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africa-daily-cross-border-trade-for-zimbabwe-6819

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zwe
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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - trade
  - zwe
pretty_name: Zimbabwe Daily FEWS NET Cross Border Trade Data
dataset_info:
  splits:
    - name: train
      num_examples: 3202
    - name: test
      num_examples: 800

Zimbabwe Daily FEWS NET Cross Border Trade Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-03-30

Abstract

Zimbabwe Daily cross border trade data collected by FEWS NET since 2018.

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: ZWE.

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) 4,003
Columns 42 (9 numeric, 31 categorical, 2 datetime)
Train split 3,202 rows
Test split 800 rows
Geographic scope ZWE
Publisher FEWS NET
HDX last updated 2026-03-30

Variables

Geographic — reporting_country (Zimbabwe, Zambia, Malawi), reporting_country_code (ZW, ZM, MW), source_country_code (ZA, ZM, ZW), destination_country_code (ZW, ZM, MZ), flow_type and 11 others.

Temporal — start_date, period_date, value_one_month_ago (range 0.027–10600.0), pct_change_from_one_month_ago (range -100.0–120589.6552).

Outcome / Measurement — value (range 0.0–31800.0).

Identifier / Metadata — source (South Africa, Zambia, Zimbabwe), indicator_name (TradeFlowQuantity), source_organization, source_document, dataseries_name and 5 others.

Other — border_point (Beitbridge, Chirundu, Dedza), destination (Zimbabwe, Zambia, Mozambique), cpcv2 (P23130AB, P23161AA, R01701AA), product (Roller Maize Meal, Rice (Milled), Beans (mixed)), collection_status and 6 others.

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-daily-cross-border-trade-for-zimbabwe-6819")
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% Zimbabwe, Zambia, Malawi
reporting_country_code object 0.0% ZW, ZM, MW
border_point object 64.5% Beitbridge, Chirundu, Dedza
source object 0.0% South Africa, Zambia, Zimbabwe
source_country_code object 0.0% ZA, ZM, ZW
destination object 0.0% Zimbabwe, Zambia, Mozambique
destination_country_code object 0.0% ZW, ZM, MZ
cpcv2 object 0.0% P23130AB, P23161AA, R01701AA
product object 0.0% Roller Maize Meal, Rice (Milled), Beans (mixed)
indicator_name object 0.0% TradeFlowQuantity
start_date datetime64[ns] 0.0%
period_date datetime64[ns] 0.0%
value float64 0.0% 0.0 – 31800.0 (mean 196.3835)
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% 27991.0 – 6960794.0 (mean 2355941.4642)
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 – 6221000.0 (mean 24122.8569)
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%
id float64 62.5% 1158959.0 – 37980943.0 (mean 2235463.8129)
value_one_month_ago float64 60.6% 0.027 – 10600.0 (mean 78.7851)
value_one_year_ago float64 70.8% 0.027 – 6221.0 (mean 77.5408)
value_two_years_ago float64 79.7% 0.027 – 6221.0 (mean 87.467)
pct_change_from_one_month_ago float64 60.6% -100.0 – 120589.6552 (mean 962.0421)
pct_change_from_one_year_ago float64 70.8% -100.0 – 40332.6123 (mean 866.9512)
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 31800.0 196.3835 0.0
dataseries 27991.0 6960794.0 2355941.4642 28039.0
common_unit_quantity 0.0 6221000.0 24122.8569 0.0
id 1158959.0 37980943.0 2235463.8129 1161762.5
value_one_month_ago 0.027 10600.0 78.7851 16.92
value_one_year_ago 0.027 6221.0 77.5408 15.76
value_two_years_ago 0.027 6221.0 87.467 12.735
pct_change_from_one_month_ago -100.0 120589.6552 962.0421 50.0
pct_change_from_one_year_ago -100.0 40332.6123 866.9512 29.4389

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. 6 column(s) with >80% missing values were removed: value_three_years_ago, value_four_years_ago, value_five_years_ago, two_year_average, five_year_average, pct_change_from_five_year_average. 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: border_point, id, value_one_month_ago, value_one_year_ago, value_two_years_ago, pct_change_from_one_month_ago, pct_change_from_one_year_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_zimbabwe_6819,
  title     = {Zimbabwe Daily FEWS NET Cross Border Trade Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/daily_cross_border_trade_for_zimbabwe_6819},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.