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africa-senegal-mpi / README.md
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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: other
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - development
  - education
  - health
  - indicators
  - mortality
  - nutrition
  - poverty
  - socioeconomics
  - sen
pretty_name: Senegal Multidimensional Poverty Index
dataset_info:
  splits:
    - name: train
      num_examples: 12
    - name: test
      num_examples: 3

Senegal Multidimensional Poverty Index

Publisher: Oxford Poverty & Human Development Initiative · Source: HDX · License: other-pd-nr · Updated: 2026-03-05

Abstract

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the data tables are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes found here.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-05. Geographic scope: SEN.

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

Dataset Characteristics

Domain Public health
Unit of observation Country-level aggregates
Rows (total) 15
Columns 13 (5 numeric, 6 categorical, 0 datetime)
Train split 12 rows
Test split 3 rows
Geographic scope SEN
Publisher Oxford Poverty & Human Development Initiative
HDX last updated 2026-03-05

Variables

Geographic — country_iso3 (SEN), admin_1_pcode (SN01, SN02, SN03), admin_1_name (Dakar, Diourbel, Fatick), intensity_of_deprivation (range 41.2301–57.3021), vulnerable_to_poverty (range 9.0108–33.1552) and 2 others.

Temporal — start_date, end_date.

Outcome / Measurement — headcount_ratio (range 12.9108–85.7648).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

Other — mpi (range 0.0548–0.4915).

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-senegal-mpi")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
country_iso3 object 0.0% SEN
admin_1_pcode object 6.7% SN01, SN02, SN03
admin_1_name object 6.7% Dakar, Diourbel, Fatick
mpi float64 0.0% 0.0548 – 0.4915 (mean 0.2731)
headcount_ratio float64 0.0% 12.9108 – 85.7648 (mean 53.4142)
intensity_of_deprivation float64 0.0% 41.2301 – 57.3021 (mean 49.6203)
vulnerable_to_poverty float64 0.0% 9.0108 – 33.1552 (mean 20.3312)
in_severe_poverty float64 0.0% 3.4639 – 63.571 (mean 28.4148)
survey object 0.0% DHS
start_date datetime64[ns, UTC] 0.0%
end_date datetime64[ns, UTC] 0.0%
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-04

Numeric Summary

Column Min Max Mean Median
mpi 0.0548 0.4915 0.2731 0.2676
headcount_ratio 12.9108 85.7648 53.4142 54.3119
intensity_of_deprivation 41.2301 57.3021 49.6203 49.6238
vulnerable_to_poverty 9.0108 33.1552 20.3312 20.9365
in_severe_poverty 3.4639 63.571 28.4148 26.7985

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. 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.

Limitations

  • Data originates from Oxford Poverty & Human Development Initiative and has not been independently validated by ESA.
  • Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
  • Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation 

@dataset{hdx_africa_senegal_mpi,
  title     = {Senegal Multidimensional Poverty Index},
  author    = {Oxford Poverty & Human Development Initiative},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/senegal-mpi},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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