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electricsheepasia/asia-settlements-pakistan-settlements

Pakistan - Population of Major Cities Publisher: OCHA Pakistan · Source: HDX · License: hdx-other · Updated: 2025-04-25 Abstract Major Cities Population Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-25. Geographic scope: PAK. Curated into ML-ready Parquet format by Electric Sheep Africa. Dataset Characteristics Domain Demographics and population Unit of observation Tabular records… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-settlements-pakistan-settlements.

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Dataset Card

Pakistan - Population of Major Cities

Publisher: OCHA Pakistan · Source: HDX · License: hdx-other · Updated: 2025-04-25


Abstract

Major Cities Population

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-25. Geographic scope: PAK.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).


Dataset Characteristics

DomainDemographics and population
Unit of observationTabular records
Rows (total)470
Columns12 (3 numeric, 9 categorical, 0 datetime)
Train split376 rows
Test split94 rows
Geographic scopePAK
PublisherOCHA Pakistan
HDX last updated2025-04-25

Variables

Identifier / Metadata — unnamed_1 (Karampur, Nowshera, Sāhīwāl), unnamed_2 (24 012, census 1981, 5 208 132), unnamed_3 (census 1998, 9 339 023, 5 143 495), unnamed_5 (range 321.0–13205339.0), unnamed_6 (range -1.27–4.66) and 6 others.

Other — pakistan_metropolitan_areas (range 1.0–467.0).


Quick Start

python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-settlements-pakistan-settlements")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

ColumnTypeNull %Range / Sample Values
pakistan_metropolitan_areasfloat640.6%1.0 – 467.0 (mean 234.0)
unnamed_1object0.4%Karampur, Nowshera, Sāhīwāl
unnamed_2object68.9%24 012, census 1981, 5 208 132
unnamed_3object2.1%census 1998, 9 339 023, 5 143 495
unnamed_5float640.6%321.0 – 13205339.0 (mean 124341.1927)
unnamed_6float6469.4%-1.27 – 4.66 (mean 2.61)
unnamed_7object13.8%32.64°N, 31.90°N, 25.07°N
unnamed_8object13.8%69.57°E, 72.47°E, 73.98°E
unnamed_9object1.1%Punjab, Sind, North-West Frontier
unnamed_10object63.2%Khairpur, Lilliani, name variants
esa_sourceobject0.0%HDX
esa_processedobject0.0%2026-05-06

Numeric Summary

ColumnMinMaxMeanMedian
pakistan_metropolitan_areas1.0467.0234.0234.0
unnamed_5321.013205339.0124341.192723246.0
unnamed_6-1.274.662.612.57

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) with >80% missing values were removed: `unnamed4`. 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.


Limitations

  • —Data originates from OCHA Pakistan and has not been independently validated by ESA.
  • —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
  • —The following columns have >20% missing values and should be treated with caution in modelling: unnamed_2, unnamed_6, unnamed_10.
  • —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

bibtex
@dataset{hdx_asia_settlements_pakistan_settlements,
  title     = {Pakistan - Population of Major Cities},
  author    = {OCHA Pakistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/pakistan-settlements},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.