| --- |
| 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: |
| - tabular-classification |
| - other |
| task_ids: [] |
| tags: |
| - africa |
| - humanitarian |
| - hdx |
| - electric-sheep-africa |
| - covid-19 |
| - disease |
| - epidemics-outbreaks |
| - fatalities |
| - gender |
| - health |
| - hxl |
| - bfa |
| pretty_name: "Burkina Faso: Coronavirus (Covid-19) City level" |
| dataset_info: |
| splits: |
| - name: train |
| num_examples: 1911 |
| - name: test |
| num_examples: 477 |
| --- |
| |
| # Burkina Faso: Coronavirus (Covid-19) City level |
|
|
| **Publisher:** HERA - Humanitarian Emergency Response Africa · **Source:** [HDX](https://data.humdata.org/dataset/burkinafaso_covid19_city-level) · **License:** `cc-by-igo` · **Updated:** 2025-05-05 |
|
|
| --- |
|
|
| ## Abstract |
|
|
| Covid-19 data at the city level in Burkina Faso - Infections (new cases, gender), Deaths, Recoveries + Urban / Rural locations. |
|
|
| Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: **BFA**. |
|
|
| *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* |
|
|
| --- |
|
|
| ## Dataset Characteristics |
|
|
| | | | |
| |---|---| |
| | **Domain** | Public health | |
| | **Unit of observation** | First-level administrative unit observations | |
| | **Rows (total)** | 2,389 | |
| | **Columns** | 3 (0 numeric, 3 categorical, 0 datetime) | |
| | **Train split** | 1,911 rows | |
| | **Test split** | 477 rows | |
| | **Geographic scope** | BFA | |
| | **Publisher** | HERA - Humanitarian Emergency Response Africa | |
| | **HDX last updated** | 2025-05-05 | |
|
|
| --- |
|
|
| ## Variables |
|
|
| **Geographic** — `id_date_iso_3_pays_id_pays_region_id_region_villes_id_villes_communes_type_contaminés_décès_guéris_femme_homme_genre_non_spécifié_source` (;#date;#country+code;#country+name;;#region+name;;#adm2+name;;;#affected+infected;#affected+killed;#affected+recovered;#affected+woman;#affected+man;#affected+gendernotspecified;;;;;;;;;;;;;;;, 1588;13/03/2021;BFA;Burkina Faso;16;Haut-Bassins;216;Bobo-Dioulasso;15;Urbain;4;0;;;;;Ministère de la Santé - Burkina Faso;;;;;;;;;;;;;;, 1590;13/03/2021;BFA;Burkina Faso;16;Haut-Bassins;216;Houndé;16;Urbain;1;0;;;;;Ministère de la Santé - Burkina Faso;;;;;;;;;;;;;;). |
|
|
| **Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-04). |
|
|
| --- |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("electricsheepafrica/africa-burkinafaso-covid19-city-level") |
| train = ds["train"].to_pandas() |
| test = ds["test"].to_pandas() |
| |
| print(train.shape) |
| train.head() |
| ``` |
|
|
| --- |
|
|
| ## Schema |
|
|
| | Column | Type | Null % | Range / Sample Values | |
| |---|---|---|---| |
| | `id_date_iso_3_pays_id_pays_region_id_region_villes_id_villes_communes_type_contaminés_décès_guéris_femme_homme_genre_non_spécifié_source` | object | 0.0% | ;#date;#country+code;#country+name;;#region+name;;#adm2+name;;;#affected+infected;#affected+killed;#affected+recovered;#affected+woman;#affected+man;#affected+gendernotspecified;;;;;;;;;;;;;;;, 1588;13/03/2021;BFA;Burkina Faso;16;Haut-Bassins;216;Bobo-Dioulasso;15;Urbain;4;0;;;;;Ministère de la Santé - Burkina Faso;;;;;;;;;;;;;;, 1590;13/03/2021;BFA;Burkina Faso;16;Haut-Bassins;216;Houndé;16;Urbain;1;0;;;;;Ministère de la Santé - Burkina Faso;;;;;;;;;;;;;; | |
| | `esa_source` | object | 0.0% | HDX | |
| | `esa_processed` | object | 0.0% | 2026-04-04 | |
|
|
| --- |
|
|
| ## Numeric Summary |
|
|
| | Column | Min | Max | Mean | Median | |
| |---|---|---|---|---| |
| _No numeric columns._ |
|
|
| --- |
|
|
| ## 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`. 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 HERA - Humanitarian Emergency Response Africa 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/burkinafaso_covid19_city-level) for the publisher's own methodology notes and caveats. |
| |
| --- |
| |
| ## Citation |
| |
| ```bibtex |
| @dataset{hdx_africa_burkinafaso_covid19_city_level, |
| title = {Burkina Faso: Coronavirus (Covid-19) City level}, |
| author = {HERA - Humanitarian Emergency Response Africa}, |
| year = {2025}, |
| url = {https://data.humdata.org/dataset/burkinafaso_covid19_city-level}, |
| 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.* |