README.md

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-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - energy
  - food-security
  - lby
pretty_name: Libya - Real Time Prices
dataset_info:
  splits:
    - name: train
      num_examples: 3729
    - name: test
      num_examples: 932

Libya - Real Time Prices

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-04-01

---

Abstract

Real Time Prices (RTP) is a live dataset compiled and updated weekly by the World Bank Development Economics Data Group (DECDG) using a combination of direct price measurement and Machine Learning estimation of missing price data. The historical and current estimates are based on price information gathered from the World Food Program (WFP), UN-Food and Agricultural Organization (FAO), select National Statistical Offices, and are continually updated and revised as more price information becomes available. Real-time exchange rate data used in this process are from official and public sources.

RTP includes three sub-series, Real Time Food Prices (RTFP) includes prices on a variety of food items that primarily include country-specific staple foods, Real Time Energy Prices (RTEP) includes fuel prices, and Real Time Exchange Rates (RTFX) and includes unofficial exchange rate estimates as well as possible other unofficial deflators.

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

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

---

Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	4,662
Columns	185 (172 numeric, 10 categorical, 3 datetime)
Train split	3,729 rows
Test split	932 rows
Geographic scope	LBY
Publisher	World Bank Group
HDX last updated	2026-04-01

---

Variables

Geographic — iso3 (LBY), country (Libya), lat (range 24.2–32.92), lon (range 9.49–23.96), year (range 2017.0–2026.0) and 33 others.

Temporal — dates, month (range 1.0–12.0).

Demographic — data_coverage (range 49.44–49.44), data_coverage_recent (range 30.33–30.33).

Identifier / Metadata — adm1_name (West, South, East), adm2_name (Tripoli, Al Jabal Al Gharbi, Murzuq), mkt_name (Abusliem, Tarhuna, Murzuq), geo_id (gid_328700000132300000, gid_324300000136400000, gid_259100000139200000), esa_source (HDX) and 1 others.

