electricsheepasia/asia-markets-wfp-food-prices-for-turkey

Turkey - Food Prices

Publisher: WFP - World Food Programme · Source: HDX · License: cc-by-igo · Updated: 2023-05-15

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Abstract

This dataset contains Food Prices data for Turkey, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the date column(s). Geographic scope: TUR.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Subnational administrative unit observations
Rows (total)	11,393
Columns	16 (4 numeric, 11 categorical, 1 datetime)
Train split	9,114 rows
Test split	2,278 rows
Geographic scope	TUR
Publisher	WFP - World Food Programme
HDX last updated	2023-05-15

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Variables

Geographic — admin1 (Ankara, Izmir, Istanbul), admin2 (Kecioren, Konak, Eminonu), latitude (range 38.4237–41.0126), longitude (range 27.1428–32.8597), category (vegetables and fruits, miscellaneous food, cereals and tubers) and 3 others.

Temporal — date.

Outcome / Measurement — priceflag (actual, #item+price+flag), price (range 0.41–275.49), usdprice (range 0.0805–29.967).

Identifier / Metadata — esa_source (HDX), esa_processed.

Other — market (National Average, Ankara, Izmir), unit (KG, L, Unit).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-markets-all")
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%
admin1	object	31.9%	Ankara, Izmir, Istanbul
admin2	object	31.9%	Kecioren, Konak, Eminonu
market	object	0.0%	National Average, Ankara, Izmir
latitude	float64	31.9%	38.4237 – 41.0126 (mean 39.7863)
longitude	float64	31.9%	27.1428 – 32.8597 (mean 29.6623)
category	object	0.0%	vegetables and fruits, miscellaneous food, cereals and tubers
commodity	object	0.0%	Bread (common), Pasta, Bulgur
unit	object	0.0%	KG, L, Unit
priceflag	object	0.0%	actual, #item+price+flag
pricetype	object	0.0%	Retail, #item+price+type
currency	object	0.0%	TRY, #currency
price	float64	0.0%	0.41 – 275.49 (mean 20.0666)
usdprice	float64	0.0%	0.0805 – 29.967 (mean 3.6539)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
latitude	38.4237	41.0126	39.7863	39.9334
longitude	27.1428	32.8597	29.6623	28.9667
price	0.41	275.49	20.0666	7.24
usdprice	0.0805	29.967	3.6539	1.3913

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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. 5 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: admin1, admin2, latitude, longitude.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_markets_all,
  title     = {Turkey - Food Prices},
  author    = {WFP - World Food Programme},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/wfp-food-prices-for-turkey},
  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.