electricsheepasia/asia-cod-em-npl

Nepal - Subnational Edge-matched Administrative Boundaries

Publisher: OCHA Field Information Services Section (FISS) · Source: HDX · License: cc-by-igo · Updated: 2025-06-24

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Abstract

Nepal administrative level 0-3 edge-matched gazetteer and boundary files.

COD-EM datasets do not replace the authoritative COD-AB available here; however COD-EM datasets may be preferred for cartographic purposes. See caveats.

These layers are suitable for database or GIS linkage to the Mauritania - Subnational Population Statistics tables using the ADM0, ADM1, ADM2, and ADM3_PCODE fields.

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the date, validon column(s). Geographic scope: NPL.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Time-series observations
Rows (total)	775
Columns	14 (1 numeric, 11 categorical, 2 datetime)
Train split	620 rows
Test split	155 rows
Geographic scope	NPL
Publisher	OCHA Field Information Services Section (FISS)
HDX last updated	2025-06-24

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Variables

Temporal — date.

Identifier / Metadata — adm3_pcode (NP0101301, NP0101401, NP0101402), adm2_pcode (NP0219, NP0215, NP0232), adm1_pcode (NP02, NP01, NP03), adm0_pcode (NP), validon and 2 others.

Other — adm3_en (Dhorpatan, Madi, Khaptad), adm3alt1en ( , Annapurna Conservation Area, Chitawan National Park), adm2_en (Sarlahi, Saptari, Rautahat), adm1_en (Madhesh, Koshi, Bagmati), adm0_en (Nepal) and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-cod-em-npl")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
adm3_en	object	0.0%	Dhorpatan, Madi, Khaptad
adm3_pcode	object	0.0%	NP0101301, NP0101401, NP0101402
adm3alt1en	object	0.0%	, Annapurna Conservation Area, Chitawan National Park
adm2_en	object	0.0%	Sarlahi, Saptari, Rautahat
adm2_pcode	object	0.0%	NP0219, NP0215, NP0232
adm1_en	object	0.0%	Madhesh, Koshi, Bagmati
adm1_pcode	object	0.0%	NP02, NP01, NP03
adm0_en	object	0.0%	Nepal
adm0_pcode	object	0.0%	NP
date	datetime64[ns]	0.0%
validon	datetime64[ns]	0.0%
area_sqkm	float64	0.0%	1.3312 – 2453.7472 (mean 190.5196)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
area_sqkm	1.3312	2453.7472	190.5196	123.0136

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 20 column(s) with >80% missing values were removed: unnamed_0, unnamed_1, unnamed_2, unnamed_3, unnamed_4, unnamed_5.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from OCHA Field Information Services Section (FISS) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_cod_em_npl,
  title     = {Nepal - Subnational Edge-matched Administrative Boundaries},
  author    = {OCHA Field Information Services Section (FISS)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/cod-em-npl},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.