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README.md
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dataset_info:
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features:
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- name: iso3
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dtype: string
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- name: startdate
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dtype: timestamp[ns]
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- name: enddate
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dtype: timestamp[ns]
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- name: area_code
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dtype: int64
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- name: area_code_m49
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dtype: string
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- name: area
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dtype: string
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- name: item_code
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dtype: int64
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- name: item
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dtype: string
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- name: element_code
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dtype: int64
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- name: element
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dtype: string
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- name: year_code
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dtype: int64
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- name: year
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dtype: int64
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- name: unit
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dtype: string
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- name: value
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dtype: float64
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- name: flag
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dtype: string
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- name: esa_source
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dtype: string
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- name: esa_processed
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dtype: string
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splits:
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num_bytes: 16822
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num_examples: 88
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download_size: 22806
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dataset_size: 85628
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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---
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---
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annotations_creators:
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- no-annotation
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language_creators:
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- found
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language:
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- en
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license: cc-by-4.0
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multilinguality:
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- monolingual
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size_categories:
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- n<1K
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source_datasets:
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- original
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task_categories:
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- tabular-regression
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task_ids: []
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tags:
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- africa
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- humanitarian
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- hdx
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- electric-sheep-africa
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- food-security
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- indicators
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- tgo
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pretty_name: "Togo - Food Prices"
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dataset_info:
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splits:
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- name: train
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num_examples: 350
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- name: test
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num_examples: 87
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---
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# Togo - Food Prices
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**Publisher:** Food and Agriculture Organization (FAO) of the United Nations · **Source:** [HDX](https://data.humdata.org/dataset/faostat-food-prices-for-togo) · **License:** `cc-by-igo` · **Updated:** 2026-03-30
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---
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## Abstract
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Food Prices for Togo.
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Contains data from the FAOSTAT [bulk data service](https://fenixservices.fao.org/faostat/static/bulkdownloads/datasets_E.json) covering the following categories: Consumer Price Indices, Deflators, Exchange rates, Producer Prices
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Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `startdate`, `enddate` column(s). Geographic scope: **TGO**.
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*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
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---
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## Dataset Characteristics
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| | |
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|---|---|
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| **Domain** | Food security and nutrition |
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| **Unit of observation** | Country-level aggregates |
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| **Rows (total)** | 438 |
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| **Columns** | 17 (6 numeric, 9 categorical, 2 datetime) |
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| **Train split** | 350 rows |
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| **Test split** | 87 rows |
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| **Geographic scope** | TGO |
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| **Publisher** | Food and Agriculture Organization (FAO) of the United Nations |
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| **HDX last updated** | 2026-03-30 |
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---
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## Variables
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**Geographic** — `iso3` (TGO), `year_code` (range 1970.0–2024.0), `year` (range 1970.0–2024.0).
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**Temporal** — `startdate`, `enddate`.
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**Outcome / Measurement** — `value` (range 5.1618–174.9819).
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**Identifier / Metadata** — `area_code` (range 217.0–217.0), `area_code_m49` ('768), `item_code` (range 22024.0–22028.0), `element_code` (range 6179.0–62250.0), `esa_source` (HDX) and 1 others.
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**Other** — `area` (Togo), `item` (GDP Deflator, Gross Fixed Capital Formation Deflator, Value Added Deflator (Agriculture, forestry and fishery)), `element` (Value Standard Local Currency, 2015 prices, Value US$, 2015 prices), `unit` (SLC, USD), `flag` (X, E).
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---
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## Quick Start
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```python
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from datasets import load_dataset
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ds = load_dataset("electricsheepafrica/africa-faostat-food-prices-for-togo")
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train = ds["train"].to_pandas()
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test = ds["test"].to_pandas()
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print(train.shape)
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train.head()
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```
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---
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## Schema
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| Column | Type | Null % | Range / Sample Values |
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|---|---|---|---|
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| `iso3` | object | 0.0% | TGO |
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| `startdate` | datetime64[ns] | 0.0% | |
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| `enddate` | datetime64[ns] | 0.0% | |
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| `area_code` | int64 | 0.0% | 217.0 – 217.0 (mean 217.0) |
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| `area_code_m49` | object | 0.0% | '768 |
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| `area` | object | 0.0% | Togo |
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| `item_code` | int64 | 0.0% | 22024.0 – 22028.0 (mean 22025.7397) |
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| `item` | object | 0.0% | GDP Deflator, Gross Fixed Capital Formation Deflator, Value Added Deflator (Agriculture, forestry and fishery) |
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| `element_code` | int64 | 0.0% | 6179.0 – 62250.0 (mean 34214.5) |
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| `element` | object | 0.0% | Value Standard Local Currency, 2015 prices, Value US$, 2015 prices |
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| `year_code` | int64 | 0.0% | 1970.0 – 2024.0 (mean 1996.8767) |
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| `year` | int64 | 0.0% | 1970.0 – 2024.0 (mean 1996.8767) |
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| `unit` | object | 0.0% | SLC, USD |
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| `value` | float64 | 0.0% | 5.1618 – 174.9819 (mean 71.14) |
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| `flag` | object | 0.0% | X, E |
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| `esa_source` | object | 0.0% | HDX |
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| `esa_processed` | object | 0.0% | 2026-04-06 |
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---
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## Numeric Summary
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| Column | Min | Max | Mean | Median |
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|---|---|---|---|---|
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| `area_code` | 217.0 | 217.0 | 217.0 | 217.0 |
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| `item_code` | 22024.0 | 22028.0 | 22025.7397 | 22025.0 |
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| `element_code` | 6179.0 | 62250.0 | 34214.5 | 34214.5 |
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| `year_code` | 1970.0 | 2024.0 | 1996.8767 | 1997.0 |
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| `year` | 1970.0 | 2024.0 | 1996.8767 | 1997.0 |
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| `value` | 5.1618 | 174.9819 | 71.14 | 72.3837 |
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---
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## Curation
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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`. 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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---
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## Limitations
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- Data originates from Food and Agriculture Organization (FAO) of the United Nations and has not been independently validated by ESA.
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- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
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- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/faostat-food-prices-for-togo) for the publisher's own methodology notes and caveats.
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---
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## Citation
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```bibtex
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@dataset{hdx_africa_faostat_food_prices_for_togo,
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title = {Togo - Food Prices},
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author = {Food and Agriculture Organization (FAO) of the United Nations},
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year = {2026},
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url = {https://data.humdata.org/dataset/faostat-food-prices-for-togo},
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note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
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}
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```
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---
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*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*
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