geopandas

Name: geopandas
Author: K-Dense-AI

31kstars3.1kforks

How to install geopandas?

k-dense-ai/scientific-agent-skills/geopandas

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill geopandas

Installs into the current project.

Summary

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

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SKILL.md

1	# GeoPandas
2
3	GeoPandas extends pandas to enable spatial operations on geometric types. It combines the capabilities of pandas and shapely for geospatial data analysis.
4
5	## Installation
6
7	```bash
8	uv pip install geopandas
9	```
10
11	### Optional Dependencies
12
13	```bash
14	# For interactive maps
15	uv pip install folium
16
17	# For classification schemes in mapping
18	uv pip install mapclassify
19
20	# For faster I/O operations (2-4x speedup)
21	uv pip install pyarrow
22
23	# For PostGIS database support
24	uv pip install psycopg2
25	uv pip install geoalchemy2
26
27	# For basemaps
28	uv pip install contextily
29
30	# For cartographic projections
31	uv pip install cartopy
32	```
33
34	## Quick Start
35
36	```python
37	import geopandas as gpd
38
39	# Read spatial data
40	gdf = gpd.read_file("data.geojson")
41
42	# Basic exploration
43	print(gdf.head())
44	print(gdf.crs)
45	print(gdf.geometry.geom_type)
46
47	# Simple plot
48	gdf.plot()
49
50	# Reproject to different CRS
51	gdf_projected = gdf.to_crs("EPSG:3857")
52
53	# Calculate area (use projected CRS for accuracy)
54	gdf_projected['area'] = gdf_projected.geometry.area
55
56	# Save to file
57	gdf.to_file("output.gpkg")
58	```
59
60	## Core Concepts
61
62	### Data Structures
63
64	- GeoSeries: Vector of geometries with spatial operations
65	- GeoDataFrame: Tabular data structure with geometry column
66
67	See [data-structures.md](references/data-structures.md) for details.
68
69	### Reading and Writing Data
70
71	GeoPandas reads/writes multiple formats: Shapefile, GeoJSON, GeoPackage, PostGIS, Parquet.
72
73	```python
74	# Read with filtering
75	gdf = gpd.read_file("data.gpkg", bbox=(xmin, ymin, xmax, ymax))
76
77	# Write with Arrow acceleration
78	gdf.to_file("output.gpkg", use_arrow=True)
79	```
80
81	See [data-io.md](references/data-io.md) for comprehensive I/O operations.
82
83	### Coordinate Reference Systems
84
85	Always check and manage CRS for accurate spatial operations:
86
87	```python
88	# Check CRS
89	print(gdf.crs)
90
91	# Reproject (transforms coordinates)
92	gdf_projected = gdf.to_crs("EPSG:3857")
93
94	# Set CRS (only when metadata missing)
95	gdf = gdf.set_crs("EPSG:4326")
96	```
97
98	See [crs-management.md](references/crs-management.md) for CRS operations.
99
100	## Common Operations
101
102	### Geometric Operations
103
104	Buffer, simplify, centroid, convex hull, affine transformations:
105
106	```python
107	# Buffer by 10 units
108	buffered = gdf.geometry.buffer(10)
109
110	# Simplify with tolerance
111	simplified = gdf.geometry.simplify(tolerance=5, preserve_topology=True)
112
113	# Get centroids
114	centroids = gdf.geometry.centroid
115	```
116
117	See [geometric-operations.md](references/geometric-operations.md) for all operations.
118
119	### Spatial Analysis
120
121	Spatial joins, overlay operations, dissolve:
122
123	```python
124	# Spatial join (intersects)
125	joined = gpd.sjoin(gdf1, gdf2, predicate='intersects')
126
127	# Nearest neighbor join
128	nearest = gpd.sjoin_nearest(gdf1, gdf2, max_distance=1000)
129
130	# Overlay intersection
131	intersection = gpd.overlay(gdf1, gdf2, how='intersection')
132
133	# Dissolve by attribute
134	dissolved = gdf.dissolve(by='region', aggfunc='sum')
135	```
136
137	See [spatial-analysis.md](references/spatial-analysis.md) for analysis operations.
138
139	### Visualization
140
141	Create static and interactive maps:
142
143	```python
144	# Choropleth map
145	gdf.plot(column='population', cmap='YlOrRd', legend=True)
146
147	# Interactive map
148	gdf.explore(column='population', legend=True).save('map.html')
149
150	# Multi-layer map
151	import matplotlib.pyplot as plt
152	fig, ax = plt.subplots()
153	gdf1.plot(ax=ax, color='blue')
154	gdf2.plot(ax=ax, color='red')
155	```
156
157	See [visualization.md](references/visualization.md) for mapping techniques.
158
159	## Detailed Documentation
160
161	- [Data Structures](references/data-structures.md) - GeoSeries and GeoDataFrame fundamentals
162	- [Data I/O](references/data-io.md) - Reading/writing files, PostGIS, Parquet
163	- [Geometric Operations](references/geometric-operations.md) - Buffer, simplify, affine transforms
164	- [Spatial Analysis](references/spatial-analysis.md) - Joins, overlay, dissolve, clipping
165	- [Visualization](references/visualization.md) - Plotting, choropleth maps, interactive maps
166	- [CRS Management](references/crs-management.md) - Coordinate reference systems and projections
167
168	## Common Workflows
169
170	### Load, Transform, Analyze, Export
171
172	```python
173	# 1. Load data
174	gdf = gpd.read_file("data.shp")
175
176	# 2. Check and transform CRS
177	print(gdf.crs)
178	gdf = gdf.to_crs("EPSG:3857")
179
180	# 3. Perform analys

