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skills/geopandas/references/visualization.md

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+# Mapping and Visualization
+
+GeoPandas provides plotting through matplotlib integration.
+
+## Basic Plotting
+
+```python
+# Simple plot
+gdf.plot()
+
+# Customize figure size
+gdf.plot(figsize=(10, 10))
+
+# Set colors
+gdf.plot(color='blue', edgecolor='black')
+
+# Control line width
+gdf.plot(edgecolor='black', linewidth=0.5)
+```
+
+## Choropleth Maps
+
+Color features based on data values:
+
+```python
+# Basic choropleth
+gdf.plot(column='population', legend=True)
+
+# Specify colormap
+gdf.plot(column='population', cmap='OrRd', legend=True)
+
+# Other colormaps: 'viridis', 'plasma', 'inferno', 'YlOrRd', 'Blues', 'Greens'
+```
+
+### Classification Schemes
+
+Requires: `uv pip install mapclassify`
+
+```python
+# Quantiles
+gdf.plot(column='population', scheme='quantiles', k=5, legend=True)
+
+# Equal interval
+gdf.plot(column='population', scheme='equal_interval', k=5, legend=True)
+
+# Natural breaks (Fisher-Jenks)
+gdf.plot(column='population', scheme='fisher_jenks', k=5, legend=True)
+
+# Other schemes: 'box_plot', 'headtail_breaks', 'max_breaks', 'std_mean'
+
+# Pass parameters to classification
+gdf.plot(column='population', scheme='quantiles', k=7,
+         classification_kwds={'pct': [10, 20, 30, 40, 50, 60, 70, 80, 90]})
+```
+
+### Legend Customization
+
+```python
+# Position legend outside plot
+gdf.plot(column='population', legend=True,
+         legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (1, 1)})
+
+# Horizontal legend
+gdf.plot(column='population', legend=True,
+         legend_kwds={'orientation': 'horizontal'})
+
+# Custom legend label
+gdf.plot(column='population', legend=True,
+         legend_kwds={'label': 'Population Count'})
+
+# Use separate axes for colorbar
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+divider = make_axes_locatable(ax)
+cax = divider.append_axes("right", size="5%", pad=0.1)
+gdf.plot(column='population', ax=ax, legend=True, cax=cax)
+```
+
+## Handling Missing Data
+
+```python
+# Style missing values
+gdf.plot(column='population',
+         missing_kwds={'color': 'lightgrey', 'edgecolor': 'red', 'hatch': '///',
+                      'label': 'Missing data'})
+```
+
+## Multi-Layer Maps
+
+Combine multiple GeoDataFrames:
+
+```python
+import matplotlib.pyplot as plt
+
+# Create base plot
+fig, ax = plt.subplots(figsize=(10, 10))
+
+# Add layers
+gdf1.plot(ax=ax, color='lightblue', edgecolor='black')
+gdf2.plot(ax=ax, color='red', markersize=5)
+gdf3.plot(ax=ax, color='green', alpha=0.5)
+
+plt.show()
+
+# Control layer order with zorder (higher = on top)
+gdf1.plot(ax=ax, zorder=1)
+gdf2.plot(ax=ax, zorder=2)
+```
+
+## Styling Options
+
+```python
+# Transparency
+gdf.plot(alpha=0.5)
+
+# Marker style for points
+points.plot(marker='o', markersize=50)
+points.plot(marker='^', markersize=100, color='red')
+
+# Line styles
+lines.plot(linestyle='--', linewidth=2)
+lines.plot(linestyle=':', color='blue')
+
+# Categorical coloring
+gdf.plot(column='category', categorical=True, legend=True)
+
+# Vary marker size by column
+gdf.plot(markersize=gdf['value']/1000)
+```
+
+## Map Enhancements
+
+```python
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(12, 8))
+gdf.plot(ax=ax, column='population', legend=True)
+
+# Add title
+ax.set_title('Population by Region', fontsize=16)
+
+# Add axis labels
+ax.set_xlabel('Longitude')
+ax.set_ylabel('Latitude')
+
+# Remove axes
+ax.set_axis_off()
+
+# Add north arrow and scale bar (requires separate packages)
+# See geopandas-plot or contextily for these features
+
+plt.tight_layout()
+plt.show()
+```
+
+## Interactive Maps
+
+Requires: `uv pip install folium`
+
+```python
+# Create interactive map
+m = gdf.explore(column='population', cmap='YlOrRd', legend=True)
+m.save('map.html')
+
+# Customize base map
+m = gdf.explore(tiles='OpenStreetMap', legend=True)
+m = gdf.explore(tiles='CartoDB positron', legend=True)
+
+# Add tooltip
+m = gdf.explore(column='population', tooltip=['name', 'population'], legend=True)
+
+# Style options
+m = gdf.explore(color='red', style_kwds={'fillOpacity': 0.5, 'weight': 2})
+
+# Multiple layers
+m = gdf1.explore(color='blue', name='Layer 1')
+gdf2.explore(m=m, color='red', name='Layer 2')
+folium.LayerControl().add_to(m)
+```
+
+## Integration with Other Plot Types
+
+GeoPandas supports pandas plot types:
+
+```python
+# Histogram of attribute
+gdf['population'].plot.hist(bins=20)
+
+# Scatter plot
+gdf.plot.scatter(x='income', y='population')
+
+# Box plot
+gdf.boxplot(column='population', by='region')
+```
+
+## Basemaps with Contextily
+
+Requires: `uv pip install contextily`
+
+```python
+import contextily as ctx
+
+# Reproject to Web Mercator for basemap compatibility
+gdf_webmercator = gdf.to_crs(epsg=3857)
+
+fig, ax = plt.subplots(figsize=(10, 10))
+gdf_webmercator.plot(ax=ax, alpha=0.5, edgecolor='k')
+
+# Add basemap
+ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik)
+# Other sources: ctx.providers.CartoDB.Positron, ctx.providers.Stamen.Terrain
+
+plt.show()
+```
+
+## Cartographic Projections with CartoPy
+
+Requires: `uv pip install cartopy`
+
+```python
+import cartopy.crs as ccrs
+
+# Create map with specific projection
+fig, ax = plt.subplots(subplot_kw={'projection': ccrs.Robinson()}, figsize=(15, 10))
+
+gdf.plot(ax=ax, transform=ccrs.PlateCarree(), column='population', legend=True)
+
+ax.coastlines()
+ax.gridlines(draw_labels=True)
+
+plt.show()
+```
+
+## Saving Figures
+
+```python
+# Save to file
+ax = gdf.plot()
+fig = ax.get_figure()
+fig.savefig('map.png', dpi=300, bbox_inches='tight')
+fig.savefig('map.pdf')
+fig.savefig('map.svg')
+```