Datasets:
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
- eth
pretty_name: Ethiopia Daily FEWS NET Cross Border Trade Data
dataset_info:
splits:
- name: train
num_examples: 38785
- name: test
num_examples: 9696
Ethiopia Daily FEWS NET Cross Border Trade Data
Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-03-30
Abstract
Ethiopia 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: ETH.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Humanitarian and development data |
| Unit of observation | First-level administrative unit observations |
| Rows (total) | 48,482 |
| Columns | 38 (5 numeric, 31 categorical, 2 datetime) |
| Train split | 38,785 rows |
| Test split | 9,696 rows |
| Geographic scope | ETH |
| Publisher | FEWS NET |
| HDX last updated | 2026-03-30 |
Variables
Geographic — reporting_country (Ethiopia, Somalia, Kenya), reporting_country_code (ET, SO, KE), source_country_code (ET, SO, SD), destination_country_code (SD, ET, SS), flow_type and 8 others.
Temporal — start_date, period_date, value_one_month_ago (range 0.0–1372872.0), pct_change_from_one_month_ago (range -99.9889–178287.6501).
Outcome / Measurement — value (range 0.0–1372872.0).
Identifier / Metadata — source (Ethiopia, Somalia, Sudan), indicator_name (TradeFlowQuantity), source_organization, source_document, dataseries_name and 4 others.
Other — border_point (Gambella, Kurmuk, Moyale), destination (Sudan, Ethiopia, South Sudan), cpcv2 (P23520AA, P23161AA, R01122AC), product (Refined sugar, Rice (Milled), Maize Grain (White)), collection_status and 6 others.
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-daily-cross-border-trade-for-ethiopia-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, Somalia, Kenya |
reporting_country_code |
object | 0.0% | ET, SO, KE |
border_point |
object | 0.0% | Gambella, Kurmuk, Moyale |
source |
object | 0.0% | Ethiopia, Somalia, Sudan |
source_country_code |
object | 0.0% | ET, SO, SD |
destination |
object | 0.0% | Sudan, Ethiopia, South Sudan |
destination_country_code |
object | 0.0% | SD, ET, SS |
cpcv2 |
object | 0.0% | P23520AA, P23161AA, R01122AC |
product |
object | 0.0% | Refined sugar, Rice (Milled), Maize Grain (White) |
indicator_name |
object | 0.0% | TradeFlowQuantity |
start_date |
datetime64[ns] | 0.0% | |
period_date |
datetime64[ns] | 0.0% | |
value |
float64 | 0.0% | 0.0 – 1372872.0 (mean 1920.4796) |
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% | 6544175.0 – 7402469.0 (mean 6669150.9641) |
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 – 384000000.0 (mean 162639.9936) |
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 | 2.7% | |
value_one_month_ago |
float64 | 70.6% | 0.0 – 1372872.0 (mean 1735.3038) |
pct_change_from_one_month_ago |
float64 | 70.6% | -99.9889 – 178287.6501 (mean 451.8913) |
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 | 1372872.0 | 1920.4796 | 0.0 |
dataseries |
6544175.0 | 7402469.0 | 6669150.9641 | 6614111.0 |
common_unit_quantity |
0.0 | 384000000.0 | 162639.9936 | 0.0 |
value_one_month_ago |
0.0 | 1372872.0 | 1735.3038 | 400.0 |
pct_change_from_one_month_ago |
-99.9889 | 178287.6501 | 451.8913 | 225.9259 |
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 for the publisher's own methodology notes and caveats.
Citation
@dataset{hdx_africa_daily_cross_border_trade_for_ethiopia_6824,
title = {Ethiopia Daily FEWS NET Cross Border Trade Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/daily_cross_border_trade_for_ethiopia_6824},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.