electricsheepafrica/africa-daily-cross-border-trade-for-sudan-6824

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- eastern-africa
- trade
- sdn
pretty_name: "Sudan Daily FEWS NET Cross Border Trade Data"
dataset_info:
splits:
- name: train
num_examples: 23362
- name: test
num_examples: 5840
---

# Sudan Daily FEWS NET Cross Border Trade Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/daily_cross_border_trade_for_sudan_6824) · **License:** `cc-by` · **Updated:** 2026-04-07

---

## Abstract

Sudan Daily cross border trade data collected by FEWS NET since 2010.

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

*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)** | 29,203 |
| **Columns** | 38 (5 numeric, 31 categorical, 2 datetime) |
| **Train split** | 23,362 rows |
| **Test split** | 5,840 rows |
| **Geographic scope** | SDN |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-07 |

---

## Variables

**Geographic** — `reporting_country` (Ethiopia, South Sudan, Sudan), `reporting_country_code` (ET, SS, SD), `source_country_code` (SD, ET, SS), `destination_country_code` (SD, SS, ET), `flow_type` and 8 others.

**Temporal** — `start_date`, `period_date`, `value_one_month_ago` (range 0.25–1372872.0), `pct_change_from_one_month_ago` (range -99.988–276585.7143).

**Outcome / Measurement** — `value` (range 0.0–1927800.0).

**Identifier / Metadata** — `source` (Sudan, Ethiopia, South Sudan), `indicator_name` (TradeFlowQuantity), `source_organization`, `source_document`, `dataseries_name` and 4 others.

**Other** — `border_point` (Kurmuk, War War, Matema), `destination` (Sudan, South Sudan, Ethiopia), `cpcv2` (P23520AA, R01253AA, P23110AA), `product` (Refined sugar, Onions, Wheat Flour), `collection_status` and 6 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-daily-cross-border-trade-for-sudan-6824")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `reporting_country` | object | 0.0% | Ethiopia, South Sudan, Sudan |
| `reporting_country_code` | object | 0.0% | ET, SS, SD |
| `border_point` | object | 0.0% | Kurmuk, War War, Matema |
| `source` | object | 0.0% | Sudan, Ethiopia, South Sudan |
| `source_country_code` | object | 0.0% | SD, ET, SS |
| `destination` | object | 0.0% | Sudan, South Sudan, Ethiopia |
| `destination_country_code` | object | 0.0% | SD, SS, ET |
| `cpcv2` | object | 0.0% | P23520AA, R01253AA, P23110AA |
| `product` | object | 0.0% | Refined sugar, Onions, Wheat Flour |
| `indicator_name` | object | 0.0% | TradeFlowQuantity |
| `start_date` | datetime64[ns] | 0.0% | |
| `period_date` | datetime64[ns] | 0.0% | |
| `value` | float64 | 0.0% | 0.0 – 1927800.0 (mean 2329.7224) |
| `flow_type` | object | 0.0% | |
| `trade_type` | object | 0.0% | |
| `collection_status` | object | 0.0% | |
| `source_organization` | object | 0.0% | |
| `source_document` | object | 0.0% | |
| `dataseries_name` | object | 0.0% | |
| `dataseries` | int64 | 0.0% | 6544253.0 – 7402455.0 (mean 6640001.5663) |
| `unit` | object | 0.0% | |
| `unit_type` | object | 0.0% | |
| `unit_name` | object | 0.0% | |
| `status` | object | 0.0% | |
| `common_unit` | object | 0.0% | |
| `common_unit_quantity` | float64 | 0.0% | 0.0 – 137287200.0 (mean 129626.3166) |
| `reporting_country_geographic_group` | object | 0.0% | |
| `reporting_country_fewsnet_region` | object | 0.0% | |
| `source_geographic_group` | object | 0.0% | |
| `source_fewsnet_region` | object | 0.0% | |
| `destination_geographic_group` | object | 0.0% | |
| `destination_fewsnet_region` | object | 1.2% | |
| `value_one_month_ago` | float64 | 77.2% | 0.25 – 1372872.0 (mean 2948.4992) |
| `pct_change_from_one_month_ago` | float64 | 77.2% | -99.988 – 276585.7143 (mean 543.1502) |
| `collection_schedule` | object | 0.0% | |
| `data_usage_policy` | object | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `value` | 0.0 | 1927800.0 | 2329.7224 | 0.0 |
| `dataseries` | 6544253.0 | 7402455.0 | 6640001.5663 | 6613764.0 |
| `common_unit_quantity` | 0.0 | 137287200.0 | 129626.3166 | 0.0 |
| `value_one_month_ago` | 0.25 | 1372872.0 | 2948.4992 | 77.75 |
| `pct_change_from_one_month_ago` | -99.988 | 276585.7143 | 543.1502 | 194.0057 |

---

## 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`. 10 column(s) with >80% missing values were removed: `id`, `value_one_year_ago`, `value_two_years_ago`, `value_three_years_ago`, `value_four_years_ago`, `value_five_years_ago`.... 2 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 FEWS NET and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `value_one_month_ago`, `pct_change_from_one_month_ago`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/daily_cross_border_trade_for_sudan_6824) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_daily_cross_border_trade_for_sudan_6824,
title = {Sudan Daily FEWS NET Cross Border Trade Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/daily_cross_border_trade_for_sudan_6824},
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.*