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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](https://data.humdata.org/dataset/philippines-functional-difficulty-census-2020) · **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](https://huggingface.co/electricsheepafrica).*
---
## 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
```python
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](https://data.humdata.org/dataset/philippines-functional-difficulty-census-2020) for the publisher's own methodology notes and caveats.
---
## Citation
```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.* |