electricsheepafrica/africa-health-facilities-2012

Côte d'Ivoire Health facilities 2012

Publisher: OCHA West and Central Africa (ROWCA) · Source: HDX · License: other-pd-nr · Updated: 2021-09-23

---

Abstract

List of Health facilities in Côte d'Ivoire provided by the Ministry of Health, DIPE (Direction de l'Information, de la Planification et de l'Evaluation)

Each row in this dataset represents individual-level records. Data was last updated on HDX on 2021-09-23. Geographic scope: CIV.

Curated into ML-ready Parquet format by Electric Sheep Africa.

---

Dataset Characteristics

Domain	Public health
Unit of observation	Individual-level records
Rows (total)	1,933
Columns	26 (3 numeric, 23 categorical, 0 datetime)
Train split	1,546 rows
Test split	386 rows
Geographic scope	CIV
Publisher	OCHA West and Central Africa (ROWCA)
HDX last updated	2021-09-23

---

Variables

Geographic — pays (COTE D'VOIRE), code_pays (CIV), region_san (PORO-TCHOLOGO-BAGOUE, BOUKANI-GONTOUGO, AGNEBY-TIASSA-ME), code_region (SAV, ZAN, AGN), district_s (GAGNOA, KORHOGO, BONDOUKOU) and 3 others.

Temporal — date_ouver, date_ouv_1.

Identifier / Metadata — code_distr (BON, BOU, GAG), code_epid (CIV-FRO-GAG-12, CIV-SAV-KOR-12, CIV-ZAN-BON-12), code_etabl (range 19.0–8840.0), esa_source, esa_processed.

Other — directeur (KARAMOKO Fofana Ibrahima, N'GUESSAN Yao, KONAN Ahou), matricule (232.476-X, 147.096R, 160.056-H), directeur1 (BOUKALO Kouassi Célestin, GOKOU Oboho Sébastien, TOA Bi Laurent), matricule1, localite and 6 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-2012")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
pays	object	0.0%	COTE D'VOIRE
code_pays	object	0.0%	CIV
region_san	object	0.0%	PORO-TCHOLOGO-BAGOUE, BOUKANI-GONTOUGO, AGNEBY-TIASSA-ME
code_region	object	0.0%	SAV, ZAN, AGN
directeur	object	0.0%	KARAMOKO Fofana Ibrahima, N'GUESSAN Yao, KONAN Ahou
matricule	object	0.0%	232.476-X, 147.096R, 160.056-H
district_s	object	0.0%	GAGNOA, KORHOGO, BONDOUKOU
code_distr	object	0.0%	BON, BOU, GAG
code_epid	object	0.0%	CIV-FRO-GAG-12, CIV-SAV-KOR-12, CIV-ZAN-BON-12
directeur1	object	0.0%	BOUKALO Kouassi Célestin, GOKOU Oboho Sébastien, TOA Bi Laurent
matricule1	object	0.0%
localite	object	0.0%
nom	object	0.0%
etablissem	object	0.0%
typologie	object	0.0%
nature	object	0.0%
code_etabl	float64	5.3%	19.0 – 8840.0 (mean 989.1956)
fonct	object	0.0%
date_ouver	object	0.0%
date_ouv_1	object	0.0%
observatio	object	0.0%
statut	object	0.0%
latitude	float64	0.0%	4.3735 – 10.6213 (mean 6.9898)
longitude	float64	0.0%	-8.4996 – -2.5705 (mean -5.3253)
esa_source	object	0.0%
esa_processed	object	0.0%

---

Numeric Summary

Column	Min	Max	Mean	Median
code_etabl	19.0	8840.0	989.1956	923.5
latitude	4.3735	10.6213	6.9898	6.8865
longitude	-8.4996	-2.5705	-5.3253	-5.2769

---

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) 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 West and Central Africa (ROWCA) 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 for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_health_facilities_2012,
  title     = {Côte d'Ivoire Health facilities 2012},
  author    = {OCHA West and Central Africa (ROWCA)},
  year      = {2021},
  url       = {https://data.humdata.org/dataset/health-facilities-2012},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.