README.md

---
license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: monolingual
size_categories:
- 1K<n<10K
tags:
- "africa"
- "electric-sheep-africa"
- "open-data"
- "metadata-backed"
- "economics-finance"
- "parquet"
- "text"
- "tabular"
- "our-world-in-data"
- "china-imports-as-share-of-gdp"
- "owid"
- "long-run-series"
- "time-series"
- "gdp"
pretty_name: "China Imports As Share Of Gdp | Africa (Our World in Data) | Africa (Electric Sheep Africa metadata inventory)"
---

# China Imports As Share Of Gdp | Africa (Our World in Data) | Africa (Electric Sheep Africa metadata inventory)

**Size category:** `1K<n<10K` - **Formats:** `parquet` - **Sector:** economics_finance - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*

![size](https://img.shields.io/badge/size-1K%3Cn%3C10K-blue)
![sector](https://img.shields.io/badge/sector-economics_finance-green)
![downloads](https://img.shields.io/badge/HF_downloads-7-orange)
![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)

## TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

## What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: China Imports As Share Of Gdp | Africa (Our World in Data) 🌍 2,695 observations · 54 Africa countries · 1960–2024 · Repackaged by Electric Sheep Africa TL;DR This dataset contains 2,695 observations of China Imports As Share Of Gdp data across 54 Africa countries, spanning 1960–2024. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: China Imports As Share Of Gdp Geographic coverage… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-owid-china-imports-as-share-of-gdp.

## Dataset Profile

| Field | Value |
|---|---|
| Hugging Face repo | [`electricsheepafrica/africa-owid-china-imports-as-share-of-gdp`](https://huggingface.co/datasets/electricsheepafrica/africa-owid-china-imports-as-share-of-gdp) |
| Sector | economics_finance |
| Topic tags | tabular, our-world-in-data, china-imports-as-share-of-gdp, owid, long-run-series, time-series |
| Modalities | `text`, `tabular` |
| Formats | `parquet` |
| Size category | `1K<n<10K` |
| Countries | Africa-wide or source-defined African coverage |
| ISO3 coverage | `not declared` |
| Last modified on HF | `2026-06-02 18:42:58+00:00` |
| Inventory snapshot | `2026-07-16T16:00:34Z` |

## How To Read This Dataset

- Start from the repository files and the dataset viewer when available.
- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
- Preserve missing values until you have a defensible imputation rule.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-owid-china-imports-as-share-of-gdp")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])
```

### Convert To Pandas When Tabular

```python
from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())
```

## Data Quality Notes

- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
- Exact schema, row counts, and source files should be inspected in the repository data files.
- Metadata gaps from the inventory: country, upstream_publisher.
- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

## Source And Provenance

- **Source context:** Electric Sheep Africa metadata inventory
- **Publisher/source attribution:** Public dataset metadata
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-owid-china-imports-as-share-of-gdp](https://huggingface.co/datasets/electricsheepafrica/africa-owid-china-imports-as-share-of-gdp)
- **Inventory retrieved at:** `2026-07-16T16:00:34Z`

## Suggested Analyses

- Inspect schema and missingness before modeling.
- Profile variables by geography, time, and subgroup columns where present.
- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

## Citation

```bibtex
@misc{electric_sheep_africa_africa_owid_china_imports_as_share_of_gdp_2026,
  title        = {China Imports As Share Of Gdp | Africa (Our World in Data) | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-owid-china-imports-as-share-of-gdp},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-china-imports-as-share-of-gdp}}
}
```

## License

Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

## About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

---

Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.