pablodiegoo/geoprocessing-brazil
src/datapro/data/skills/geoprocessing-brazil/SKILL.md
Normalization and state-mapping of municipal data and generation of professional choropleth maps of Brazil (UF/State level). Use for: (1) Detecting city/state strings and normalizing names, (2) Attaching regional metadata, (3) Generating professional maps integrating survey data with shapefiles.
$ install --global
skillsauthnpx skillsauth add pablodiegoo/data-pro-skill geoprocessing-brazilInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- geoprocessing-brazil
- description:
- Normalization and state-mapping of municipal data and generation of professional choropleth maps of Brazil (UF/State level). Use for: (1) Detecting city/state strings and normalizing names, (2) Attaching regional metadata, (3) Generating professional maps integrating survey data with shapefiles.
Geoprocessing Brazil
This skill unifies the process of geographic data treatment and map visualization for the Brazilian territory.
1. Municipality Normalization
Cleaning and standardizing city and state names from diverse strings (e.g., "São Paulo - SP", "Rio/RJ").
- Script:
scripts/municipality_mapper.py - Functionality: Detects, cleans, and attaches metadata (Region, UF, IBGE Code if available).
2. Choropleth Map Generation
Creation of thematic maps based on scores or categories by State.
Capabilities:
- Automatic Merge: Joins your data (DataFrame) with Brazil shapefiles.
- Custom Styling: Sebrae-friendly color palettes and legend control.
Usage:
- Script:
scripts/map_generator.py
from scripts.map_generator import generate_brazil_map
# Example: Generate satisfaction map by UF
generate_brazil_map(df, score_col="Score", title="Satisfaction by State", filename="brazil_map.png")
3. Recommended Workflow
- Cleaning: Use
municipality_mapperto ensure the UF column is standardized (e.g., 'SP', 'RJ'). - Aggregation: Group your data by the UF column (Example:
df.groupby('UF')['Score'].mean()). - Visualization: Pass the resulting DataFrame to
map_generator.
[!NOTE] For city-level maps (beyond UF), check for specific shapefile availability or IBGE APIs.
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