geopandas — Анализ и обработка геопространственных данных в Python

--- name: geopandas description: Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats. license: BSD-3-Clause license metadata: skill-author: K-Dense Inc. --- # GeoPandas GeoPandas extends pandas to enable spatial operations on geometric types. It combines the capabilities of pandas and shapely for geospatial data analysis. ## Installation ```bash uv pip install geopandas ``` ### Optional Dependencies ```bash # For interactive maps uv pip install folium # For classification schemes in mapping uv pip install mapclassify # For faster I/O operations (2-4x speedup) uv pip install pyarrow # For PostGIS database support uv pip install psycopg2 uv pip install geoalchemy2 # For basemaps uv pip install contextily # For cartographic projections uv pip install cartopy ``` ## Quick Start ```python import geopandas as gpd # Read spatial data gdf = gpd.read_file("data.geojson") # Basic exploration print(gdf.head()) print(gdf.crs) print(gdf.geometry.geom_type) # Simple plot gdf.plot() # Reproject to different CRS gdf_projected = gdf.to_crs("EPSG:3857") # Calculate area (use projected CRS for accuracy) gdf_projected['area'] = gdf_projected.geometry.area # Save to file gdf.to_file("output.gpkg") ``` ## Core Concepts ### Data Structures - **GeoSeries**: Vector of geometries with spatial operations - **GeoDataFrame**: Tabular data structure with geometry column See [data-structures.md](references/data-structures.md) for details. ### Reading and Writing Data GeoPandas reads/writes multiple formats: Shapefile, GeoJSON, GeoPackage, PostGIS, Parquet. ```python # Read with filtering gdf = gpd.read_file("data.gpkg", bbox=(xmin, ymin, xmax, ymax)) # Write with Arrow acceleration gdf.to_file("output.gpkg", use_arrow=True) ``` See [data-io.md](references/data-io.md) for comprehensive I/O operations. ### Coordinate Reference Systems Always check and manage CRS for accurate spatial operations: ```python # Check CRS print(gdf.crs) # Reproject (transforms coordinates) gdf_projected = gdf.to_crs("EPSG:3857") # Set CRS (only when metadata missing) gdf = gdf.set_crs("EPSG:4326") ``` See [crs-management.md](references/crs-management.md) for CRS operations. ## Common Operations ### Geometric Operations Buffer, simplify, centroid, convex hull, affine transformations: ```python # Buffer by 10 units buffered = gdf.geometry.buffer(10) # Simplify with tolerance simplified = gdf.geometry.simplify(tolerance=5, preserve_topology=True) # Get centroids centroids = gdf.geometry.centroid ``` See [geometric-operations.md](references/geometric-operations.md) for all operations. ### Spatial Analysis Spatial joins, overlay operations, dissolve: ```python # Spatial join (intersects) joined = gpd.sjoin(gdf1, gdf2, predicate='intersects') # Nearest neighbor join nearest = gpd.sjoin_nearest(gdf1, gdf2, max_distance=1000) # Overlay intersection intersection = gpd.overlay(gdf1, gdf2, how='intersection') # Dissolve by attribute dissolved = gdf.dissolve(by='region', aggfunc='sum') ``` See [spatial-analysis.md](references/spatial-analysis.md) for analysis operations. ### Visualization Create static and interactive maps: ```python # Choropleth map gdf.plot(column='population', cmap='YlOrRd', legend=True) # Interactive map gdf.explore(column='population', legend=True).save('map.html') # Multi-layer map import matplotlib.pyplot as plt fig, ax = plt.subplots() gdf1.plot(ax=ax, color='blue') gdf2.plot(ax=ax, color='red') ``` See [visualization.md](references/visualization.md) for mapping techniques. ## Detailed Documentation - **[Data Structures](references/data-structures.md)** - GeoSeries and GeoDataFrame fundamentals - **[Data I/O](references/data-io.md)** - Reading/writing files, PostGIS, Parquet - **[Geometric Operations](references/geometric-operations.md)** - Buffer, simplify, affine transforms - **[Spatial Analysis](references/spatial-analysis.md)** - Joins, overlay, dissolve, clipping - **[Visualization](references/visualization.md)** - Plotting, choropleth maps, interactive maps - **[CRS Management](references/crs-management.md)** - Coordinate reference systems and projections ## Common Workflows ### Load, Transform, Analyze, Export ```python # 1. Load data gdf = gpd.read_file("data.shp") # 2. Check and transform CRS print(gdf.crs) gdf = gdf.to_crs("EPSG:3857") # 3. Perform analysis gdf['area'] = gdf.geometry.area buffered = gdf.copy() buffered['geometry'] = gdf.geometry.buffer(100) # 4. Export results gdf.to_file("results.gpkg", layer='original') buffered.to_file("results.gpkg", layer='buffered') ``` ### Spatial Join and Aggregate ```python # Join points to polygons points_in_polygons = gpd.sjoin(points_gdf, polygons_gdf, predicate='within') # Aggregate by polygon aggregated = points_in_polygons.groupby('index_right').agg({ 'value': 'sum', 'count': 'size' }) # Merge back to polygons result = polygons_gdf.merge(aggregated, left_index=True, right_index=True) ``` ### Multi-Source Data Integration ```python # Read from different sources roads = gpd.read_file("roads.shp") buildings = gpd.read_file("buildings.geojson") parcels = gpd.read_postgis("SELECT * FROM parcels", con=engine, geom_col='geom') # Ensure matching CRS buildings = buildings.to_crs(roads.crs) parcels = parcels.to_crs(roads.crs) # Perform spatial operations buildings_near_roads = buildings[buildings.geometry.distance(roads.union_all()) < 50] ``` ## Performance Tips 1. **Use spatial indexing**: GeoPandas creates spatial indexes automatically for most operations 2. **Filter during read**: Use `bbox`, `mask`, or `where` parameters to load only needed data 3. **Use Arrow for I/O**: Add `use_arrow=True` for 2-4x faster reading/writing 4. **Simplify geometries**: Use `.simplify()` to reduce complexity when precision isn't critical 5. **Batch operations**: Vectorized operations are much faster than iterating rows 6. **Use appropriate CRS**: Projected CRS for area/distance, geographic for visualization ## Best Practices 1. **Always check CRS** before spatial operations 2. **Use projected CRS** for area and distance calculations 3. **Match CRS** before spatial joins or overlays 4. **Validate geometries** with `.is_valid` before operations 5. **Use `.copy()`** when modifying geometry columns to avoid side effects 6. **Preserve topology** when simplifying for analysis 7. **Use GeoPackage** format for modern workflows (better than Shapefile) 8. **Set max_distance** in sjoin_nearest for better performance