Other — components (beans (400 G, Index Weight = 10), beans_fao (400 gms, Index Weight = 0.01), bread (5 pcs, Index Weight = 16), chickpeas (400 G, Index Weight = 20), chili (1 KG, Index Weight = 8), couscous (1 KG, Index Weight = 8), eggs (30 pcs, Index Weight = 2.67), fish_tuna_canned (200 G, Index Weight = 40), meat_chicken (1 KG, Index Weight = 8), meat_lamb (1 KG, Index Weight = 8), milk (1 L, Index Weight = 8), oil (1 L, Index Weight = 8), onions (1 KG, Index Weight = 4), onions_fao (1 Kg, Index Weight = 4), pasta (500 G, Index Weight = 16), potatoes (1 KG, Index Weight = 8), rice (1 KG, Index Weight = 8), salt (1 KG, Index Weight = 8), sugar (1 KG, Index Weight = 8), tea (250 G, Index Weight = 32), tomatoes (1 KG, Index Weight = 8), tomatoes_paste (400 G, Index Weight = 20), wheat_flour (1 KG, Index Weight = 8)), start_dense_data, beans (range 0.83–6.12), bread (range 0.31–6.25), chickpeas (range 0.44–8.0) and 132 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-libya-real-time-prices")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
iso3	object	0.0%	LBY
country	object	0.0%	Libya
adm1_name	object	0.0%	West, South, East
adm2_name	object	0.0%	Tripoli, Al Jabal Al Gharbi, Murzuq
mkt_name	object	0.0%	Abusliem, Tarhuna, Murzuq
lat	float64	2.4%	24.2 – 32.92 (mean 30.9788)
lon	float64	2.4%	9.49 – 23.96 (mean 14.7739)
geo_id	object	0.0%	gid_328700000132300000, gid_324300000136400000, gid_259100000139200000
dates	datetime64[ns]	0.0%
year	int64	0.0%	2017.0 – 2026.0 (mean 2021.1351)
month	int64	0.0%	1.0 – 12.0 (mean 6.3784)
currency	object	0.0%	LYD
components	object	0.0%	beans (400 G, Index Weight = 10), beans_fao (400 gms, Index Weight = 0.01), bread (5 pcs, Index Weight = 16), chickpeas (400 G, Index Weight = 20), chili (1 KG, Index Weight = 8), couscous (1 KG, Index Weight = 8), eggs (30 pcs, Index Weight = 2.67), fish_tuna_canned (200 G, Index Weight = 40), meat_chicken (1 KG, Index Weight = 8), meat_lamb (1 KG, Index Weight = 8), milk (1 L, Index Weight = 8), oil (1 L, Index Weight = 8), onions (1 KG, Index Weight = 4), onions_fao (1 Kg, Index Weight = 4), pasta (500 G, Index Weight = 16), potatoes (1 KG, Index Weight = 8), rice (1 KG, Index Weight = 8), salt (1 KG, Index Weight = 8), sugar (1 KG, Index Weight = 8), tea (250 G, Index Weight = 32), tomatoes (1 KG, Index Weight = 8), tomatoes_paste (400 G, Index Weight = 20), wheat_flour (1 KG, Index Weight = 8)
start_dense_data	datetime64[ns]	0.0%
last_survey_point	datetime64[ns]	0.0%
data_coverage	float64	0.0%	49.44 – 49.44 (mean 49.44)
data_coverage_recent	float64	0.0%	30.33 – 30.33 (mean 30.33)
index_confidence_score	float64	0.0%	0.91 – 0.91 (mean 0.91)
spatially_interpolated	int64	0.0%	0.0 – 0.0 (mean 0.0)
beans	float64	62.1%	0.83 – 6.12 (mean 2.6651)
bread	float64	58.6%	0.31 – 6.25 (mean 1.3812)
chickpeas	float64	60.8%	0.44 – 8.0 (mean 2.6052)
couscous	float64	53.9%	1.25 – 18.88 (mean 4.8176)
eggs	float64	58.2%	1.17 – 27.0 (mean 13.4605)
fish_tuna_canned	float64	51.5%	1.5 – 12.18 (mean 4.3806)
meat_chicken	float64	50.0%	2.5 – 42.0 (mean 12.7803)
meat_lamb	float64	62.6%	2.0 – 100.0 (mean 43.5603)
milk	float64	56.9%	1.38 – 10.0 (mean 4.4341)
oil	float64	54.1%	0.01 – 17.0 (mean 7.0906)
onions	float64	61.5%	0.75 – 9.0 (mean 2.7086)
pasta	float64	61.3%	0.94 – 7.5 (mean 2.1179)
potatoes	float64	51.9%
rice	float64	52.1%
salt	float64	58.3%
sugar	float64	61.0%
tea	float64	64.3%
tomatoes	float64	49.5%
tomatoes_paste	float64	54.7%
wheat_flour	float64	50.7%
o_beans	float64	0.0%
h_beans	float64	0.0%
l_beans	float64	0.0%
c_beans	float64	0.0%
inflation_beans	float64	10.8%
trust_beans	float64	0.0%
o_beans_fao	float64	0.0%
h_beans_fao	float64	0.0%
l_beans_fao	float64	0.0%
c_beans_fao	float64	0.0%
inflation_beans_fao	float64	10.8%
trust_beans_fao	float64	0.0%
o_bread	float64	0.0%
h_bread	float64	0.0%
l_bread	float64	0.0%
c_bread	float64	0.0%
inflation_bread	float64	10.8%
trust_bread	float64	0.0%
o_chickpeas	float64	0.0%
h_chickpeas	float64	0.0%
l_chickpeas	float64	0.0%
c_chickpeas	float64	0.0%
inflation_chickpeas	float64	10.8%
trust_chickpeas	float64	0.0%
o_chili	float64	0.0%
h_chili	float64	0.0%
l_chili	float64	0.0%
c_chili	float64	0.0%
inflation_chili	float64	10.8%
trust_chili	float64	0.0%
o_couscous	float64	0.0%
h_couscous	float64	0.0%
l_couscous	float64	0.0%
c_couscous	float64	0.0%
inflation_couscous	float64	10.8%
trust_couscous	float64	0.0%
o_eggs	float64	0.0%
h_eggs	float64	0.0%
l_eggs	float64	0.0%
c_eggs	float64	0.0%
inflation_eggs	float64	10.8%
trust_eggs	float64	0.0%
o_fish_tuna_canned	float64	0.0%
h_fish_tuna_canned	float64	0.0%
l_fish_tuna_canned	float64	0.0%
c_fish_tuna_canned	float64	0.0%
inflation_fish_tuna_canned	float64	10.8%
trust_fish_tuna_canned	float64	0.0%
o_meat_chicken	float64	0.0%
h_meat_chicken	float64	0.0%
l_meat_chicken	float64	0.0%
c_meat_chicken	float64	0.0%
inflation_meat_chicken	float64	10.8%
trust_meat_chicken	float64	0.0%
o_meat_lamb	float64	0.0%
h_meat_lamb	float64	0.0%
l_meat_lamb	float64	0.0%
c_meat_lamb	float64	0.0%
inflation_meat_lamb	float64	10.8%
trust_meat_lamb	float64	0.0%
o_milk	float64	0.0%
h_milk	float64	0.0%
l_milk	float64	0.0%
c_milk	float64	0.0%
inflation_milk	float64	10.8%
trust_milk	float64	0.0%
o_oil	float64	0.0%
h_oil	float64	0.0%
l_oil	float64	0.0%
c_oil	float64	0.0%
inflation_oil	float64	10.8%
trust_oil	float64	0.0%
o_onions	float64	0.0%
h_onions	float64	0.0%
l_onions	float64	0.0%
c_onions	float64	0.0%
inflation_onions	float64	10.8%
trust_onions	float64	0.0%
o_onions_fao	float64	0.0%
h_onions_fao	float64	0.0%
l_onions_fao	float64	0.0%
c_onions_fao	float64	0.0%
inflation_onions_fao	float64	10.8%
trust_onions_fao	float64	0.0%
o_pasta	float64	0.0%
h_pasta	float64	0.0%
l_pasta	float64	0.0%
c_pasta	float64	0.0%
inflation_pasta	float64	10.8%
trust_pasta	float64	0.0%
o_potatoes	float64	0.0%
h_potatoes	float64	0.0%
l_potatoes	float64	0.0%
c_potatoes	float64	0.0%
inflation_potatoes	float64	10.8%
trust_potatoes	float64	0.0%
o_rice	float64	0.0%
h_rice	float64	0.0%
l_rice	float64	0.0%
c_rice	float64	0.0%
inflation_rice	float64	10.8%
trust_rice	float64	0.0%
o_salt	float64	0.0%
h_salt	float64	0.0%
l_salt	float64	0.0%
c_salt	float64	0.0%
inflation_salt	float64	10.8%
trust_salt	float64	0.0%
o_sugar	float64	0.0%
h_sugar	float64	0.0%
l_sugar	float64	0.0%
c_sugar	float64	0.0%
inflation_sugar	float64	10.8%
trust_sugar	float64	0.0%
o_tea	float64	0.0%
h_tea	float64	0.0%
l_tea	float64	0.0%
c_tea	float64	0.0%
inflation_tea	float64	10.8%
trust_tea	float64	0.0%
o_tomatoes	float64	0.0%
h_tomatoes	float64	0.0%
l_tomatoes	float64	0.0%
c_tomatoes	float64	0.0%
inflation_tomatoes	float64	10.8%
trust_tomatoes	float64	0.0%
o_tomatoes_paste	float64	0.0%
h_tomatoes_paste	float64	0.0%
l_tomatoes_paste	float64	0.0%
c_tomatoes_paste	float64	0.0%
inflation_tomatoes_paste	float64	10.8%
trust_tomatoes_paste	float64	0.0%
o_wheat_flour	float64	0.0%
h_wheat_flour	float64	0.0%
l_wheat_flour	float64	0.0%
c_wheat_flour	float64	0.0%
inflation_wheat_flour	float64	10.8%
trust_wheat_flour	float64	0.0%
o_food_price_index	float64	0.0%
h_food_price_index	float64	0.0%
l_food_price_index	float64	0.0%
c_food_price_index	float64	0.0%
inflation_food_price_index	float64	10.8%
trust_food_price_index	float64	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

