electricsheepasia/asia-gender-philippines-other-0-0-0-0-0-0-0-0-0-0-0

Philippines - Employed Persons by major Industry group and by sex

Publisher: OCHA Philippines · Source: HDX · License: hdx-other · Updated: 2025-07-22

Abstract

This dataset shows the employed Persons by major Industry group and by sex

Each row in this dataset represents tabular records. 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

Variables

Identifier / Metadata — unnamed_1 (range 2004.0–2011.0), unnamed_2 (range 7.0–61882.0), unnamed_3 (range 11.3–7979.0), unnamed_4 (range 4.2–1121.0), unnamed_5 (range 8.1–3467.0) and 15 others.

Other — table_11_1 (HOUSEHOLD POPULATION 15 YEARS OLD AND OVER BY EMPLOYMENT STATUS, AND BY REGION, 2004 to 2011), table_11_1_continued (range 3.9–1875.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-gender-philippines-other-0-0-0-0-0-0-0-0-0-0-0")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

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. 9 exact duplicate rows were removed. 19 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 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.
• —The following columns have >20% missing values and should be treated with caution in modelling: table_11_1, unnamed_1, unnamed_2, unnamed_3, unnamed_4, unnamed_5, unnamed_6, unnamed_7....
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_gender_philippines_other_0_0_0_0_0_0_0_0_0_0_0,
  title     = {Philippines - Employed Persons by major Industry group and by sex},
  author    = {OCHA Philippines},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/philippines-other-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0},
  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.