nepal-geo-data 0.3.3

Comprehensive Python package offering Nepal's geographical data including GeoJSON for all 77 districts and 7 provinces. Ideal for GIS, mapping, and data visualization projects for Nepal.

Nepal Geo Data: Complete Geographical GeoJSON for Nepal

Nepal Geo Data is the most comprehensive Python package for Nepal's geographical data. It provides high-quality GeoJSON for all 77 Districts, 7 Provinces, and 753 Local Levels (Municipalities/Gaunpalikas), along with rich metadata (Nepali names, official codes).

Whether you are building a dashboard in Streamlit, analyzing spatial data with Pandas/GeoPandas, or creating interactive maps with Plotly/Folium, this package provides the map data you need with a simple API.

Key Features

• Complete Administrative Coverage:
  • 7 Provinces (Pradesh)
  • 77 Districts (Jilla)
  • 753 Local Levels (Palikas/Municipalities) - New in v0.2.0!
• Rich Metadata: Includes English names, Nepali names (e.g., "Kathmandu", "काठमाडौँ"), and official government codes.
• Zero Dependencies: Works with standard Python libraries.
• GIS Ready: Outputs standard FeatureCollections compatible with GeoPandas (gpd.read_file), Folium, and Plotly.
• Coordinate Reference System (CRS): WGS84 (EPSG:4326). All coordinates are in Latitude/Longitude.
• Backward Compatible: Preserves legacy behavior (UPPERCASE district keys) for existing projects.

Installation

Install via pip from PyPI:

pip install nepal-geo-data

Quick Start & Tutorials

1. Districts and Provinces

Get lists and details of administrative boundaries.

import nepal_geo_data

# Print the built-in guide
nepal_geo_data.help()

# Get all district names
districts = nepal_geo_data.get_districts()
print(districts) 
# Output: ['ACHHAM', 'ARGHAKHANCHI', ..., 'UDAYAPUR']

# Get details for a specific district (Case Insensitive)
ktm = nepal_geo_data.get_district("Kathmandu")
print(ktm['properties']['district_name_np'])  # Output: काठमाडौँ
print(ktm['properties']['province_name_en'])  # Output: Bagmati Province

# Get all districts in a specific province (e.g., Karnali Province / Province 6)
karnali_districts = nepal_geo_data.get_province_districts(6)

2. Working with Municipalities (New!)

The package now supports all 753 Local Levels.

Get All Municipalities

# Get a list of ALL municipalities in Nepal
all_munis = nepal_geo_data.get_municipalities()
print(f"Total Municipalities: {len(all_munis)}") # 753
print(all_munis[:5]) 

Filter Municipalities by District

The get_municipalities function accepts an optional district_name parameter.

# Get only municipalities in 'Jhapa' district
jhapa_munis = nepal_geo_data.get_municipalities("Jhapa")

print(f"Municipalities in Jhapa: {len(jhapa_munis)}")
for muni in jhapa_munis:
    print(f"- {muni}")
    
# Output:
# - Arjundhara
# - Bhadrapur
# - Birtamod
# ...

Get Municipality Details & Map Data

Search for a specific municipality to get its full GeoJSON (coordinates, codes, Nepali name).

# Get data for 'Kathmandu Metropolitan City'
# You can search by English name (case-insensitive)
ktm_metro = nepal_geo_data.get_municipality("Kathmandu Metropolitan City")

if ktm_metro:
    props = ktm_metro['properties']
    print(f"Name (EN): {props['gapa_napa']}")      # Kathmandu Metropolitan City
    print(f"Name (NP): {props['gapa_napa_np']}")  # काठमाडौँ महानगरपालिका
    print(f"Type: {props['type']}")                # Mahanagarpalika
    print(f"District ID: {props['district_code']}")
    
    # Access geometry for plotting
    # geometry = ktm_metro['geometry']

3. Integration with Plotly (Choropleth Maps)

Create stunning interactive maps using the GeoJSON data.

import plotly.express as px
from nepal_geo_data import get_geojson

# Load District GeoJSON
nepal_geojson = get_geojson()

# Dummy data dictionary
data_dict = {'KATHMANDU': 100, 'LALITPUR': 80, 'BHAKTAPUR': 60}
# Convert to list of dicts for Plotly
data = [{'District': k, 'Value': v} for k, v in data_dict.items()]

fig = px.choropleth_mapbox(
    data_frame=data,
    geojson=nepal_geojson,
    locations='District',
    featureidkey="properties.DISTRICT", # Using the backward-compatible key
    color='Value',
    center={"lat": 28.3949, "lon": 84.1240},
    mapbox_style="carto-positron",
    zoom=6,
    title="Nepal District Density Map"
)
fig.show()

📚 API Reference & Examples

Here is a complete list of all available functions with examples.

1. Administrative Lists

get_districts()

Returns a list of all 77 districts in Nepal.

import nepal_geo_data
districts = nepal_geo_data.get_districts()
print(districts[:5]) 
# output: ['ACHHAM', 'ARGHAKHANCHI', 'BAGLUNG', 'BAITADI', 'BAJHANG']

get_provinces()

Returns a list of metadata for all 7 provinces.

provinces = nepal_geo_data.get_provinces()
for p in provinces:
    print(f"{p['province_name_en']} (ID: {p['province_code']})")

get_province_districts(province_id)

Returns a list of districts within a specific province (IDs 1-7).

