electricsheepafrica/africa-county-government-expenditure-2013-2014

---
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
- governance-and-civil-society
- ken
pretty_name: "County Government Expenditure 2013_2014"
dataset_info:
splits:
- name: train
num_examples: 37
- name: test
num_examples: 9
---

# County Government Expenditure 2013_2014

**Publisher:** Kenya National Bureau of Statistics (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/county-government-expenditure-2013-2014) · **License:** `other-pd-nr` · **Updated:** 2025-02-06

---

## Abstract

The County Government Expenditure for the financial years of 2013_2014

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-02-06. Geographic scope: **KEN**.

*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)** | 47 |
| **Columns** | 17 (14 numeric, 3 categorical, 0 datetime) |
| **Train split** | 37 rows |
| **Test split** | 9 rows |
| **Geographic scope** | KEN |
| **Publisher** | Kenya National Bureau of Statistics (inactive) |
| **HDX last updated** | 2025-02-06 |

---

## Variables

**Geographic** — `county` (Baringo, Nyeri, Migori), `compensation_of_employees` (range 76.4756–4573.1104), `debt_repayment_pending_bills` (range 0.0–1806.4711), `hospitality_supplies_and_services` (range 2.5702–76.0747), `other_operating_expenses` (range 0.0–534.7113) and 2 others.

**Outcome / Measurement** — `transportation_costs` (range 13.1841–131.1065).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `acquisition_of_non_financial_assets` (range 0.0–1427.0577), `communication_supplies_and_services` (range 0.0–20.2), `fuel_oil_and_lubricants` (range 0.0–29.6458), `office_and_general_supplies_and_services` (range 0.0–111.709), `printing_advertising_and_information_supplies_and_services` (range 0.0–65.7295) and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-county-government-expenditure-2013-2014")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `county` | object | 0.0% | Baringo, Nyeri, Migori |
| `acquisition_of_non_financial_assets` | float64 | 0.0% | 0.0 – 1427.0577 (mean 154.19) |
| `communication_supplies_and_services` | float64 | 0.0% | 0.0 – 20.2 (mean 4.7657) |
| `compensation_of_employees` | float64 | 0.0% | 76.4756 – 4573.1104 (mean 462.9877) |
| `debt_repayment_pending_bills` | float64 | 0.0% | 0.0 – 1806.4711 (mean 46.818) |
| `fuel_oil_and_lubricants` | float64 | 0.0% | 0.0 – 29.6458 (mean 10.5228) |
| `hospitality_supplies_and_services` | float64 | 0.0% | 2.5702 – 76.0747 (mean 19.3012) |
| `office_and_general_supplies_and_services` | float64 | 0.0% | 0.0 – 111.709 (mean 14.8917) |
| `other_operating_expenses` | float64 | 0.0% | 0.0 – 534.7113 (mean 83.4789) |
| `printing_advertising_and_information_supplies_and_services` | float64 | 0.0% | 0.0 – 65.7295 (mean 14.1381) |
| `routine_maintenance` | float64 | 0.0% | 0.0 – 93.0878 (mean 20.7169) |
| `total_expenditure` | float64 | 0.0% | 169.9241 – 8450.7911 (mean 918.4539) |
| `training_expenses` | float64 | 0.0% | 0.02 – 45.8874 (mean 12.1757) |
| `transportation_costs` | float64 | 0.0% | 13.1841 – 131.1065 (mean 60.7137) |
| `utilities_supplies_and_services` | float64 | 0.0% | 0.0 – 142.8074 (mean 13.7533) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `acquisition_of_non_financial_assets` | 0.0 | 1427.0577 | 154.19 | 103.3798 |
| `communication_supplies_and_services` | 0.0 | 20.2 | 4.7657 | 2.7118 |
| `compensation_of_employees` | 76.4756 | 4573.1104 | 462.9877 | 308.559 |
| `debt_repayment_pending_bills` | 0.0 | 1806.4711 | 46.818 | 0.0 |
| `fuel_oil_and_lubricants` | 0.0 | 29.6458 | 10.5228 | 8.4745 |
| `hospitality_supplies_and_services` | 2.5702 | 76.0747 | 19.3012 | 16.6931 |
| `office_and_general_supplies_and_services` | 0.0 | 111.709 | 14.8917 | 8.0412 |
| `other_operating_expenses` | 0.0 | 534.7113 | 83.4789 | 54.152 |
| `printing_advertising_and_information_supplies_and_services` | 0.0 | 65.7295 | 14.1381 | 9.9243 |
| `routine_maintenance` | 0.0 | 93.0878 | 20.7169 | 13.5212 |
| `total_expenditure` | 169.9241 | 8450.7911 | 918.4539 | 691.3839 |
| `training_expenses` | 0.02 | 45.8874 | 12.1757 | 9.9596 |
| `transportation_costs` | 13.1841 | 131.1065 | 60.7137 | 53.6236 |
| `utilities_supplies_and_services` | 0.0 | 142.8074 | 13.7533 | 4.8487 |

---

## 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 Kenya National Bureau of Statistics (inactive) 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/county-government-expenditure-2013-2014) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_county_government_expenditure_2013_2014,
title = {County Government Expenditure 2013_2014},
author = {Kenya National Bureau of Statistics (inactive)},
year = {2025},
url = {https://data.humdata.org/dataset/county-government-expenditure-2013-2014},
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.*