--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - tabular-classification - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - food-security - caf pretty_name: "Central African Republic Most Likely FEWS NET Acutely Food Insecure Population Estimates Data" dataset_info: splits: - name: train num_examples: 56 - name: test num_examples: 14 --- # Central African Republic Most Likely FEWS NET Acutely Food Insecure Population Estimates Data **Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/central_african_republic_most_likely_fewsnet_fipe) · **License:** `cc-by` · **Updated:** 2026-04-01 --- ## Abstract Central African Republic Most Likely FEWS NET Acutely Food Insecure Population Estimates Data from 2019 Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `projection_start`, `projection_end` column(s). Geographic scope: **CAF**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Food security and nutrition | | **Unit of observation** | First-level administrative unit observations | | **Rows (total)** | 71 | | **Columns** | 44 (10 numeric, 27 categorical, 7 datetime) | | **Train split** | 56 rows | | **Test split** | 14 rows | | **Geographic scope** | CAF | | **Publisher** | FEWS NET | | **HDX last updated** | 2026-04-01 | --- ## Variables **Geographic** — `country` (Central African Republic), `country_code` (CF), `fewsnet_region` (West Africa), `admin_0` (Central African Republic), `specialization_type` and 3 others. **Temporal** — `datacollectionperiod` (range 310323.0–344052.0), `reporting_date`. **Outcome / Measurement** — `phase`, `low_value` (range 100000.0–500000.0), `high_value` (range 499999.0–999999.0), `value` (range 100000.0–500000.0), `phase_name`. **Identifier / Metadata** — `source_organization` (FEWS NET), `source_document` (Food Assistance Outlook Brief), `geographic_unit_full_name` (Central African Republic), `geographic_unit_name` (Central African Republic), `fnid` (CF) and 8 others. **Other** — `geographic_group` (Middle Africa), `indicator_abbreviation`, `projection_start`, `projection_end`, `status` and 11 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-central-african-republic-most-likely-fewsnet-fipe") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `source_organization` | object | 0.0% | FEWS NET | | `source_document` | object | 0.0% | Food Assistance Outlook Brief | | `country` | object | 0.0% | Central African Republic | | `country_code` | object | 0.0% | CF | | `geographic_group` | object | 0.0% | Middle Africa | | `fewsnet_region` | object | 0.0% | West Africa | | `geographic_unit_full_name` | object | 0.0% | Central African Republic | | `geographic_unit_name` | object | 0.0% | Central African Republic | | `fnid` | object | 0.0% | CF | | `admin_0` | object | 0.0% | Central African Republic | | `phase` | object | 0.0% | | | `scenario_name` | object | 0.0% | | | `indicator_name` | object | 0.0% | | | `indicator_abbreviation` | object | 0.0% | | | `projection_start` | datetime64[ns] | 0.0% | | | `projection_end` | datetime64[ns] | 0.0% | | | `status` | object | 0.0% | | | `low_value` | float64 | 0.0% | 100000.0 – 500000.0 (mean 478169.0141) | | `high_value` | float64 | 0.0% | 499999.0 – 999999.0 (mean 904928.5775) | | `value` | float64 | 0.0% | 100000.0 – 500000.0 (mean 478169.0141) | | `id` | int64 | 0.0% | 33126746.0 – 37183541.0 (mean 33814935.1549) | | `datacollectionperiod` | int64 | 0.0% | 310323.0 – 344052.0 (mean 316950.4507) | | `datacollection` | int64 | 0.0% | 325936.0 – 354564.0 (mean 331718.5493) | | `scenario` | object | 0.0% | | | `geographic_unit` | int64 | 0.0% | 8014.0 – 8014.0 (mean 8014.0) | | `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) | | `datasourcedocument` | int64 | 0.0% | 6986.0 – 6986.0 (mean 6986.0) | | `dataseries` | int64 | 0.0% | 6932791.0 – 6932791.0 (mean 6932791.0) | | `dataseries_name` | object | 0.0% | | | `specialization_type` | object | 0.0% | | | `dataseries_specialization_type` | object | 0.0% | | | `data_usage_policy` | object | 0.0% | | | `created` | datetime64[ns] | 0.0% | | | `modified` | datetime64[ns] | 0.0% | | | `status_changed` | datetime64[ns] | 0.0% | | | `collection_status` | object | 0.0% | | | `collection_status_changed` | datetime64[ns] | 0.0% | | | `collection_schedule` | object | 0.0% | | | `reporting_date` | datetime64[ns] | 0.0% | | | `phase_name` | object | 0.0% | | | `population_range` | object | 0.0% | | | `description` | object | 0.0% | | | `esa_source` | object | 0.0% | | | `esa_processed` | object | 0.0% | | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `low_value` | 100000.0 | 500000.0 | 478169.0141 | 500000.0 | | `high_value` | 499999.0 | 999999.0 | 904928.5775 | 999999.0 | | `value` | 100000.0 | 500000.0 | 478169.0141 | 500000.0 | | `id` | 33126746.0 | 37183541.0 | 33814935.1549 | 33128744.0 | | `datacollectionperiod` | 310323.0 | 344052.0 | 316950.4507 | 310393.0 | | `datacollection` | 325936.0 | 354564.0 | 331718.5493 | 325971.0 | | `geographic_unit` | 8014.0 | 8014.0 | 8014.0 | 8014.0 | | `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 | | `datasourcedocument` | 6986.0 | 6986.0 | 6986.0 | 6986.0 | | `dataseries` | 6932791.0 | 6932791.0 | 6932791.0 | 6932791.0 | --- ## 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`. 7 column(s) with >80% missing values were removed: `admin_1`, `admin_2`, `admin_3`, `admin_4`, `pct_phase3`, `pct_phase4`.... 7 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 FEWS NET 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/central_african_republic_most_likely_fewsnet_fipe) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_central_african_republic_most_likely_fewsnet_fipe, title = {Central African Republic Most Likely FEWS NET Acutely Food Insecure Population Estimates Data}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/central_african_republic_most_likely_fewsnet_fipe}, 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.*