---
license: other
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: monolingual
size_categories:
- n<1K
tags:
- tabular
- xlsx
- africa
- nigeria
- official-statistics
- open-data
- health
pretty_name: "National Health Facility Survey 2023 | Africa (Nigeria official open data)"
---

# National Health Facility Survey 2023 | Africa (Nigeria official open data)

435 rows - 1 Africa country - 2023 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

![rows](https://img.shields.io/badge/rows-435-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-other-lightgrey)

## TL;DR

This dataset packages one official `XLSX` resource from **Nigeria** 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:** [National Health Facility Survey 2023](https://microdata.nigerianstat.gov.ng/index.php/catalog/149/related-materials)
- **Publisher:** National Bureau of Statistics (NBS)
- **Resource:** [Clinical IMCI Modules Analyses](https://microdata.nigerianstat.gov.ng/index.php/catalog/149/download/1093)
- **Format:** `XLSX`
- **License:** [Other open license]()
- **Packaging mode:** `tabular_resource`

## Geographic coverage

1 Africa country:

| Country | Rows | First year | Last year | Name |
|---------|-----:|-----------:|----------:|------|
| `NGA` | 435 | 2023 | 2023 | `Nigeria` |

## 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. | `nbs-nada-149-1093:diagnostic-accuracy-fac-type:0` |
| `country_iso3` | `string` | ISO3 country code. | `NGA` |
| `country_name` | `string` | Country name. | `Nigeria` |
| `source_sheet` | `string` | Workbook sheet name, when the source is a spreadsheet. | `DIagnostic accuracy_fac_type` |
| `year` | `Int64` | Observation year. | `2023` |
| `adamawa` | `string` | Source column. | `Akwa Ibom` |
| `d_46_5553` | `float64` | Source column. | `38.88557` |
| `d_65_07466` | `float64` | Source column. | `35.16267` |
| `d_47_44941` | `float64` | Source column. | `38.62627` |
| `d_79_19819` | `float64` | Source column. | `45.65664` |
| `d_100` | `float64` | Source column. | `55.65046` |
| `d_80_32926` | `float64` | Source column. | `46.30559` |
| `d_31_47178` | `float64` | Source column. | `25.931510000000003` |
| `d_53_862` | `float64` | Source column. | `35.86354` |
| `d_32_60228` | `float64` | Source column. | `26.556790000000003` |
| `d_3_0976299999999997` | `float64` | Source column. | `21.87607` |
| `d_15_511069999999998` | `float64` | Source column. | `0.0` |
| `d_3_6938400000000002` | `float64` | Source column. | `20.264190000000003` |
| `d_72_08584` | `float64` | Source column. | `64.78464000000001` |
| `d_92_89676` | `float64` | Source column. | `57.923820000000006` |
| `d_73_11532` | `float64` | Source column. | `64.2981` |
| `d_18_28171` | `float64` | Source column. | `35.33621` |
| `d_28_07848` | `float64` | Source column. | `29.33242` |
| `d_18_73137` | `float64` | Source column. | `34.90672` |
| `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` | `string` | Human-readable period inferred from source resource metadata. | `2023` |
| `source_provider` | `string` | Publishing organization. | `National Bureau of Statistics (NBS)` |
| `source_dataset` | `string` | Source package title. | `National Health Facility Survey 2023` |
| `source_resource` | `string` | Source resource title. | `Clinical IMCI Modules Analyses` |
| `source_package_id` | `string` | CKAN package UUID. | `NGA-NBS-NHFS` |
| `source_resource_id` | `string` | CKAN resource UUID. | `nbs-nada-149-1093` |
| `source_url` | `string` | Original source resource URL. | `https://microdata.nigerianstat.gov.ng/index.php/catalog/149/download/109` |
| `license_id` | `string` | Source license identifier. | `other-open` |
| `retrieved_at` | `string` | UTC retrieval timestamp. | `2026-07-19T04:13:01Z` |
| `gombe` | `string` | Source column. | `` |
| `d_62_36862` | `float64` | Source column. | `` |
| `d_57_716` | `float64` | Source column. | `` |
| `d_54_80698` | `float64` | Source column. | `` |
| `d_51_898619999999994` | `float64` | Source column. | `` |
| `d_81_63454` | `float64` | Source column. | `` |
| `d_50_846349999999994` | `float64` | Source column. | `` |
| `d_55_060430000000004` | `float64` | Source column. | `` |
| `d_66_75876` | `float64` | Source column. | `` |
| `d_62_71407` | `float64` | Source column. | `` |
| `d_41_337869999999995` | `float64` | Source column. | `` |
| `d_24_06278` | `float64` | Source column. | `` |
