| --- |
| 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 |
| - ssd |
| pretty_name: "South Sudan Daily FEWS NET Cross Border Trade Data" |
| dataset_info: |
| splits: |
| - name: train |
| num_examples: 21709 |
| - name: test |
| num_examples: 5427 |
| --- |
| |
| # South Sudan Daily FEWS NET Cross Border Trade Data |
|
|
| **Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/daily_cross_border_trade_for_south_sudan_6824) · **License:** `cc-by` · **Updated:** 2026-04-07 |
|
|
| --- |
|
|
| ## Abstract |
|
|
| South 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: **SSD**. |
|
|
| *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)** | 27,137 | |
| | **Columns** | 38 (5 numeric, 31 categorical, 2 datetime) | |
| | **Train split** | 21,709 rows | |
| | **Test split** | 5,427 rows | |
| | **Geographic scope** | SSD | |
| | **Publisher** | FEWS NET | |
| | **HDX last updated** | 2026-04-07 | |
|
|
| --- |
|
|
| ## Variables |
|
|
| **Geographic** — `reporting_country` (South Sudan, Ethiopia, Sudan), `reporting_country_code` (SS, ET, SD), `source_country_code` (ET, SD, UG), `destination_country_code` (SS, SD, ET), `flow_type` and 8 others. |
|
|
| **Temporal** — `start_date`, `period_date`, `value_one_month_ago` (range 0.5–244536280.5), `pct_change_from_one_month_ago` (range -99.988–339609.1105). |
|
|
| **Outcome / Measurement** — `value` (range 0.0–978145122.0). |
|
|
| **Identifier / Metadata** — `source` (Ethiopia, Sudan, Uganda), `indicator_name` (TradeFlowQuantity), `source_organization`, `source_document`, `dataseries_name` and 4 others. |
|
|
| **Other** — `border_point` (Gambella, War War, Goc Machar), `destination` (South Sudan, Sudan, Ethiopia), `cpcv2` (P23520AA, P23110AA, P21549AA), `product` (Refined sugar, Wheat Flour, Refined Vegetable Oil), `collection_status` and 6 others. |
|
|
| --- |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("electricsheepafrica/africa-daily-cross-border-trade-for-south-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% | South Sudan, Ethiopia, Sudan | |
| | `reporting_country_code` | object | 0.0% | SS, ET, SD | |
| | `border_point` | object | 0.0% | Gambella, War War, Goc Machar | |
| | `source` | object | 0.0% | Ethiopia, Sudan, Uganda | |
| | `source_country_code` | object | 0.0% | ET, SD, UG | |
| | `destination` | object | 0.0% | South Sudan, Sudan, Ethiopia | |
| | `destination_country_code` | object | 0.0% | SS, SD, ET | |
| | `cpcv2` | object | 0.0% | P23520AA, P23110AA, P21549AA | |
| | `product` | object | 0.0% | Refined sugar, Wheat Flour, Refined Vegetable Oil | |
| | `indicator_name` | object | 0.0% | TradeFlowQuantity | |
| | `start_date` | datetime64[ns] | 0.0% | | |
| | `period_date` | datetime64[ns] | 0.0% | | |
| | `value` | float64 | 0.0% | 0.0 – 978145122.0 (mean 1030589.5011) | |
| | `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% | 6544221.0 – 7402467.0 (mean 6681220.24) | |
| | `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 – 10399759600.0 (mean 3119770.3643) | |
| | `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 | 0.0% | | |
| | `value_one_month_ago` | float64 | 67.1% | 0.5 – 244536280.5 (mean 763606.4849) | |
| | `pct_change_from_one_month_ago` | float64 | 67.1% | -99.988 – 339609.1105 (mean 612.9642) | |
| | `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 | 978145122.0 | 1030589.5011 | 0.0 | |
| | `dataseries` | 6544221.0 | 7402467.0 | 6681220.24 | 6615877.0 | |
| | `common_unit_quantity` | 0.0 | 10399759600.0 | 3119770.3643 | 0.0 | |
| | `value_one_month_ago` | 0.5 | 244536280.5 | 763606.4849 | 394.6667 | |
| | `pct_change_from_one_month_ago` | -99.988 | 339609.1105 | 612.9642 | 198.2516 | |
|
|
| --- |
|
|
| ## 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_south_sudan_6824) for the publisher's own methodology notes and caveats. |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{hdx_africa_daily_cross_border_trade_for_south_sudan_6824, |
| title = {South Sudan Daily FEWS NET Cross Border Trade Data}, |
| author = {FEWS NET}, |
| year = {2026}, |
| url = {https://data.humdata.org/dataset/daily_cross_border_trade_for_south_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.* |
