electricsheepafrica/africa-monthly-cross-border-trade-for-zambia-299

Zambia Monthly FEWS NET Cross Border Trade Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-01

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Abstract

Zambia Monthly cross border trade data collected by FEWS NET since 2005.

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: ZMB.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	7,425
Columns	44 (11 numeric, 31 categorical, 2 datetime)
Train split	5,940 rows
Test split	1,485 rows
Geographic scope	ZMB
Publisher	FEWS NET
HDX last updated	2026-04-01

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Variables

Geographic — reporting_country (Zambia, Malawi, Tanzania, United Republic of), reporting_country_code (ZM, MW, TZ), source_country_code (ZM, MW, TZ), destination_country_code (ZM, CD, MW), flow_type and 13 others.

Temporal — start_date, period_date, value_one_month_ago (range 0.2–16824.0), pct_change_from_one_month_ago (range -100.0–147275.0004).

Outcome / Measurement — value (range 0.0–227830.0).

Identifier / Metadata — source (Zambia, Malawi, Tanzania, United Republic of), indicator_name (TradeFlowQuantity), source_organization, source_document, dataseries_name and 5 others.

Other — border_point (Mqocha, Nakonde, Mpulungu), destination (Zambia, Congo, The Democratic Republic of the, Malawi), cpcv2 (R01122AC, P23161AA, P23130AB), product (Maize Grain (White), Rice (Milled), Roller Maize Meal), collection_status and 6 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-monthly-cross-border-trade-for-zambia-299")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
reporting_country	object	0.0%	Zambia, Malawi, Tanzania, United Republic of
reporting_country_code	object	0.0%	ZM, MW, TZ
border_point	object	55.9%	Mqocha, Nakonde, Mpulungu
source	object	0.0%	Zambia, Malawi, Tanzania, United Republic of
source_country_code	object	0.0%	ZM, MW, TZ
destination	object	0.0%	Zambia, Congo, The Democratic Republic of the, Malawi
destination_country_code	object	0.0%	ZM, CD, MW
cpcv2	object	0.0%	R01122AC, P23161AA, P23130AB
product	object	0.0%	Maize Grain (White), Rice (Milled), Roller Maize Meal
indicator_name	object	0.0%	TradeFlowQuantity
start_date	datetime64[ns]	0.0%
period_date	datetime64[ns]	0.0%
value	float64	0.0%	0.0 – 227830.0 (mean 1172.3848)
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%	27990.0 – 7636338.0 (mean 2912286.4805)
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 – 11391500.0 (mean 125883.6154)
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	61.2%	1158853.0 – 37980797.0 (mean 4125235.8966)
value_one_month_ago	float64	55.3%	0.2 – 16824.0 (mean 338.675)
value_one_year_ago	float64	64.3%	0.4 – 16824.0 (mean 347.472)
value_two_years_ago	float64	72.4%	0.425 – 9440.0 (mean 320.7466)
value_three_years_ago	float64	79.3%	0.425 – 6221.0 (mean 267.553)
two_year_average	float64	74.6%	1.5 – 8705.5435 (mean 339.9495)
pct_change_from_one_month_ago	float64	55.3%	-100.0 – 147275.0004 (mean 835.5938)
pct_change_from_one_year_ago	float64	64.3%	-100.0 – 647662.5016 (mean 1669.0543)
collection_schedule	object	0.0%
data_usage_policy	object	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
value	0.0	227830.0	1172.3848	0.6
dataseries	27990.0	7636338.0	2912286.4805	28047.0
common_unit_quantity	0.0	11391500.0	125883.6154	50.0
id	1158853.0	37980797.0	4125235.8966	1162001.5
value_one_month_ago	0.2	16824.0	338.675	88.7407
value_one_year_ago	0.4	16824.0	347.472	87.3025
value_two_years_ago	0.425	9440.0	320.7466	84.0625
value_three_years_ago	0.425	6221.0	267.553	71.11
two_year_average	1.5	8705.5435	339.9495	107.366
pct_change_from_one_month_ago	-100.0	147275.0004	835.5938	100.0
pct_change_from_one_year_ago	-100.0	647662.5016	1669.0543	137.3689

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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. 4 column(s) with >80% missing values were removed: value_four_years_ago, value_five_years_ago, 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.

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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, value_three_years_ago, two_year_average, pct_change_from_one_month_ago....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_monthly_cross_border_trade_for_zambia_299,
  title     = {Zambia Monthly FEWS NET Cross Border Trade Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/monthly_cross_border_trade_for_zambia_299},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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