electricsheepafrica/africa-kenya-market-assessment-data-for-bomet-county

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- survey
- ken
pretty_name: "Kenya - Market Assessment data for Bomet County"
dataset_info:
splits:
- name: train
num_examples: 28
- name: test
num_examples: 7
---

# Kenya - Market Assessment data for Bomet County

**Publisher:** Kenya Red Cross Society · **Source:** [HDX](https://data.humdata.org/dataset/kenya-market-assessment-data-for-bomet-county) · **License:** `other-pd-nr` · **Updated:** 2023-03-03

---

## Abstract

These datasets shows the price of commonly used household items/commodities in Bomet county.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2023-03-03. Geographic scope: **KEN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Market and price monitoring |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 36 |
| **Columns** | 16 (12 numeric, 4 categorical, 0 datetime) |
| **Train split** | 28 rows |
| **Test split** | 7 rows |
| **Geographic scope** | KEN |
| **Publisher** | Kenya Red Cross Society |
| **HDX last updated** | 2023-03-03 |

---

## Variables

**Identifier / Metadata** — `unnamed_1` (Per Head, Per Piece, 2 kg), `unnamed_2` (range 6.0–20000.0), `unnamed_3` (range 6.0–22000.0), `unnamed_4` (range 10.0–22000.0), `unnamed_5` (range 5.0–17000.0) and 10 others.

**Other** — `table_55_market_assessment_data_chepalungu` (Chepalungu, Chicken (Matured), Cattle (Male -2 years old)).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-kenya-market-assessment-data-for-bomet-county")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `table_55_market_assessment_data_chepalungu` | object | 2.8% | Chepalungu, Chicken (Matured), Cattle (Male -2 years old) |
| `unnamed_1` | object | 19.4% | Per Head, Per Piece, 2 kg |
| `unnamed_2` | float64 | 19.4% | 6.0 – 20000.0 (mean 1907.1034) |
| `unnamed_3` | float64 | 19.4% | 6.0 – 22000.0 (mean 1976.069) |
| `unnamed_4` | float64 | 19.4% | 10.0 – 22000.0 (mean 1991.0345) |
| `unnamed_5` | float64 | 19.4% | 5.0 – 17000.0 (mean 1517.6552) |
| `unnamed_6` | float64 | 19.4% | 0.0 – 14000.0 (mean 1385.8621) |
| `unnamed_7` | float64 | 19.4% | 0.0 – 13000.0 (mean 1336.7586) |
| `unnamed_8` | float64 | 19.4% | 0.0 – 18000.0 (mean 1730.9655) |
| `unnamed_9` | float64 | 19.4% | 0.0 – 10000.0 (mean 1030.9655) |
| `unnamed_10` | float64 | 19.4% | 0.0 – 12000.0 (mean 1129.6552) |
| `unnamed_11` | float64 | 19.4% | 0.0 – 15000.0 (mean 1640.6897) |
| `unnamed_12` | float64 | 19.4% | 0.0 – 12000.0 (mean 1130.6897) |
| `unnamed_13` | float64 | 19.4% | 7.18 – 15636.36 (mean 1525.2228) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-09 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `unnamed_2` | 6.0 | 20000.0 | 1907.1034 | 95.0 |
| `unnamed_3` | 6.0 | 22000.0 | 1976.069 | 95.0 |
| `unnamed_4` | 10.0 | 22000.0 | 1991.0345 | 190.0 |
| `unnamed_5` | 5.0 | 17000.0 | 1517.6552 | 130.0 |
| `unnamed_6` | 0.0 | 14000.0 | 1385.8621 | 100.0 |
| `unnamed_7` | 0.0 | 13000.0 | 1336.7586 | 90.0 |
| `unnamed_8` | 0.0 | 18000.0 | 1730.9655 | 90.0 |
| `unnamed_9` | 0.0 | 10000.0 | 1030.9655 | 120.0 |
| `unnamed_10` | 0.0 | 12000.0 | 1129.6552 | 120.0 |
| `unnamed_11` | 0.0 | 15000.0 | 1640.6897 | 100.0 |
| `unnamed_12` | 0.0 | 12000.0 | 1130.6897 | 120.0 |
| `unnamed_13` | 7.18 | 15636.36 | 1525.2228 | 119.55 |

---

## 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`. 12 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 Kenya Red Cross Society 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/kenya-market-assessment-data-for-bomet-county) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_kenya_market_assessment_data_for_bomet_county,
title = {Kenya - Market Assessment data for Bomet County},
author = {Kenya Red Cross Society},
year = {2023},
url = {https://data.humdata.org/dataset/kenya-market-assessment-data-for-bomet-county},
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.*