electricsheepafrica/africa-et

--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - who-is-doing-what-and-where-3w-4w-5w - eth pretty_name: "Ethiopia - 3W Operational Presence December 2016" dataset_info: splits: - name: train num_examples: 3126 - name: test num_examples: 781 --- # Ethiopia - 3W Operational Presence December 2016 **Publisher:** OCHA Ethiopia · **Source:** [HDX](https://data.humdata.org/dataset/et) · **License:** `cc-by` · **Updated:** 2024-09-13 --- ## Abstract The Who Does What Where is a core humanitarian dataset for coordination. This data contains operational presence of humanitarian partners in Ethiopia at admin3 level by cluster in CSV format. Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the `start_date`, `end_date` column(s). Geographic scope: **ETH**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Humanitarian and development data | | **Unit of observation** | Subnational administrative unit observations | | **Rows (total)** | 3,908 | | **Columns** | 21 (2 numeric, 17 categorical, 2 datetime) | | **Train split** | 3,126 rows | | **Test split** | 781 rows | | **Geographic scope** | ETH | | **Publisher** | OCHA Ethiopia | | **HDX last updated** | 2024-09-13 | --- ## Variables **Geographic** — `organizationtype` (International NGO, Government, United Nations), `region` (Oromia, Somali, Amhara), `zone` (Fafan, Siti, Arsi), `woreda` (Jijiga, Shinile, Erer), `project_type` (Emergency, Emergency Response, Development). **Temporal** — `start_date`, `end_date`. **Identifier / Metadata** — `pcode` (ET050204, ET050103, ET050104), `project_title_description`, `esa_source`, `esa_processed`. **Other** — `organization` (NDRMC, WFP, Bureau of Women and Children Affairs), `implementing_partner_s` (NDRMC, WFP, BOWCA), `kebele_s_optional` (All, various , Various kebeles), `sector` (WASH, Food, Protection), `subsector` and 5 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-et") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `organization` | object | 0.0% | NDRMC, WFP, Bureau of Women and Children Affairs | | `implementing_partner_s` | object | 0.0% | NDRMC, WFP, BOWCA | | `organizationtype` | object | 0.0% | International NGO, Government, United Nations | | `region` | object | 0.0% | Oromia, Somali, Amhara | | `zone` | object | 0.0% | Fafan, Siti, Arsi | | `woreda` | object | 0.8% | Jijiga, Shinile, Erer | | `pcode` | object | 0.8% | ET050204, ET050103, ET050104 | | `kebele_s_optional` | object | 43.1% | All, various , Various kebeles | | `project_type` | object | 6.0% | Emergency, Emergency Response, Development | | `sector` | object | 0.0% | WASH, Food, Protection | | `subsector` | object | 6.0% | | | `project_title_description` | object | 41.9% | | | `activities` | object | 0.7% | | | `project_status` | object | 0.0% | | | `start_date` | datetime64[ns] | 1.3% | | | `end_date` | datetime64[ns] | 6.6% | | | `of_ben_planned` | float64 | 24.0% | 1.0 – 3352071.0 (mean 11699.137) | | `of_ben_achieved` | float64 | 53.4% | 0.0 – 1477315.0 (mean 5926.0172) | | `unit_of_measurement` | object | 63.2% | | | `esa_source` | object | 0.0% | | | `esa_processed` | object | 0.0% | | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `of_ben_planned` | 1.0 | 3352071.0 | 11699.137 | 2500.0 | | `of_ben_achieved` | 0.0 | 1477315.0 | 5926.0172 | 2000.0 | --- ## 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`. 1 column(s) with >80% missing values were removed: `hrd_non_hrd`. 172 exact duplicate rows were removed. 2 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 Ethiopia 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: `kebele_s_optional`, `project_title_description`, `of_ben_planned`, `of_ben_achieved`, `unit_of_measurement`. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/et) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_et, title = {Ethiopia - 3W Operational Presence December 2016}, author = {OCHA Ethiopia}, year = {2024}, url = {https://data.humdata.org/dataset/et}, 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.*