Learn Advanced Plotting with Matplotlib and Seaborn

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When you work with spatial data, effective visualization is essential for uncovering patterns and communicating insights. Python's matplotlib and seaborn libraries provide powerful tools for enhancing your spatial plots, allowing you to customize colors, add informative legends, set descriptive titles, and overlay multiple data layers. By mastering these techniques, you can create clear, compelling maps and visualizations that highlight important spatial relationships.

Customizing spatial plots often starts with controlling the appearance of your data. You can specify colors to represent different categories or values, adjust transparency to reveal overlapping features, and use legends to explain your visual encodings. Adding titles and axis labels helps viewers interpret your plots quickly. Overlaying multiple layers—such as roads on top of administrative boundaries—can provide deeper context for your spatial analysis.


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import geopandas as gpd
import matplotlib.pyplot as plt


url = "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
world = gpd.read_file(url)

# Create a plot with customizations
fig, ax = plt.subplots(figsize=(12, 8))

# Plot countries, coloring by continent 
world.plot(column='CONTINENT', 
           cmap='Set2', 
           edgecolor='black', 
           linewidth=0.5, 
           legend=True, 
           ax=ax, 
           alpha=0.8)

# Overlay country boundaries with a thicker line for emphasis
world.boundary.plot(ax=ax, color='gray', linewidth=1.5)

# Add a title and axis labels
ax.set_title('World Countries by Continent', fontsize=18)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')

# Customize legend position so it doesn't overlap the map
leg = ax.get_legend()
if leg:
    leg.set_bbox_to_anchor((1.15, 0.5))

plt.tight_layout()
plt.show()

Note

The column name in the official dataset is CONTINENT

Beyond map-based visualizations, you may also want to explore the distribution of spatial attributes — such as area, population, or density — using statistical plots. Seaborn is especially useful for this purpose, letting you create histograms, scatter plots, and more, all with attractive default styles and easy customization. By combining seaborn with spatial data, you can quickly identify trends and outliers in your datasets, supporting deeper spatial analysis.


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import geopandas as gpd
import seaborn as sns
import matplotlib.pyplot as plt

# Load sample data directly from the official URL
url = "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
world = gpd.read_file(url)

# The official dataset doesn't have a pre-made 'area' column, so we calculate it.
# Note: This calculates area in decimal degrees based on the WGS84 projection.
world['area'] = world.geometry.area

# Plot a histogram of country areas
plt.figure(figsize=(10, 6))
# 'area' is our new custom column, kde=True adds the density curve
sns.histplot(world['area'], bins=30, color='skyblue', kde=True)
plt.title('Distribution of Country Areas')
plt.xlabel('Area (sq. degrees)')
plt.ylabel('Number of Countries')
plt.tight_layout()
plt.show()

# Plot a scatter plot of population vs. area
plt.figure(figsize=(10, 6))
# Adjusted column names to match the official dataset: 'POP_EST' and 'CONTINENT'
sns.scatterplot(x='area', y='POP_EST', data=world, hue='CONTINENT', palette='Set2')
plt.title('Country Population vs. Area by Continent')
plt.xlabel('Area (sq. degrees)')
plt.ylabel('Estimated Population')
plt.legend(title='Continent', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

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