# Get districts in Karnali Province (ID: 6)
karnali = nepal_geo_data.get_province_districts(6)
print(karnali)

get_municipalities(district_name=None)

Returns a list of all 753 municipalities. Optionally filter by district.

# All municipalities
all_munis = nepal_geo_data.get_municipalities()

# Filter by district (e.g., Chitwan)
chitwan_munis = nepal_geo_data.get_municipalities("Chitwan")
print(chitwan_munis)

get_wards(municipality_name)

Returns a list of ward numbers for a specific municipality.

wards = nepal_geo_data.get_wards("Kathmandu Metropolitan City")
print(f"Total Wards: {len(wards)}") # 32
print(wards) # [1, 2, 3, ..., 32]

2. Search & Details (Rich Metadata)

get_district(name)

Get full details including geometry, headquarters, and codes.

data = nepal_geo_data.get_district("Kaski")
props = data['properties']
print(f"District: {props['district_name_en']}")
print(f"Headquarter: {props['headquarter']}")
print(f"Area: {props['area_sq_km']} sq km")

get_municipality(name)

Get full details including wards, website, and geometry.

muni = nepal_geo_data.get_municipality("Pokhara Lekhnath Metropolitan City")
props = muni['properties']
print(f"Website: {props.get('website')}")
print(f"Wards: {props.get('wards')}")

get_boundaries(district_name)

Get only the geometry (Polygon/MultiPolygon) for mapping.

geom = nepal_geo_data.get_boundaries("Mustang")
# Use with Shapely or GeoPandas
# shape = shapely.geometry.shape(geom)

3. Raw GeoJSON Data

Access the raw FeatureCollections directly for use with GIS libraries like GeoPandas.

get_geojson()

All districts as a FeatureCollection.

import geopandas as gpd
geojson = nepal_geo_data.get_geojson()
gdf = gpd.GeoDataFrame.from_features(geojson)
gdf.plot()

get_provinces_geojson()

All provinces as a FeatureCollection.

get_municipalities_geojson()

All municipalities as a FeatureCollection.

4. Interactive Help

help()

Prints a quick guide to the console.

nepal_geo_data.help()

🚀 Advanced Usage & Integrations

1. Plotly Integration: Mapping Provinces

Here is a complete example of how to create a Choropleth map of Nepal's 7 Provinces using plotly.express.

import plotly.express as px
from nepal_geo_data import get_provinces_geojson

# 1. Get Province GeoJSON
province_geojson = get_provinces_geojson()

# 2. Prepare Data (Example: Population or GDP by Province)
# Note: Keys must match the Feature properties (e.g., 'province_name_en' or 'province_code')
data = [
    {'Province': 'Koshi Province', 'Value': 10},
    {'Province': 'Madhesh Province', 'Value': 20},
    {'Province': 'Bagmati Province', 'Value': 50},
    {'Province': 'Gandaki Province', 'Value': 30},
    {'Province': 'Lumbini Province', 'Value': 40},
    {'Province': 'Karnali Province', 'Value': 5},
    {'Province': 'Sudurpaschim Province', 'Value': 15},
]

# 3. Create Map
fig = px.choropleth_mapbox(
    data_frame=data,
    geojson=province_geojson,
    locations='Province',             # Key in 'data'
    featureidkey="properties.province_name_en", # Key in GeoJSON properties
    color='Value',
    center={"lat": 28.3949, "lon": 84.1240},
    mapbox_style="carto-positron",
    zoom=6,
    title="Nepal Province-wise Distribution",
    opacity=0.7
)

fig.update_layout(margin={"r":0,"t":40,"l":0,"b":0})
fig.show()

2. GeoPandas Integration (Static Maps)

Easily convert data for spatial analysis.

import geopandas as gpd
from nepal_geo_data import get_municipalities_geojson

# Load all 753 municipalities into a GeoDataFrame
gdf = gpd.GeoDataFrame.from_features(get_municipalities_geojson())

# Filter for a specific district
ktm_munis = gdf[gdf['district_name_en'] == 'Kathmandu']

# Plot
ktm_munis.plot(column='type', legend=True, figsize=(10, 10))

3. Folium Integration (Interactive Web Maps)

import folium
from nepal_geo_data import get_boundaries

# Create base map
m = folium.Map(location=[28.3949, 84.1240], zoom_start=7)

# Add District Boundary (e.g., Mustang)
mustang_geom = get_boundaries("Mustang")
folium.GeoJson(
    mustang_geom,
    style_function=lambda x: {'color': 'red', 'fillOpacity': 0.3}
).add_to(m)

m.save("nepal_map.html")

Contributing

We welcome contributions! GitHub Repository: https://github.com/bedbyaspokhrel/nepal-geo-data

License

MIT License - see the LICENSE file for details.

---

Keywords: Nepal GIS, Nepal Map Python, GeoJSON Nepal, Nepal Districts Data, Nepal Municipalities, Local Level Nepal, Gaunpalika, Nepal Provinces JSON, Python GIS Nepal.