# data-structures

> data-structures skill

- Author: github-actions[bot]
- Repository: brolag/claude-code-templates
- Version: 20260105034122
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-07
- Source: https://github.com/brolag/claude-code-templates
- Web: https://mule.run/skillshub/@@brolag/claude-code-templates~data-structures:20260105034122

---

---
name: data-structures
description: data-structures skill
metadata:
  short-description: data-structures skill
  category: utilities
  source: claude-code-templates
---

# Data Structures

# GeoPandas Data Structures

## GeoSeries

A GeoSeries is a vector where each entry is a set of shapes corresponding to one observation (similar to a pandas Series but with geometric data).

```python
import geopandas as gpd
from shapely.geometry import Point, Polygon

# Create a GeoSeries from geometries
points = gpd.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)])

# Access geometric properties
points.area
points.length
points.bounds
```

## GeoDataFrame

A GeoDataFrame is a tabular data structure that contains a GeoSeries (similar to a pandas DataFrame but with geographic data).

```python
# Create from dictionary
gdf = gpd.GeoDataFrame({
    'name': ['Point A', 'Point B'],
    'value': [100, 200],
    'geometry': [Point(1, 1), Point(2, 2)]
})

# Create from pandas DataFrame with coordinates
import pandas as pd
df = pd.DataFrame({'x': [1, 2, 3], 'y': [1, 2, 3], 'name': ['A', 'B', 'C']})
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.x, df.y))
```

## Key Properties

- **geometry**: The active geometry column (can have multiple geometry columns)
- **crs**: Coordinate reference system
- **bounds**: Bounding box of all geometries
- **total_bounds**: Overall bounding box

## Setting Active Geometry

When a GeoDataFrame has multiple geometry columns:

```python
# Set active geometry column
gdf = gdf.set_geometry('other_geom_column')

# Check active geometry column
gdf.geometry.name
```

## Indexing and Selection

Use standard pandas indexing with spatial data:

```python
# Select by label
gdf.loc[0]

# Boolean indexing
large_areas = gdf[gdf.area > 100]

# Select columns
gdf[['name', 'geometry']]
```


## Usage

Invoke this skill with:
```
$data-structures [arguments]
```

Or let Codex auto-select based on your prompt.