Add README.md

README.md

@@ -1,82 +1,183 @@
 ---
+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:
-  features:
-  - name: region
-    dtype: string
-  - name: province
-    dtype: string
-  - name: mun
-    dtype: string
-  - name: regcode_new
-    dtype: string
-  - name: regcode_old
-    dtype: string
-  - name: provcode_new
-    dtype: string
-  - name: provcode_old
-    dtype: string
-  - name: muncode_new
-    dtype: string
-  - name: muncode_old
-    dtype: string
-  - name: disability
-    dtype: string
-  - name: sex
-    dtype: string
-  - name: status
-    dtype: string
-  - name: household_population_5_years_old_and_over_with_functional_difficulty
-    dtype: int64
-  - name: age_5_9
-    dtype: int64
-  - name: age_10_14
-    dtype: int64
-  - name: age_15_19
-    dtype: int64
-  - name: age_20_24
-    dtype: int64
-  - name: age_25_29
-    dtype: int64
-  - name: age_30_34
-    dtype: int64
-  - name: age_35_39
-    dtype: int64
-  - name: age_40_44
-    dtype: int64
-  - name: age_45_49
-    dtype: int64
-  - name: age_50_54
-    dtype: int64
-  - name: age_55_59
-    dtype: int64
-  - name: age_60_64
-    dtype: int64
-  - name: age_65_69
-    dtype: int64
-  - name: age_70_74
-    dtype: int64
-  - name: age_75_79
-    dtype: int64
-  - name: age_80_years_and_over
-    dtype: int64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 34741909
-    num_examples: 94632
-  - name: test
-    num_bytes: 8683527
-    num_examples: 23659
-  download_size: 6950273
-  dataset_size: 43425436
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - 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.*