| `d_22_54862` | `float64` | Source column. | `` |
| `d_0` | `float64` | Source column. | `` |
| `d_16_59692` | `float64` | Source column. | `` |
| `d_28_4807` | `float64` | Source column. | `` |
| `d_22_33737` | `float64` | Source column. | `` |
| `d_100_2` | `float64` | Source column. | `` |
| `d_93_97339` | `float64` | Source column. | `` |
| `d_72_80402000000001` | `float64` | Source column. | `` |
| `d_75_30623` | `float64` | Source column. | `` |
| `d_74_87452` | `float64` | Source column. | `` |
| `d_69_46391` | `float64` | Source column. | `` |
| `d_68_39127` | `float64` | Source column. | `` |
| `abia` | `string` | Source column. | `` |
| `d_63_89351` | `float64` | Source column. | `` |
| `d_58_39858999999999` | `float64` | Source column. | `` |
| `d_56_186800000000005` | `float64` | Source column. | `` |
| `d_63_96343` | `float64` | Source column. | `` |
| `d_63_95418000000001` | `float64` | Source column. | `` |
| `d_58_018899999999995` | `float64` | Source column. | `` |
| `d_51_421499999999995` | `float64` | Source column. | `` |
| `d_70_79514999999999` | `float64` | Source column. | `` |
| `d_60` | `float64` | Source column. | `` |
| `d_52_1911` | `float64` | Source column. | `` |
| `d_47_70132` | `float64` | Source column. | `` |
| `d_54_42849` | `float64` | Source column. | `` |
| `d_67_72635` | `float64` | Source column. | `` |
| `d_64_98576` | `float64` | Source column. | `` |
| `d_69_4376` | `float64` | Source column. | `` |
| `d_66_66667` | `float64` | Source column. | `` |
| `d_33_72322` | `float64` | Source column. | `` |
| `d_25_141730000000003` | `float64` | Source column. | `` |
| `d_32_195049999999995` | `float64` | Source column. | `` |
| `d_34_89807` | `float64` | Source column. | `` |
| `d_57_604639999999996` | `float64` | Source column. | `` |
| `d_62_10793` | `float64` | Source column. | `` |
| `d_57_716319999999996` | `float64` | Source column. | `` |
| `d_55_19296` | `float64` | Source column. | `` |
| `d_66_24657` | `float64` | Source column. | `` |
| `d_55_46706999999999` | `float64` | Source column. | `` |
| `d_49_79087` | `float64` | Source column. | `` |
| `d_52_84245` | `float64` | Source column. | `` |
| `d_49_86655` | `float64` | Source column. | `` |
| `d_67_8301` | `float64` | Source column. | `` |
| `d_67_23477` | `float64` | Source column. | `` |
| `d_67_81532999999999` | `float64` | Source column. | `` |
| `d_30_41487` | `float64` | Source column. | `` |
| `d_29_09084` | `float64` | Source column. | `` |
| `d_30_38204` | `float64` | Source column. | `` |
| `d_52_600970000000004` | `float64` | Source column. | `` |
| `d_44_6768` | `float64` | Source column. | `` |
| `d_36_817699999999995` | `float64` | Source column. | `` |
| `d_43_17738` | `float64` | Source column. | `` |
| `d_41_09465` | `float64` | Source column. | `` |
| `d_45_96739` | `float64` | Source column. | `` |
| `d_37_89353` | `float64` | Source column. | `` |
| `d_47_038289999999996` | `float64` | Source column. | `` |
| `d_53_55451` | `float64` | Source column. | `` |
| `d_29_834339999999997` | `float64` | Source column. | `` |
| `d_23_30439` | `float64` | Source column. | `` |
| `d_27_112609999999997` | `float64` | Source column. | `` |
| `d_63_153760000000005` | `float64` | Source column. | `` |
| `d_58_22867000000001` | `float64` | Source column. | `` |
| `d_49_25519` | `float64` | Source column. | `` |
| `d_55_38123` | `float64` | Source column. | `` |
| `d_68_98844` | `float64` | Source column. | `` |
| `d_44_3224` | `float64` | Source column. | `` |
| `d_54_38785000000001` | `float64` | Source column. | `` |
| `d_57_281740000000006` | `float64` | Source column. | `` |
| `d_53_426660000000005` | `float64` | Source column. | `` |
| `d_47_42989` | `float64` | Source column. | `` |
| `d_32_45134` | `float64` | Source column. | `` |
| `d_47_807739999999995` | `float64` | Source column. | `` |
| `d_51_66795` | `float64` | Source column. | `` |
| `d_59_83614000000001` | `float64` | Source column. | `` |
| `d_51_8705` | `float64` | Source column. | `` |
| `d_38_46875` | `float64` | Source column. | `` |
| `d_48_11804` | `float64` | Source column. | `` |
| `d_38_70803` | `float64` | Source column. | `` |
| `d_64_21544999999999` | `float64` | Source column. | `` |
| `d_55_77183` | `float64` | Source column. | `` |
| `d_49_297380000000004` | `float64` | Source column. | `` |
| `d_54_60334999999999` | `float64` | Source column. | `` |
