electricsheepafrica/africa-faostat-food-prices-for-united-republic-of-tanzania-the

United Republic of Tanzania (the) - Food Prices

Publisher: Food and Agriculture Organization (FAO) of the United Nations · Source: HDX · License: cc-by-igo · Updated: 2023-10-08

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Abstract

Food Prices for United Republic of Tanzania (the).

Contains data from the FAOSTAT bulk data service covering the following categories: Consumer Price Indices, Deflators, Exchange rates, Producer Prices

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the startdate, enddate column(s). Geographic scope: TZA.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	817
Columns	20 (7 numeric, 11 categorical, 2 datetime)
Train split	653 rows
Test split	163 rows
Geographic scope	TZA
Publisher	Food and Agriculture Organization (FAO) of the United Nations
HDX last updated	2023-10-08

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Variables

Geographic — iso3 (TZA, #country+code), year_code (range 2000.0–2022.0), year (range 2000.0–2022.0).

Temporal — startdate, enddate, months_code (range 7001.0–7012.0), months (January, February, March).

Outcome / Measurement — value (range 0.1078–149.5905).

Identifier / Metadata — area_code (range 215.0–215.0), area_code_m49 ('834), item_code (range 23012.0–23014.0), element_code (range 6121.0–6125.0), esa_source (HDX) and 1 others.

Other — area (United Republic of Tanzania, #country+name), item (Consumer Prices, Food Indices (2015 = 100), Consumer Prices, General Indices (2015 = 100), Food price inflation), element (Value), unit (Index, %, #indicator+type), flag (X, E, A) and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-faostat-food-prices-for-united-republic-of-tanzania-the")
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
iso3	object	0.0%	TZA, #country+code
startdate	datetime64[ns]	0.1%
enddate	datetime64[ns]	0.1%
area_code	float64	0.1%	215.0 – 215.0 (mean 215.0)
area_code_m49	object	0.1%	'834
area	object	0.0%	United Republic of Tanzania, #country+name
item_code	float64	0.1%	23012.0 – 23014.0 (mean 23012.9853)
item	object	0.0%	Consumer Prices, Food Indices (2015 = 100), Consumer Prices, General Indices (2015 = 100), Food price inflation
element_code	float64	0.1%	6121.0 – 6125.0 (mean 6123.7059)
element	object	0.1%	Value
months_code	float64	0.1%	7001.0 – 7012.0 (mean 7006.5)
months	object	0.1%	January, February, March
year_code	float64	0.1%	2000.0 – 2022.0 (mean 2011.1618)
year	float64	0.1%	2000.0 – 2022.0 (mean 2011.1618)
unit	object	0.0%	Index, %, #indicator+type
value	float64	0.1%	0.1078 – 149.5905 (mean 53.7692)
flag	object	0.1%	X, E, A
note	object	32.4%	base year is 2015
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
area_code	215.0	215.0	215.0	215.0
item_code	23012.0	23014.0	23012.9853	23013.0
element_code	6121.0	6125.0	6123.7059	6125.0
months_code	7001.0	7012.0	7006.5	7006.5
year_code	2000.0	2022.0	2011.1618	2011.0
year	2000.0	2022.0	2011.1618	2011.0
value	0.1078	149.5905	53.7692	39.9016

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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 Food and Agriculture Organization (FAO) of the United Nations 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: note.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_faostat_food_prices_for_united_republic_of_tanzania_the,
  title     = {United Republic of Tanzania (the) - Food Prices},
  author    = {Food and Agriculture Organization (FAO) of the United Nations},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/faostat-food-prices-for-united-republic-of-tanzania-the},
  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.