discipline', ascending=False) ax = df_teams_countries_disciplines.plot.bar(x='NOC', xlabel = '', figsize=(20,8))

import geopandas as gpd df_world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) print(f"{type(df_world)}, {df_world.geometry.name}") print(df_world.head()) print(df_world.geometry.geom_type.value_counts())

“*naturalearth_lowres*” is a base map provided with *geopandas* which we loaded.

*df_world* is of type *GeoDataFrame* with continent, (country) name, and geometry (of country area) columns. geometry is of type *GeoSeries* and is the active geometry with country area represented in *Polygon* and *MultiPolygon* types.

Let’s now plot the world

df_world.plot(figsize=(10,6))

df_world_teams = df_world.merge(df_teams_countries_disciplines, how="left", left_on=['name'], right_on=['NOC']) print("Type of DataFrame : ", type(df_world_teams), df_world_teams.shape[0]) df_world_teams.head()

Note:

*df_world_teams will have some entries with NOC and Discipline as NaN. This (merge on left instead of right) has been done intentionally so that we also have the countries that have not participated.**Few country names were inconsistent between the Olympics and world datasets. So I adjusted the country names (as far as possible). Details are in the source code.*

As a first step, let’s plot the basic map – world with boundary only. In the next steps, we will colour the countries that we are interested in.

ax = df_world["geometry"].boundary.plot(figsize=(20,16))

Next, let us colour the countries that have participated in the Olympics with the darkness of the colour based on the number of disciplines the country has participated in. The more disciplines the country participated in, the darker the shade and vice versa. **Choropleth** maps colour the regions/polygons in relation to a data variable.

df_world_teams.plot( column="Discipline", ax=ax, cmap='OrRd', legend=True, legend_kwds={"label": "Participation", "orientation":"horizontal"}) ax.set_title("Countries Vs Number of Disciplines Particpated in 2021 Olympics")

where

*ax*is the axes on which to draw the map*cmap*is the name of the colormap*legend & legend_kwds*control the display of the legend.

Based on the shading, we can quickly see that Japan, America, Italy, Germany, and Australia are the countries that have participated in most disciplines.

Notice that the legend at the bottom does not look that great. Let’s modify df_world_teams.plot to make the visualization more presentable.

fig, ax = plt.subplots(1, 1, figsize=(20, 16)) divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="2%", pad="0.5%")

df_world_teams.plot(column="Discipline", ax=ax,cax=cax, cmap='OrRd', legend=True, legend_kwds={"label": "Participation"})

With a neat Colormap

Isn’t this visualization a lot neater?

Now, what about the countries that didn’t participate? All the countries that are not shaded (i.e. in white colour) are the ones that did not participate. But let’s make that more obvious by shading these countries in grey. We can use *missing_kwds* with just a plain colour or with colour and pattern for that.

df_world_teams.plot(column="Discipline", ax=ax, cax=cax, cmap='OrRd', legend=True, legend_kwds={"label": "Participation"},missing_kwds={'color': 'lightgrey'})

df_world_teams.plot(column= 'Discipline', ax=ax, cax=cax, cmap='OrRd', legend=True, legend_kwds={"label": "Participation"},missing_kwds={"color": "lightgrey","edgecolor": "white", "hatch": "|"})

Which is the discipline where the participation was the least?

df_discipline_countries = df_teams.groupby(by='Discipline').agg({'NOC':'count'}).sort_values(by='NOC', ascending=False) ax = df_discipline_countries.plot.bar(figsize=(8, 6))

Disciplines Vs Number of Countries

So Baseball/Softball is the discipline in which the least number of countries (12) participated. Now which countries participated in this discipline? Let’s find out.

For this, firstly create a dataset with only the countries that participated in the least participate discipline and then merge this dataset *df_teams_least_participated_disciplines* and *df_world* and then calculate the centroids.

# Create a dataset with only the countries participated in the least participate discipline countries_in_least_participated_disciplines = df_discipline_countries[df_discipline_countries['NOC']<13].index.tolist() print(least_participated_disciplines) df_teams_least_participated_disciplines = df_teams[df_teams['Discipline'].isin(countries_in_least_participated_disciplines)].groupby(by=['NOC','Discipline']).agg({'Discipline':'count'}) df_teams_least_participated_disciplines.groupby(by=['NOC']).agg({'Discipline':'count'}).sort_values(by='Discipline', ascending=False)

# Merge df_teams_least_participated_disciplines with df_world

df_world_teams_least_participated_disciplines = df_world.merge(df_teams_least_participated_disciplines, how="right", left_on=['name'], right_on=['NOC']) df_world_teams_least_participated_disciplines['centroid'] = df_world_teams_least_participated_disciplines.centroid print("Type of DataFrame : ", type(df_world_teams_least_disciplines),df_world_teams_least_participated_disciplines.shape[0]) print(df_world_teams_least_participated_disciplines[:5])

So Australia, Canada, Dominican Rep., and others have participated in the least-participated discipline.

Add the following lines to the plotting code we wrote earlier to mark these countries with a dark blue-filled circle.

df_world_teams_least_participated_disciplines["centroid"].plot(ax=ax, color="DarkBlue")

and the below to annotate the countries

df_world_teams_least_participated_disciplines.apply(lambda x: ax.annotate(text=x['name'], xy=(x['centroid'].coords[0][0],x['centroid'].coords[0][1]-5), ha='center'), axis=1)

Now we have the Olympics teams displayed on the world map. We can extend this further to make it richer with information. A word of caution: Do not add too much detail to the map at the cost of clarity.

In the above sections, we have seen that *geopandas.GeoDataFrame* can work seamlessly with the base *pandas.DataFrame*‘s functions – *read_file*, *merge, *etc., and with its own functions – *boundary*, *centroid*, *plot,* etc. to generate visualizations in a geographical map that enhances the data storytelling.

References: Source Code | GeoPandas | Spatial data | CRS

Image 1: https://www.earthdatascience.org/courses/earth-analytics/spatial-data-r/intro-to-coordinate-reference-systems/

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