| `d_51_677589999999995` | `float64` | Source column. | `` |
| `d_62_58274` | `float64` | Source column. | `` |
| `d_51_94801999999999` | `float64` | Source column. | `` |
| `d_76_15185` | `float64` | Source column. | `` |
| `d_64_24009000000001` | `float64` | Source column. | `` |
| `d_54_887620000000005` | `float64` | Source column. | `` |
| `d_56_669219999999996` | `float64` | Source column. | `` |
| `d_57_72623` | `float64` | Source column. | `` |
| `d_74_0664` | `float64` | Source column. | `` |
| `d_58_131429999999995` | `float64` | Source column. | `` |
| `d_62_88973` | `float64` | Source column. | `` |
| `d_54_990280000000006` | `float64` | Source column. | `` |
| `d_34_16479` | `float64` | Source column. | `` |
| `d_45_47575` | `float64` | Source column. | `` |
| `d_41_57848` | `float64` | Source column. | `` |
| `d_53_17062` | `float64` | Source column. | `` |
| `d_41_86594` | `float64` | Source column. | `` |
| `d_62_14558` | `float64` | Source column. | `` |
| `d_50_55345` | `float64` | Source column. | `` |
| `d_49_99286` | `float64` | Source column. | `` |
| `d_51_411629999999995` | `float64` | Source column. | `` |
| `d_50_3876` | `float64` | Source column. | `` |
| `d_53_369820000000004` | `float64` | Source column. | `` |
| `d_50_46155` | `float64` | Source column. | `` |
| `d_62_51766` | `float64` | Source column. | `` |
| `d_52_77187` | `float64` | Source column. | `` |
| `d_42_07882` | `float64` | Source column. | `` |
| `d_48_443690000000004` | `float64` | Source column. | `` |
| `d_45_983039999999995` | `float64` | Source column. | `` |
| `d_53_27022` | `float64` | Source column. | `` |
| `d_46_16375` | `float64` | Source column. | `` |
| `d_83_7251` | `float64` | Source column. | `` |
| `d_82_19623` | `float64` | Source column. | `` |
| `d_63_39567` | `float64` | Source column. | `` |
| `d_62_05508` | `float64` | Source column. | `` |
| `d_72_35281` | `float64` | Source column. | `` |
| `d_62_55067` | `float64` | Source column. | `` |
| `d_35_87824` | `float64` | Source column. | `` |
| `d_43_143769999999996` | `float64` | Source column. | `` |
| `d_69_07997` | `float64` | Source column. | `` |
| `d_71_18247` | `float64` | Source column. | `` |
| `d_69_13211` | `float64` | Source column. | `` |
| `d_44_68821` | `float64` | Source column. | `` |
| `d_58_22321` | `float64` | Source column. | `` |
| `d_45_023849999999996` | `float64` | Source column. | `` |
| `d_32_911010000000005` | `float64` | Source column. | `` |
| `d_0_0606272` | `float64` | Source column. | `` |
| `d_82_13032` | `float64` | Source column. | `` |
| `d_0_0489785` | `float64` | Source column. | `` |
| `d_46_46891` | `float64` | Source column. | `` |
| `d_0_0553952` | `float64` | Source column. | `` |
| `lagos` | `string` | Source column. | `` |
| `d_33_550999999999995` | `float64` | Source column. | `` |
| `d_0_1061768` | `float64` | Source column. | `` |
| `d_25_11971` | `float64` | Source column. | `` |
| `d_0_0856221` | `float64` | Source column. | `` |
| `d_18_6673` | `float64` | Source column. | `` |
| `d_0_0858494` | `float64` | Source column. | `` |
| `omitted` | `float64` | Source column. | `` |
| `d_51_02723` | `float64` | Source column. | `` |
| `d_0_111273` | `float64` | Source column. | `` |
| `d_54_613929999999996` | `float64` | Source column. | `` |
| `d_0_113502` | `float64` | Source column. | `` |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-national-health-facility-survey-2023-c5654893")
df = ds["train"].to_pandas()
print(df.head())
```

### Filter to one country

```python
sample_country = df[df["country_iso3"] == "NGA"]
```

### 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_nigeria_national_health_facility_survey_2023_c5654893_2023,
  title        = {National Health Facility Survey 2023 | Africa (Nigeria official open data)},
  author       = {National Bureau of Statistics (NBS)},
  year         = {2023},
  url          = {https://microdata.nigerianstat.gov.ng/index.php/catalog/149/related-materials},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-national-health-facility-survey-2023-c5654893}}
}
```

## License

Released under [Other open license]().

Original data (c) National Bureau of Statistics (NBS). 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 2026-07-19 via the Electric Sheep pipeline. Source URL:
https://microdata.nigerianstat.gov.ng/index.php/catalog/149/download/1093