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asia-census-philippines-functional-difficulty-census

Tasks:
Tabular Classification
Languages:
English
Size:
100K - 1M
Tags:
africa
humanitarian
hdx
electric-sheep-africa
census
disability
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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - tabular-classification
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - census
  - disability
  - gender-and-age-disaggregated-data-gadd
  - phl
pretty_name: Philippines functional difficulty census 2020
dataset_info:
  splits:
    - name: train
      num_examples: 94632
    - name: test
      num_examples: 23658

Philippines functional difficulty census 2020

Publisher: OCHA Philippines · Source: HDX · License: cc-by · Updated: 2025-07-22

Abstract

Philippines: Household Population 5 Years Old and Over by Functional Difficulty, Severity, Age Group, Sex, and City/Municipality Census 2020

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-07-22. Geographic scope: PHL.

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

Dataset Characteristics

Domain Demographics and population
Unit of observation First-level administrative unit observations
Rows (total) 118,291
Columns 31 (17 numeric, 14 categorical, 0 datetime)
Train split 94,632 rows
Test split 23,658 rows
Geographic scope PHL
Publisher OCHA Philippines
HDX last updated 2025-07-22

Variables

Geographic — region (Region VIII (Eastern Visayas), Region IV-A (CALABARZON), Region VI (Western Visayas)), province (CEBU, BOHOL, PANGASINAN), disability ( Self-caring (washing all over or dressing), Seeing even if wearing eyeglasses, Hearing even if using a hearing aid), sex, household_population_5_years_old_and_over_with_functional_difficulty (range 0.0–158468.0) and 1 others.

Demographic — age_5_9 (range 0.0–1814.0), age_10_14 (range 0.0–3089.0), age_15_19 (range 0.0–5248.0), age_20_24 (range 0.0–6838.0), age_25_29 (range 0.0–7245.0) and 10 others.

Identifier / Metadata — regcode_new (PH0800000000, PH0400000000, PH0600000000), regcode_old (PH080000000, PH040000000, PH060000000), provcode_new (PH0702200000, PH0701200000, PH0105500000), provcode_old (PH072200000, PH015500000, PH071200000), muncode_new (PH1004217000, PH0305421000, PH0907225000) and 3 others.

Other — mun (SAN ISIDRO, SAN JOSE, SAN MIGUEL), status.

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-census-philippines-functional-difficulty-census")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
region object 0.0% Region VIII (Eastern Visayas), Region IV-A (CALABARZON), Region VI (Western Visayas)
province object 0.0% CEBU, BOHOL, PANGASINAN
mun object 0.0% SAN ISIDRO, SAN JOSE, SAN MIGUEL
regcode_new object 0.0% PH0800000000, PH0400000000, PH0600000000
regcode_old object 0.0% PH080000000, PH040000000, PH060000000
provcode_new object 0.0% PH0702200000, PH0701200000, PH0105500000
provcode_old object 0.5% PH072200000, PH015500000, PH071200000
muncode_new object 0.0% PH1004217000, PH0305421000, PH0907225000
muncode_old object 0.5% PH104217000, PH035421000, PH097225000
disability object 0.0% Self-caring (washing all over or dressing), Seeing even if wearing eyeglasses, Hearing even if using a hearing aid
sex object 0.0%
status object 0.0%
household_population_5_years_old_and_over_with_functional_difficulty int64 0.0% 0.0 – 158468.0 (mean 453.2272)
age_5_9 int64 0.0% 0.0 – 1814.0 (mean 7.2631)
age_10_14 int64 0.0% 0.0 – 3089.0 (mean 7.4244)
age_15_19 int64 0.0% 0.0 – 5248.0 (mean 8.6837)
age_20_24 int64 0.0% 0.0 – 6838.0 (mean 10.4521)
age_25_29 int64 0.0% 0.0 – 7245.0 (mean 11.2575)
age_30_34 int64 0.0% 0.0 – 6517.0 (mean 11.9206)
age_35_39 int64 0.0% 0.0 – 6357.0 (mean 13.5599)
age_40_44 int64 0.0% 0.0 – 10443.0 (mean 23.3327)
age_45_49 int64 0.0% 0.0 – 16164.0 (mean 32.821)
age_50_54 int64 0.0% 0.0 – 19872.0 (mean 41.9724)
age_55_59 int64 0.0% 0.0 – 20334.0 (mean 45.4262)
age_60_64 int64 0.0% 0.0 – 20893.0 (mean 51.9463)
age_65_69 int64 0.0% 0.0 – 17443.0 (mean 48.6366)
age_70_74 int64 0.0% 0.0 – 14032.0 (mean 45.0239)
age_75_79 int64 0.0% 0.0 – 10714.0 (mean 35.7994)
age_80_years_and_over int64 0.0% 0.0 – 11525.0 (mean 57.7073)
esa_source object 0.0%
esa_processed object 0.0%

Numeric Summary

Column Min Max Mean Median
household_population_5_years_old_and_over_with_functional_difficulty 0.0 158468.0 453.2272 93.0
age_5_9 0.0 1814.0 7.2631 2.0
age_10_14 0.0 3089.0 7.4244 2.0
age_15_19 0.0 5248.0 8.6837 2.0
age_20_24 0.0 6838.0 10.4521 2.0
age_25_29 0.0 7245.0 11.2575 2.0
age_30_34 0.0 6517.0 11.9206 3.0
age_35_39 0.0 6357.0 13.5599 3.0
age_40_44 0.0 10443.0 23.3327 3.0
age_45_49 0.0 16164.0 32.821 3.0
age_50_54 0.0 19872.0 41.9724 4.0
age_55_59 0.0 20334.0 45.4262 5.0
age_60_64 0.0 20893.0 51.9463 7.0
age_65_69 0.0 17443.0 48.6366 7.0
age_70_74 0.0 14032.0 45.0239 9.0

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. 6 exact duplicate rows were removed. 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 Philippines 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.

Citation 

@dataset{hdx_asia_census_philippines_functional_difficulty_census,
  title     = {Philippines functional difficulty census 2020},
  author    = {OCHA Philippines},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/philippines-functional-difficulty-census-2020},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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