nepalboundariespy 0.1.0

Nepal administrative boundaries dataset package

NP nepalboundaries.py

Administrative boundaries of Nepal for Python — country, province, district, municipality, and ward level, all in a single pip install.

Inspired by rgeoboundaries but tailored specifically for Nepal — standardised column names, WGS84 projection, and hierarchical filtering baked in.

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Installation

# From GitHub (latest)
pip install git+https://github.com/Lalitgis/nepalboundariespy.git

# From PyPI (when published)
pip install nepalboundariespy

# With visualisation extras (matplotlib + folium)
pip install nepalboundariespy[viz]

Requirements: Python ≥ 3.8, geopandas ≥ 0.10, shapely ≥ 1.8, pandas ≥ 1.0

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Quick start

import nepalboundariespy as nb

# All 7 provinces
provinces = nb.get_province()
provinces.plot(figsize=(12, 8), edgecolor="black", alpha=0.6)

# All 77 districts
districts = nb.get_district()

# Districts in one province
bagmati = nb.get_district(province="Bagmati")

# Wards in Kathmandu
ktm_wards = nb.get_ward(municipality="Kathmandu")
print(f"Kathmandu has {len(ktm_wards)} wards")

# Check what's available
nb.list_levels()
# {'country': 1, 'province': 7, 'district': 77, 'municipality': 753, 'ward': 6743}

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API reference

Boundary functions

Function	Description
get_country()	Nepal national boundary
get_province(province=None)	Provincial boundaries (7 total)
get_district(district=None, province=None)	District boundaries (77 total)
get_municipality(municipality=None, district=None, province=None)	Municipality boundaries (753 total)
get_ward(ward=None, municipality=None, district=None, province=None)	Ward boundaries (6 743 total)
get_multiple(levels=['province','district'])	Fetch multiple levels at once → dict

All functions accept a single string or a list of strings for each filter parameter.

Utility functions

Function	Description
list_levels()	Feature counts for all levels
info(level)	One-row summary (count, columns, CRS)
clear_cache()	Free in-memory cache

Convenience aliases

get_prov → get_province · get_dist → get_district · get_mun → get_municipality

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Examples

Filtering

import nepalboundariespy as nb

# Single filter
bhaktapur = nb.get_district("Bhaktapur")

# Multiple at once
two_provinces = nb.get_province(["Bagmati", "Gandaki"])

# Hierarchical filter — municipalities inside a specific district
patan_mun = nb.get_municipality(district="Lalitpur")

# All wards in province 3 (Bagmati)
bagmati_wards = nb.get_ward(province="Bagmati")

Plotting

import matplotlib.pyplot as plt
import nepalboundariespy as nb

fig, axes = plt.subplots(1, 2, figsize=(16, 6))

nb.get_province().plot(ax=axes[0], edgecolor="black", alpha=0.6)
axes[0].set_title("Provinces of Nepal")

nb.get_district(province="Bagmati").plot(ax=axes[1], edgecolor="black", alpha=0.6)
axes[1].set_title("Districts in Bagmati Province")

plt.tight_layout()
plt.show()

Interactive map with Folium

import folium
import nepalboundariespy as nb

m = folium.Map(location=[28.3949, 84.1240], zoom_start=7)

folium.GeoJson(
    nb.get_district().to_json(),
    name="Districts",
    tooltip=folium.GeoJsonTooltip(fields=["district_name", "province_name"]),
).add_to(m)

folium.LayerControl().add_to(m)
m.save("nepal_districts.html")

Spatial analysis

import geopandas as gpd
from shapely.geometry import Point
import nepalboundariespy as nb

# Point-in-polygon: which district contains Kathmandu Durbar Square?
point = gpd.GeoDataFrame(
    {"geometry": [Point(85.3157, 27.7044)]}, crs="EPSG:4326"
)
match = gpd.sjoin(point, nb.get_district(), how="left", predicate="within")
print(match["district_name"].values[0])   # → Kathmandu

# Neighbouring districts
ktm = nb.get_district("Kathmandu").iloc[0]
neighbours = nb.get_district()[nb.get_district().touches(ktm.geometry)]
print(neighbours["district_name"].tolist())

Export data

import nepalboundariespy as nb

districts = nb.get_district()

districts.to_file("nepal_districts.geojson", driver="GeoJSON")
districts.to_file("nepal_districts.gpkg", driver="GPKG")
districts.to_file("nepal_districts.shp")

# Attribute table only (no geometry)
districts.drop(columns="geometry").to_csv("nepal_districts.csv", index=False)

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Data structure

Nepal (Country)
└── 7 Provinces
    └── 77 Districts
        └── 753 Municipalities / Rural Municipalities + Procted Areas of Nepal
            └── 6743 Wards

All data is projected to WGS84 (EPSG:4326).

Column names by level

Level	Key columns
country	geometry
province	province_name, province_code, area_km2, geometry
district	district_name, district_code, province_name, area_km2, geometry
municipality	municipality_name, municipality_code, district_name, province_name, area_km2, geometry
ward	ward_number, municipality_name, district_name, province_name, geometry

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Preparing/updating the data

The package expects GeoJSON files in nepalboundaries/data/.
Use the provided script to convert your raw boundary files:

# Edit the paths inside the script, then run:
python scripts/data_preparation_python.py

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Repository structure

nepalboundariespy/                  ← GitHub repo root
├── nepalboundariespy/              ← Python package
│   ├── __init__.py
│   ├── core.py
│   └── data/
│       ├── country.geojson
│       ├── province.geojson
│       ├── district.geojson
│       ├── municipality.geojson
│       └── ward.geojson
├── tests/
│   └── test_nepalboundaries.py
├── examples/
│   └── examples_Python_usage.py
├── scripts/
│   └── data_preparation_python.py
├── setup.py
├── MANIFEST.in
├── LICENSE
├── CHANGELOG.md
└── README.md

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Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/my-improvement
3. Make your changes and add tests
4. Run the test suite: pytest tests/ -v
5. Open a Pull Request

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License

MIT — see LICENSE for details.

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Citation

@software{nepalboundariespy2026,
  title   = {nepalboundariespy: Administrative Boundaries of Nepal},
  author  = {BC Lalit},
  year    = {2026},
  url     = {https://github.com/Lalitgis/nepalboundariespy}
}

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Related resources

• Nepal CBS — Central Bureau of Statistics
• HDX Nepal — Humanitarian Data Exchange
• OpenStreetMap Nepal
• rgeoboundaries — R package that inspired this