---

Numeric Summary

Column	Min	Max	Mean	Median
lat	24.2	32.92	30.9788	32.33
lon	9.49	23.96	14.7739	13.35
year	2017.0	2026.0	2021.1351	2021.0
month	1.0	12.0	6.3784	6.0
data_coverage	49.44	49.44	49.44	49.44
data_coverage_recent	30.33	30.33	30.33	30.33
index_confidence_score	0.91	0.91	0.91	0.91
spatially_interpolated	0.0	0.0	0.0	0.0
beans	0.83	6.12	2.6651	2.5
bread	0.31	6.25	1.3812	1.25
chickpeas	0.44	8.0	2.6052	2.5
couscous	1.25	18.88	4.8176	4.0
eggs	1.17	27.0	13.4605	13.25
fish_tuna_canned	1.5	12.18	4.3806	4.25
meat_chicken	2.5	42.0	12.7803	11.48

---

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. 675 column(s) with >80% missing values were removed: apples, bananas, beans_egyptian, beans_fao, bread_fao, bulgur.... 3 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 World Bank Group 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: beans, bread, chickpeas, couscous, eggs, fish_tuna_canned, meat_chicken, meat_lamb....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_libya_real_time_prices,
  title     = {Libya - Real Time Prices},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/libya-real-time-prices},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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