map in python

3 min read 11-03-2025

Python offers several powerful libraries for creating and manipulating maps. This guide explores the capabilities of matplotlib, plotly, and folium, demonstrating how to generate various map visualizations. We'll cover creating basic maps, adding markers and layers, customizing aesthetics, and working with geographical data.

Getting Started: Installing Necessary Libraries

Before we dive into the code, ensure you have the required libraries installed. You can install them using pip:

pip install matplotlib plotly folium

These libraries provide different functionalities and levels of interactivity. matplotlib is great for static maps, plotly excels at interactive plots, and folium leverages Leaflet.js for highly interactive web maps.

Creating Basic Maps with Matplotlib

matplotlib provides a foundational approach to mapping, ideal for simple visualizations. Let's create a basic scatter plot on a map:

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature

# Create a map centered on a specific location (longitude, latitude)
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())

# Add coastlines and land
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.LAND)

# Add data points (replace with your data)
longitude = [-77.0369, -77.0439]
latitude = [38.9072, 38.8977]
plt.plot(longitude, latitude, 'ro', transform=ccrs.PlateCarree())

# Set map boundaries
ax.set_extent([-77.1, -77.0, 38.85, 38.95], crs=ccrs.PlateCarree())

plt.title('Simple Map with Matplotlib')
plt.show()

This code uses the cartopy library alongside matplotlib for better map projections and features. Remember to install cartopy (pip install cartopy). You'll replace the example longitude and latitude lists with your own geographical data.

Interactive Maps with Plotly

Plotly offers significantly enhanced interactivity. We can create a map showing multiple points with hover information:

import plotly.express as px

data = dict(
    longitude=[-77.0369, -77.0439, -77.00],
    latitude=[38.9072, 38.8977, 38.95],
    location=['Point A', 'Point B', 'Point C']
)

fig = px.scatter_mapbox(data, lat="latitude", lon="longitude", hover_name="location",
                        zoom=10, height=600,
                        center = {"lat": 38.9, "lon": -77.05})
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

This uses plotly.express, a high-level interface, making it easy to create visually appealing and interactive maps. Note the use of mapbox_style to leverage OpenStreetMap tiles. Remember to install Plotly (pip install plotly).

Powerful Web Maps using Folium

Folium provides a bridge to Leaflet.js, resulting in highly customizable and interactive web maps.

import folium

# Create a map centered on a specific location
m = folium.Map(location=[38.9, -77.05], zoom_start=12)

# Add markers
folium.Marker([38.9072, -77.0369], popup="Point A").add_to(m)
folium.Marker([38.8977, -77.0439], popup="Point B").add_to(m)

# Save the map to an HTML file
m.save("my_map.html")

This simple example demonstrates adding markers. Folium supports a wide range of features, including tilesets, popups, choropleth maps, and more.

Handling GeoJSON Data

Many geographical datasets are available in GeoJSON format. Let's see how to work with GeoJSON using Folium:

import folium
import json

# Load GeoJSON data (replace with your file path)
with open('your_geojson_file.geojson', 'r') as f:
    geojson_data = json.load(f)

# Create a map
m = folium.Map(location=[38.9, -77.05], zoom_start=12)

# Add GeoJSON layer
folium.GeoJson(geojson_data).add_to(m)

m.save("geojson_map.html")

Remember to replace 'your_geojson_file.geojson' with the actual path to your GeoJSON file. This code reads the GeoJSON data and adds it as a layer to the map.

Conclusion

Python offers versatile tools for mapping. matplotlib is suitable for static visualizations, plotly excels in interactive plots, and folium provides a powerful interface for creating dynamic web maps. The choice of library depends on your specific needs and desired level of interactivity. Experiment with these libraries and explore their extensive documentation to unlock their full potential for your mapping tasks. Remember to install the necessary libraries before running the code.