electricsheepafrica/africa-global-market-monitor

WFP Global Market Monitor

Publisher: WFP - World Food Programme · Source: HDX · License: cc-by-igo · Updated: 2026-03-31

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Abstract

The WFP Global Market Monitor monitors food prices in markets across a range of countries globally. The resources are updated every other week, with country data updating based on when data is received from country offices.

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the date, lastmodifydate column(s). Geographic scope: AFG, DZA, AGO, ARM, BGD, BEN, BOL, BFA, and 72 others.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	11,957
Columns	25 (11 numeric, 12 categorical, 2 datetime)
Train split	9,565 rows
Test split	2,391 rows
Geographic scope	AFG, DZA, AGO, ARM, BGD, BEN, BOL, BFA, and 72 others
Publisher	WFP - World Food Programme
HDX last updated	2026-03-31

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Variables

Geographic — monthlyversion (range 50.0–113.0), frequencyname (Monthly), countrycode (range 1.0–70001.0), countryname (Mauritania, Rwanda, Pakistan), admin1 (National Average , National Average ) and 12 others.

Temporal — date, pricetrendquarter (Negative, Moderate, Normal ), pricetrendmonth (N/A , Negative, Normal ).

Identifier / Metadata — esa_source, esa_processed.

Other — datalevel (National ), mainstaplefood (Sugar , Wheat flour , Rice (imported) ), caloriccontribution (range 5.0–70.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-global-market-monitor")
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
date	datetime64[ns]	0.0%
monthlyversion	int64	0.0%	50.0 – 113.0 (mean 78.5272)
frequencyname	object	0.0%	Monthly
countrycode	int64	0.0%	1.0 – 70001.0 (mean 2181.7929)
countryname	object	0.0%	Mauritania, Rwanda, Pakistan
admin1	object	0.0%	National Average , National Average
datalevel	object	0.0%	National
mainstaplefood	object	0.0%	Sugar , Wheat flour , Rice (imported)
pricetype	object	0.0%	Retail , Wholesale
caloriccontribution	float64	0.2%	5.0 – 70.0 (mean 17.332)
quarterlychangensa	float64	14.9%	-100.0 – 658.0 (mean 4.3069)
monthlychangensa	float64	45.0%	-74.0 – 193.0 (mean 1.5485)
quarterlychangesa	float64	15.1%	-100.0 – 847.0 (mean 2.5239)
monthlychangesa	float64	45.2%	-74.0 – 508.0 (mean 0.9105)
yoychangequarter	float64	15.5%	-100.0 – 1942.0 (mean 21.5421)
yoychangemonth	float64	15.8%	-100.0 – 1796.0 (mean 22.3996)
pricetrendquarter	object	0.0%	Negative, Moderate, Normal
pricetrendmonth	object	0.0%	N/A , Negative, Normal
quarterlycostshare	float64	14.8%	2.0 – 100.0 (mean 32.5415)
totimpactquarterlychange	float64	72.4%	-100.0 – 296.0 (mean 1.709)
totimpactquarterlycode	object	0.0%	Negative, Moderate, Normal
totimpactmonthlycode	object	0.0%	N/A , Negative, Normal
lastmodifydate	datetime64[ns]	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
monthlyversion	50.0	113.0	78.5272	76.0
countrycode	1.0	70001.0	2181.7929	159.0
caloriccontribution	5.0	70.0	17.332	12.0
quarterlychangensa	-100.0	658.0	4.3069	1.0
monthlychangensa	-74.0	193.0	1.5485	0.0
quarterlychangesa	-100.0	847.0	2.5239	1.0
monthlychangesa	-74.0	508.0	0.9105	0.0
yoychangequarter	-100.0	1942.0	21.5421	9.0
yoychangemonth	-100.0	1796.0	22.3996	9.0
quarterlycostshare	2.0	100.0	32.5415	24.0
totimpactquarterlychange	-100.0	296.0	1.709	1.0

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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. 1 column(s) with >80% missing values were removed: totimpactmonthlychange. 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 WFP - World Food Programme 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: monthlychangensa, monthlychangesa, totimpactquarterlychange.
• This dataset spans 80 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_global_market_monitor,
  title     = {WFP Global Market Monitor},
  author    = {WFP - World Food Programme},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/global-market-monitor},
  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.