---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- funding
- humanitarian-response-plan-hrp
- hxl
- mli
pretty_name: "Mali Humanitarian Response Plan"
dataset_info:
  splits:
    - name: train
      num_examples: 45
    - name: test
      num_examples: 11
---

# Mali Humanitarian Response Plan

**Publisher:** OCHA Humanitarian Programme Cycle Tools (HPC Tools) · **Source:** [HDX](https://data.humdata.org/dataset/mali-humanitarian-response-plan) · **License:** `cc-by` · **Updated:** 2025-12-02

---

## Abstract

This data has been produced by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. The data provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance. It represents a consolidated evidence base and helps inform joint strategic response planning

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-12-02. Geographic scope: **MLI**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 57 |
| **Columns** | 22 (16 numeric, 6 categorical, 0 datetime) |
| **Train split** | 45 rows |
| **Test split** | 11 rows |
| **Geographic scope** | MLI |
| **Publisher** | OCHA Humanitarian Programme Cycle Tools (HPC Tools) |
| **HDX last updated** | 2025-12-02 |

---

## Variables

**Identifier / Metadata** — `unnamed_2` (MOPTI, KAYES, KOULIKORO), `unnamed_3` (ML05, ML01, ML02), `unnamed_4` (CERCLES, GOURMA-RHAROUS, MOPTI), `unnamed_5` (PCODE_CERCLE, ML0604, ML0501), `unnamed_6` (range 0.0–61178.0194) and 17 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mali-humanitarian-response-plan")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `unnamed_2` | object | 5.3% | MOPTI, KAYES, KOULIKORO |
| `unnamed_3` | object | 5.3% | ML05, ML01, ML02 |
| `unnamed_4` | object | 5.3% | CERCLES, GOURMA-RHAROUS, MOPTI |
| `unnamed_5` | object | 5.3% | PCODE_CERCLE, ML0604, ML0501 |
| `unnamed_6` | float64 | 5.3% | 0.0 – 61178.0194 (mean 2265.8526) |
| `unnamed_7` | float64 | 5.3% | 0.0 – 1578.9124 (mean 58.4782) |
| `unnamed_8` | float64 | 5.3% | 0.0 – 436.5582 (mean 16.1688) |
| `unnamed_9` | float64 | 5.3% | 0.0 – 0.0 (mean 0.0) |
| `unnamed_10` | float64 | 5.3% | 0.0 – 250998.0 (mean 9296.2222) |
| `unnamed_11` | float64 | 5.3% | 0.0 – 250998.0 (mean 9296.2222) |
| `unnamed_12` | float64 | 5.3% | 0.0 – 250998.0 (mean 9296.2222) |
| `unnamed_13` | float64 | 5.3% | 0.0 – 255433.0 (mean 9460.4815) |
| `unnamed_15` | float64 | 5.3% | 0.0 – 55089.0498 (mean 2040.3352) |
| `unnamed_16` | float64 | 5.3% | 0.0 – 4374.0388 (mean 162.0014) |
| `unnamed_17` | float64 | 5.3% | 0.0 – 1979.3867 (mean 73.3106) |
| `unnamed_18` | float64 | 5.3% | 0.0 – 0.0 (mean 0.0) |
| `unnamed_19` | float64 | 5.3% | 0.0 – 243759.5 (mean 9028.1296) |
| `unnamed_20` | float64 | 5.3% | 0.0 – 545048.4372 (mean 20186.9792) |
| `unnamed_21` | float64 | 5.3% | 0.0 – 0.0 (mean 0.0) |
| `unnamed_22` | float64 | 5.3% | 0.0 – 545506.4372 (mean 20203.9421) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-18 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `unnamed_6` | 0.0 | 61178.0194 | 2265.8526 | 347.0286 |
| `unnamed_7` | 0.0 | 1578.9124 | 58.4782 | 0.9116 |
| `unnamed_8` | 0.0 | 436.5582 | 16.1688 | 0.0 |
| `unnamed_9` | 0.0 | 0.0 | 0.0 | 0.0 |
| `unnamed_10` | 0.0 | 250998.0 | 9296.2222 | 751.5 |
| `unnamed_11` | 0.0 | 250998.0 | 9296.2222 | 935.0 |
| `unnamed_12` | 0.0 | 250998.0 | 9296.2222 | 751.5 |
| `unnamed_13` | 0.0 | 255433.0 | 9460.4815 | 935.0 |
| `unnamed_15` | 0.0 | 55089.0498 | 2040.3352 | 1.0 |
| `unnamed_16` | 0.0 | 4374.0388 | 162.0014 | 0.0 |
| `unnamed_17` | 0.0 | 1979.3867 | 73.3106 | 0.0 |
| `unnamed_18` | 0.0 | 0.0 | 0.0 | 0.0 |
| `unnamed_19` | 0.0 | 243759.5 | 9028.1296 | 0.0 |
| `unnamed_20` | 0.0 | 545048.4372 | 20186.9792 | 9.0 |
| `unnamed_21` | 0.0 | 0.0 | 0.0 | 0.0 |

---

## 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`. 3 column(s) with >80% missing values were removed: `unnamed_0`, `unnamed_1`, `unnamed_14`. 15 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 Humanitarian Programme Cycle Tools (HPC Tools) 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/mali-humanitarian-response-plan) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_mali_humanitarian_response_plan,
  title     = {Mali Humanitarian Response Plan},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mali-humanitarian-response-plan},
  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.*