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geopandas

31kstars3.1kforks

View on GitHub

How to install geopandas?

k-dense-ai/scientific-agent-skills/geopandas

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill geopandas

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# GeoPandas
2
3	GeoPandas extends pandas to enable spatial operations on geometric types. It combines the capabilities of pandas and shapely for geospatial data analysis.
4
5	## Installation
6
7	```bash
8	uv pip install geopandas
9	```
10
11	### Optional Dependencies
12
13	```bash
14	# For interactive maps
15	uv pip install folium
16
17	# For classification schemes in mapping
18	uv pip install mapclassify
19
20	# For faster I/O operations (2-4x speedup)
21	uv pip install pyarrow
22
23	# For PostGIS database support
24	uv pip install psycopg2
25	uv pip install geoalchemy2
26
27	# For basemaps
28	uv pip install contextily
29
30	# For cartographic projections
31	uv pip install cartopy
32	```
33
34	## Quick Start
35
36	```python
37	import geopandas as gpd
38
39	# Read spatial data
40	gdf = gpd.read_file("data.geojson")
41
42	# Basic exploration
43	print(gdf.head())
44	print(gdf.crs)
45	print(gdf.geometry.geom_type)
46
47	# Simple plot
48	gdf.plot()
49
50	# Reproject to different CRS
51	gdf_projected = gdf.to_crs("EPSG:3857")
52
53	# Calculate area (use projected CRS for accuracy)
54	gdf_projected['area'] = gdf_projected.geometry.area
55
56	# Save to file
57	gdf.to_file("output.gpkg")
58	```
59
60	## Core Concepts
61
62	### Data Structures
63
64	- GeoSeries: Vector of geometries with spatial operations
65	- GeoDataFrame: Tabular data structure with geometry column
66
67	See [data-structures.md](references/data-structures.md) for details.
68
69	### Reading and Writing Data
70
71	GeoPandas reads/writes multiple formats: Shapefile, GeoJSON, GeoPackage, PostGIS, Parquet.
72
73	```python
74	# Read with filtering
75	gdf = gpd.read_file("data.gpkg", bbox=(xmin, ymin, xmax, ymax))
76
77	# Write with Arrow acceleration
78	gdf.to_file("output.gpkg", use_arrow=True)
79	```
80
81	See [data-io.md](references/data-io.md) for comprehensive I/O operations.
82
83	### Coordinate Reference Systems
84
85	Always check and manage CRS for accurate spatial operations:
86
87	```python
88	# Check CRS
89	print(gdf.crs)
90
91	# Reproject (transforms coordinates)
92	gdf_projected = gdf.to_crs("EPSG:3857")
93
94	# Set CRS (only when metadata missing)
95	gdf = gdf.set_crs("EPSG:4326")
96	```
97
98	See [crs-management.md](references/crs-management.md) for CRS operations.
99
100	## Common Operations
101
102	### Geometric Operations
103
104	Buffer, simplify, centroid, convex hull, affine transformations:
105
106	```python
107	# Buffer by 10 units
108	buffered = gdf.geometry.buffer(10)
109
110	# Simplify with tolerance
111	simplified = gdf.geometry.simplify(tolerance=5, preserve_topology=True)
112
113	# Get centroids
114	centroids = gdf.geometry.centroid
115	```
116
117	See [geometric-operations.md](references/geometric-operations.md) for all operations.
118
119	### Spatial Analysis
120
121	Spatial joins, overlay operations, dissolve:
122
123	```python
124	# Spatial join (intersects)
125	joined = gpd.sjoin(gdf1, gdf2, predicate='intersects')
126
127	# Nearest neighbor join
128	nearest = gpd.sjoin_nearest(gdf1, gdf2, max_distance=1000)
129
130	# Overlay intersection
131	intersection = gpd.overlay(gdf1, gdf2, how='intersection')
132
133	# Dissolve by attribute
134	dissolved = gdf.dissolve(by='region', aggfunc='sum')
135	```
136
137	See [spatial-analysis.md](references/spatial-analysis.md) for analysis operations.
138
139	### Visualization
140
141	Create static and interactive maps:
142
143	```python
144	# Choropleth map
145	gdf.plot(column='population', cmap='YlOrRd', legend=True)
146
147	# Interactive map
148	gdf.explore(column='population', legend=True).save('map.html')
149
150	# Multi-layer map
151	import matplotlib.pyplot as plt
152	fig, ax = plt.subplots()
153	gdf1.plot(ax=ax, color='blue')
154	gdf2.plot(ax=ax, color='red')
155	```
156
157	See [visualization.md](references/visualization.md) for mapping techniques.
158
159	## Detailed Documentation
160
161	- [Data Structures](references/data-structures.md) - GeoSeries and GeoDataFrame fundamentals
162	- [Data I/O](references/data-io.md) - Reading/writing files, PostGIS, Parquet
163	- [Geometric Operations](references/geometric-operations.md) - Buffer, simplify, affine transforms
164	- [Spatial Analysis](references/spatial-analysis.md) - Joins, overlay, dissolve, clipping
165	- [Visualization](references/visualization.md) - Plotting, choropleth maps, interactive maps
166	- [CRS Management](references/crs-management.md) - Coordinate reference systems and projections
167
168	## Common Workflows
169
170	### Load, Transform, Analyze, Export
171
172	```python
173	# 1. Load data
174	gdf = gpd.read_file("data.shp")
175
176	# 2. Check and transform CRS
177	print(gdf.crs)
178	gdf = gdf.to_crs("EPSG:3857")
179
180	# 3. Perform analys

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