--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression multilinguality: monolingual size_categories: - n<1K tags: - tabular - xlsx - africa - central-african-republic - official-statistics - open-data configs: - config_name: default data_files: - split: train path: data/train-00000-of-00001.parquet pretty_name: "Central African Republic: Global Acute Malnutrition and Severe Acute Malnutrion data | Africa (Central African Republic official open data)" --- # Central African Republic: Global Acute Malnutrition and Severe Acute Malnutrion data | Africa (Central African Republic official open data) 357 rows - 1 Africa country - 2023 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) ![rows](https://img.shields.io/badge/rows-357-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![years](https://img.shields.io/badge/years-2023-orange) ![indicators](https://img.shields.io/badge/indicators-0-purple) ![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey) ## TL;DR This dataset packages one official `XLSX` resource from **Central African Republic** as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo. ## About the source - **Source:** [Central African Republic: Global Acute Malnutrition and Severe Acute Malnutrion data](https://data.humdata.org/dataset/nutrition-in-central-african-republic) - **Publisher:** OCHA Central African Republic - **Resource:** [PIN_Nutrition_RCA_19_10_2023](https://data.humdata.org/dataset/ebe59aa8-67bd-41fc-9190-0c73b46e1edb/resource/8d64a1b2-1b7f-49b0-805d-a915fa545376/download/pin_nutrition_rca_19_10_2023_final.xlsx) - **Format:** `XLSX` - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **Packaging mode:** `tabular_resource` ## Geographic coverage 1 Africa country: | Country | Rows | First year | Last year | Name | |---------|-----:|-----------:|----------:|------| | `CAF` | 357 | 2023 | 2023 | `Central African Republic` | ## Indicators or Resource Contents - This source file is packaged as a normalized tabular resource. ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `source_record_id` | `string` | Stable row identifier for tabular resources. | `8d64a1b2-1b7f-49b0-805d-a915fa545376:ocha-data:0` | | `country_iso3` | `category` | ISO3 country code. | `CAF` | | `country_name` | `category` | Country name. | `Central African Republic` | | `source_sheet` | `string` | Workbook sheet name, when the source is a spreadsheet. | `OCHA data` | | `year` | `Int64` | Observation year. | `2023` | | `pcode` | `string` | Source column. | `CF224` | | `sous_prefecture` | `string` | Source column. | `Abba` | | `popoulation_hno` | `float64` | Source column. | `31257.0` | | `population_0_59m` | `float64` | Source column. | `4860.4635` | | `population_fefa` | `float64` | Source column. | `2500.56` | | `pdi_site` | `float64` | Source column. | `0.0` | | `pdi_hors_site` | `float64` | Source column. | `1429.0` | | `retournes` | `float64` | Source column. | `0.0` | | `hote` | `float64` | Source column. | `29828.0` | | `pdi_site_2` | `float64` | Source column. | `0.0` | | `pdi_hors_site_2` | `float64` | Source column. | `0.0457177592219342` | | `retournes_2` | `float64` | Source column. | `0.0` | | `hote_2` | `float64` | Source column. | `0.9542822407780656` | | `pin_nut` | `float64` | Source column. | `7204.7385` | | `idp_site` | `float64` | Source column. | `0.0` | | `idp_fa` | `float64` | Source column. | `329.3845` | | `retourne` | `float64` | Source column. | `0.0` | | `hote_3` | `float64` | Source column. | `6875.353999999999` | | `severite_projetee` | `float64` | Source column. | `3.0` | | `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2023` | | `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2023` | | `source_period_label` | `category` | Human-readable period inferred from source resource metadata. | `2023` | | `source_provider` | `category` | Publishing organization. | `OCHA Central African Republic` | | `source_dataset` | `category` | Source package title. | `Central African Republic: Global Acute Malnutrition and Severe Acute Mal` | | `source_resource` | `category` | Source resource title. | `PIN_Nutrition_RCA_19_10_2023` | | `source_package_id` | `category` | CKAN package UUID. | `ebe59aa8-67bd-41fc-9190-0c73b46e1edb` | | `source_resource_id` | `category` | CKAN resource UUID. | `8d64a1b2-1b7f-49b0-805d-a915fa545376` | | `source_url` | `category` | Original source resource URL. | `https://data.humdata.org/dataset/ebe59aa8-67bd-41fc-9190-0c73b46e1edb/re` | | `license_id` | `category` | Source license identifier. | `cc-by` | | `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-14T23:28:30Z` | | `prefecture_16_prefectures_bangui` | `string` | Source column. | `` | | `sous_preftecture_71_sp_bangui_avec_ces_8_arrondissements` | `string` | Source column. | `` | | `analyser_y_n` | `string` | Source column. | `` | | `echelle_d_analyse_prefecture_1_sous_prefecture_2_idps_3` | `float64` | Source column. | `` | | `mag_utilisee_ptz_pb_smart_rapid_pb_depistage_serie_histo` | `string` | Source column. | `` | | `prevalence_mag_utilisee` | `float64` | Source column. | `` | | `phase_actulle_amn_sept_2023_a_fevrier_2024_faible_mag` | `float64` | Source column. | `` | | `phase_projetee_amn_mars_a_aout_2024_pic_malnutrition` | `float64` | Source column. | `` | | `niveau_de_confiance` | `string` | Source