electricsheepafrica/africa-world-bank-private-sector-indicators-for-central-african-republic

---
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-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- caf
pretty_name: "Central African Republic - Private Sector"
dataset_info:
splits:
- name: train
num_examples: 2646
- name: test
num_examples: 661
---

# Central African Republic - Private Sector

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-central-african-republic) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-central-african-republic) on HDX.

Private markets drive economic growth, tapping initiative and investment to create productive jobs and raise incomes. Trade is also a driver of economic growth as it integrates developing countries into the world economy and generates benefits for their people. Data on the private sector and trade are from the World Bank Group's Private Participation in Infrastructure Project Database, Enterprise Surveys, and Doing Business Indicators, as well as from the International Monetary Fund's Balance of Payments database and International Financial Statistics, the UN Commission on Trade and Development, the World Trade Organization, and various other sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **CAF**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Market and price monitoring |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 3,308 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 2,646 rows |
| **Test split** | 661 rows |
| **Geographic scope** | CAF |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Central African Republic), `country_iso3` (CAF), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -0.0–736209534.0).

**Identifier / Metadata** — `indicator_name` (Merchandise imports (current US$), Merchandise trade (% of GDP), Merchandise exports (current US$)), `indicator_code` (TM.VAL.MRCH.CD.WT, TG.VAL.TOTL.GD.ZS, TX.VAL.MRCH.CD.WT), `esa_source` (HDX), `esa_processed` (2026-04-16).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-private-sector-indicators-for-central-african-republic")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Central African Republic |
| `country_iso3` | object | 0.0% | CAF |
| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 1997.9556) |
| `indicator_name` | object | 0.0% | Merchandise imports (current US$), Merchandise trade (% of GDP), Merchandise exports (current US$) |
| `indicator_code` | object | 0.0% | TM.VAL.MRCH.CD.WT, TG.VAL.TOTL.GD.ZS, TX.VAL.MRCH.CD.WT |
| `value` | float64 | 0.0% | -0.0 – 736209534.0 (mean 11197768.9793) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-16 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1997.9556 | 2001.0 |
| `value` | -0.0 | 736209534.0 | 11197768.9793 | 17.8109 |

---

## 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 World Bank Group 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/world-bank-private-sector-indicators-for-central-african-republic) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_private_sector_indicators_for_central_african_republic,
title = {Central African Republic - Private Sector},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-central-african-republic},
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.*