electricsheepafrica/africa-zimbabwe-operational-presence

---
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
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- hxl
- operational-presence
- who-is-doing-what-and-where-3w-4w-5w
- zwe
pretty_name: "Zimbabwe: Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 1256
- name: test
num_examples: 314
---

# Zimbabwe: Operational Presence

**Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/zimbabwe-operational-presence) · **License:** `cc-by` · **Updated:** 2025-09-16

---

## Abstract

Who is doing what and where in zimbabwe

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `reporting_month` column(s). Geographic scope: **ZWE**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 1,570 |
| **Columns** | 9 (0 numeric, 8 categorical, 1 datetime) |
| **Train split** | 1,256 rows |
| **Test split** | 314 rows |
| **Geographic scope** | ZWE |
| **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| **HDX last updated** | 2025-09-16 |

---

## Variables

**Geographic** — `province` (Manicaland, Mashonaland East, Matabeleland North), `lead_agency` (United Nations Children's Fund (UNICEF), World Health Organization (WHO), World Food Programme (WFP) ), `organization_type` (Government, INGO, NNGO).

**Temporal** — `reporting_month`.

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-18).

**Other** — `areas` (Harare, Binga, Chipinge), `sector_cluster` (Education, Protection/CP, Health), `implementing_partner` (Ministry of Health and Child Care (MoHCC), Regional Psychosocial Support Initiative (REPSSI), United Nations Children's Fund (UNICEF)).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-zimbabwe-operational-presence")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `reporting_month` | datetime64[ns] | 0.1% | |
| `province` | object | 0.1% | Manicaland, Mashonaland East, Matabeleland North |
| `areas` | object | 0.1% | Harare, Binga, Chipinge |
| `sector_cluster` | object | 0.0% | Education, Protection/CP, Health |
| `lead_agency` | object | 0.1% | United Nations Children's Fund (UNICEF), World Health Organization (WHO), World Food Programme (WFP) |
| `implementing_partner` | object | 0.0% | Ministry of Health and Child Care (MoHCC), Regional Psychosocial Support Initiative (REPSSI), United Nations Children's Fund (UNICEF) |
| `organization_type` | object | 0.0% | Government, INGO, NNGO |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-18 |

---

## 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`. 4,608 exact duplicate rows were removed. 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 Regional Office for Southern and Eastern Africa (ROSEA) 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/zimbabwe-operational-presence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_zimbabwe_operational_presence,
title = {Zimbabwe: Operational Presence},
author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
year = {2025},
url = {https://data.humdata.org/dataset/zimbabwe-operational-presence},
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.*