column. | `` | | `admin_2_pour_les_zones_d_interet_affectees` | `string` | Source column. | `` | | `donnees_demographiques` | `float64` | Source column. | `` | | `nombre_denfants_de_6_59_mois_souffrant_de_mam_dans_le_be` | `float64` | Source column. | `` | | `nombre_de_filles_de_6_59_mois_souffrant_le_mam_dans_le_b` | `float64` | Source column. | `` | | `nombre_de_garcons_de_6_59_mois_souffrant_de_mam_dans_le` | `float64` | Source column. | `` | | `nombre_denfants_de_0_59_mois_souffrant_de_mas_dans_le_be` | `float64` | Source column. | `` | | `nombre_de_filles_de_0_59_mois_souffrant_de_mas_dans_le_b` | `float64` | Source column. | `` | | `nombre_de_garcons_de_0_59_mois_souffrant_de_mas_dans_le` | `float64` | Source column. | `` | | `nombre_denfants_de_0_59_mois_souffrant_de_mas_ayant_beso` | `float64` | Source column. | `` | | `nombre_de_filles_de_0_59_mois_souffrant_de_mas_ayant_bes` | `float64` | Source column. | `` | | `nombre_de_garcons_de_0_59_mois_souffrant_de_mas_ayant_be` | `float64` | Source column. | `` | | `nombre_de_mas_chez_les_enfants_de_6_59_mois_ayant_besoin` | `float64` | Source column. | `` | | `nombre_de_filles_de_6_59_mois_souffrant_de_mas_ayant_bes` | `float64` | Source column. | `` | | `nombre_de_garcons_de_6_59_mois_souffrant_de_mas_ayant_be` | `float64` | Source column. | `` | | `nombre_denfants_de_6_59_mois_dans_le_besoin_de_supplemen` | `float64` | Source column. | `` | | `nombre_de_filles_de_6_59_mois_dans_le_besoin_de_suppleme` | `float64` | Source column. | `` | | `nombre_de_garcons_de_6_59_mois_dans_le_besoin_de_supplem` | `float64` | Source column. | `` | | `nombre_denfants_de_12_59_mois_ayant_besoin_d_un_deparasi` | `float64` | Source column. | `` | | `nombre_de_filles_de_12_59_mois_ayant_besoin_d_un_traitem` | `float64` | Source column. | `` | | `nombre_de_garcons_de_12_59_mois_ayant_besoin_d_un_traite` | `float64` | Source column. | `` | | `nombre_de_denfants_de_6_59_mois_a_examiner` | `float64` | Source column. | `` | | `nombre_de_filles_de_6_59_mois_a_examiner` | `float64` | Source column. | `` | | `nombre_de_garcons_de_6_59_mois_a_examiner2` | `float64` | Source column. | `` | | `nombre_denfants_de_6_23_mois_ayant_besoin_de_bfsp` | `float64` | Source column. | `` | | `nombre_de_filles_de_6_23_mois_ayant_besoin_d_un_bfsp` | `float64` | Source column. | `` | | `nombre_denfants_de_6_23_mois_ayant_besoin_d_un_bfsp` | `float64` | Source column. | `` | | `nombre_de_femmes_enceintes_et_allaitantes_a_examiner` | `float64` | Source column. | `` | | `pin` | `float64` | Source column. | `` | | `proportion_du_pin_par_rapport_a_la_population_totale` | `float64` | Source column. | `` | | `district_sanitaire` | `string` | Source column. | `` | | `enfants_de_moins_de_5_ans` | `float64` | Source column. | `` | | `cible` | `float64` | Source column. | `` | | `legendes` | `string` | Source column. | `` | | `column_2` | `string` | Source column. | `` | | `kaga_bandoro` | `float64` | Source column. | `` | | `birao` | `float64` | Source column. | `` | | `idps_briao` | `float64` | Source column. | `` | | `ouada_djale` | `float64` | Source column. | `` | | `yalinga` | `float64` | Source column. | `` | | `zangba` | `float64` | Source column. | `` | | `alindao` | `float64` | Source column. | `` | | `mingala` | `float64` | Source column. | `` | | `bereberati` | `float64` | Source column. | `` | | `bambari` | `float64` | Source column. | `` | | `ippy` | `float64` | Source column. | `` | | `bakala` | `float64` | Source column. | `` | | `koui` | `float64` | Source column. | `` | | `traitement` | `string` | Source column. | `` | | `global_pin` | `float64` | Source column. | `` | | `nc_pases_3_4` | `float64` | Source column. | `` | | `prevention` | `string` | Source column. | `` | | `global_pin_2` | `string` | Source column. | `` | | `nc_pases_3_4_2` | `string` | Source column. | `` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-central-african-republic-central-african-republic-global-acute-malnutrition-and-sev-9e0a0") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python sample_country = df[df["country_iso3"] == "CAF"] ``` ### Work with indicators ```python if "indicator_id" in df.columns: print(df["indicator_id"].value_counts().head()) sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns]) ``` ## Citation ```bibtex @misc{electric_sheep_africa_africa_central_african_republic_central_african_republic_global_acute_malnutriti_2023, title = {Central African Republic: Global Acute Malnutrition and Severe Acute Malnutrion data | Africa (Central African Republic official open data)}, author = {OCHA Central African Republic}, year = {2023}, url = {https://data.humdata.org/dataset/nutrition-in-central-african-republic}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-central-african-republic-central-african-republic-global-acute-malnutrition-and-sev-9e0a0}} } ``` ## License Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Original data (c) OCHA Central African Republic. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging. ## About Electric Sheep Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds. Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica) --- Provenance: ingested 2026-08-15 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/ebe59aa8-67bd-41fc-9190-0c73b46e1edb/resource/8d64a1b2-1b7f-49b0-805d-a915fa545376/download/pin_nutrition_rca_19_10_2023_final.xlsx