Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://www.geeksforgeeks.org/data-science/plotting-geospatial-data-using-geopandas/ below:

Plotting Geospatial Data using GeoPandas

Last Updated : 24 Jul, 2025

GeoPandas is a powerful open-source Python library that extends the functionality of Pandas to support spatial/geographic operations. It brings the simplicity of pandas to geospatial data and makes it easy to visualize and analyze geographical datasets with minimal code. GeoPandas combines several libraries:

• pandas for data handling
• shapely for geometry operations
• fiona for file access
• matplotlib for plotting
• pyproj for coordinate reference systems

You can install the required libraries using pip:

pip install geopandas
pip install matplotlib
pip install numpy
pip install pandas

Alternatively, platforms like Google Colab or Jupyter Notebook often have many of these pre-installed or allow for quick installation within cells.

Step 1: Import Required Modules

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

Step 2: Load Sample GeoSpatial Dataset

GeoPandas provides sample datasets for practice. One of the most commonly used is the world map dataset, which includes country-level geographic and economic information.

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world.head()

Output

Explanation: This loads the built-in world dataset containing country-level geometries and attributes like population, GDP and continent. head() displays the first few records.

GeoPandas makes geospatial plotting simple. Using the built-in world dataset and the .plot() method, we can quickly visualize country boundaries:

world.plot()
plt.title("World Map")
plt.show()

Output

Explanation: A simple .plot() call visualizes country boundaries. Adding a title with Matplotlib improves clarity.

To focus on a specific region, we can filter the dataset. Here, we extract only Asian countries and visualize their GDP estimates (gdp_md_est) using a red color gradient:

world_asia = world[world.continent == "Asia"]
world_asia.plot(column='gdp_md_est', cmap='Reds', legend=True)
plt.title("GDP of Countries in Asia")
plt.show()

Explanation: This filters the dataset to only include Asian countries, then plots them using a red color gradient based on GDP (gdp_md_est). The legend=True adds a scale.

To visualize population estimates (pop_est), we can use the cmap property to apply color gradients—lighter shades indicate lower values, darker shades indicate higher ones:

world.plot(column ='pop_est')

Output:

Explanation: This uses the pop_est column to apply a color gradient—automatically mapping population estimates to shades of blue (default).

The default population plot lacks visual clarity. We improve it by:

• Enlarging the figure,
• Plotting a black world map for contrast,
• Overlaying population data with a red color map.

fig, ax = plt.subplots(1, figsize =(16, 8))
world.plot(ax = ax, color ='black')
world.plot(ax = ax, column ='pop_est', cmap ='Reds')

Output:

Explanation: First, a larger figure is created. A base black world map provides contrast. Then, population data is overlaid using a red color map (cmap='Reds') to improve visual clarity.

To improve map readability, we add a color bar to show the data scale. Using the facecolor parameter (e.g., light blue) enhances visual contrast. We then apply make_axes_locatable from mpl_toolkits.axes_grid1 to create a side space for the color bar.

from mpl_toolkits.axes_grid1 import make_axes_locatable


fig, ax = plt.subplots(1, figsize =(16, 8),
                       facecolor ='lightblue')

world.plot(ax = ax, color ='black')
world.plot(ax = ax, column ='pop_est', cmap ='Reds',
           edgecolors ='grey')

# axis for the color bar
div = make_axes_locatable(ax)
cax = div.append_axes("right", size ="3 %", pad = 0.05)

# color bar
vmax = world.pop_est.max()
mappable = plt.cm.ScalarMappable(cmap ='Reds',
                                 norm = plt.Normalize(vmin = 0, vmax = vmax))
cbar = fig.colorbar(mappable, cax)

ax.axis('off')
plt.show()

Output:

Explanation: This enhances the map with a clear color bar indicating population ranges. make_axes_locatable creates space for it, while a light blue background improves aesthetics. Red shades represent population estimates, with a legend showing intensity levels.

Plotting Geospatial Data using GeoPandas

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4