electricsheepasia/asia-census-philippines-functional-difficulty-census

Philippines functional difficulty census 2020

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

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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.

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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

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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.

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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()

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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%

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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

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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. 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.

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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.

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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)}
}

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