year float64 2.01k 2.01k ⌀ | country stringclasses 7
values | pin stringclasses 7
values | fs_pin stringlengths 7 9 ⌀ | fs_tar stringlengths 7 9 ⌀ | nut_pin stringlengths 7 9 ⌀ | nut_tar stringlengths 7 9 ⌀ | health_pin stringlengths 7 10 ⌀ | healthtar stringlengths 7 10 ⌀ | wash_pin stringlengths 7 9 ⌀ | wash_tar stringlengths 7 9 ⌀ | edu_pin stringlengths 7 9 ⌀ | edu_tar stringlengths 7 9 ⌀ | shelter_nfi_pin stringclasses 9
values | shelter_nfi_tar stringlengths 7 9 ⌀ | protection_pin stringclasses 7
values | protection_tar stringclasses 9
values | multi_sector_pin stringclasses 9
values | multi_sector_tar stringclasses 9
values | mine_action_pin stringclasses 6
values | mine_action_tar stringclasses 6
values | source stringlengths 8 23 ⌀ | esa_source stringclasses 1
value | esa_processed stringdate 2026-04-14 00:00:00 2026-04-14 00:00:00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,012 | SUD | null | 7,000,807 | 5,098,823 | 4,420,000 | 2,812,000 | 11,058,279 | 11,058,279 | 4,183,000 | 4,183,000 | 8,346,329 | 1,740,998 | 415,208 | 351,874 | 1,180,352 | 628,307 | 164,274 | 164,274 | 1,344,362 | 724,287 | MYR 2012 | HDX | 2026-04-14 |
2,013 | KEN | 1,700,000 | 2,100,000 | 1,981,000 | 2,999,937 | 2,262,668 | 5,000,000 | 4,500,000 | 2,500,000 | 2,500,000 | 566,217 | 340,752 | 877,429 | 831,000 | null | null | 998,803 | 646,119 | null | null | June 2013, MYR 2013 | HDX | 2026-04-14 |
2,011 | SOM | null | 3,200,000 | 2,600,000 | 460,893 | null | 1,934,000 | 1,934,000 | 2,000,000 | 1,670,000 | 460,000 | 435,000 | 1,242,000 | 1,212,500 | 1,460,000 | 2,556,404 | null | null | null | null | CAP 2011 | HDX | 2026-04-14 |
2,014 | DJI | 300,000 | 257,000 | 142,000 | 277,786 | 196,306 | 300,000 | 189,000 | 300,000 | 139,000 | null | null | null | null | null | null | 127,500 | 87,500 | null | null | April 2014, SRP 2014-15 | HDX | 2026-04-14 |
2,011 | ETH | null | null | null | 3,500,000 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | OCHA Ethiopia | HDX | 2026-04-14 |
2,011 | SUD | null | 7,071,000 | 3,560,000 | 7,997,190 | 5,101,500 | 11,544,400 | 11,544,400 | 4,489,000 | 4,489,000 | 8,926,000 | 1,240,000 | 222,000 | 222,000 | 6,459,145 | 4,442,500 | null | null | 1,138,980 | 1,138,980 | Sudan Work Plan 2011 | HDX | 2026-04-14 |
2,012 | ETH | null | null | null | 3,900,000 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | OCHA Ethiopia | HDX | 2026-04-14 |
2,013 | SUD | null | 7,236,500 | 5,515,210 | 5,078,153 | 3,403,637 | 6,059,692 | 4,244,322 | 6,071,000 | 2,494,065 | 3,080,159 | 1,027,198 | 1,358,140 | 1,358,140 | null | 4,301,831 | 157,600 | 157,600 | 2,176,443 | 1,546,943 | May 2013, MYR 2013 | HDX | 2026-04-14 |
2,011 | SSD | null | 2,550,000 | 1,206,000 | 827,551 | 278,357 | 3,304,197 | 3,316,425 | 2,880,000 | null | 754,616 | 228,217 | null | null | null | null | null | null | 2,985,905 | 2,985,905 | MYR 2011 | HDX | 2026-04-14 |
2,011 | DJI | null | 210,000 | 179,000 | 172,500 | 107,000 | 222,500 | 164,800 | 210,000 | 179,000 | null | null | null | null | null | null | 43,000 | 42,400 | null | null | null | HDX | 2026-04-14 |
2,014 | SUD | 6,100,000 | 6,100,000 | 5,400,000 | 4,600,000 | 899,000 | 6,100,000 | 5,100,000 | 3,300,000 | 3,000,000 | 2,700,000 | 826,100 | 1,800,000 | 1,200,000 | 4,200,000 | 3,700,000 | 368,800 | 359,800 | null | null | Dec 2013, SRP 2014 | HDX | 2026-04-14 |
2,013 | DJI | null | 139,000 | 139,000 | 344,545 | 149,121 | 152,000 | 152,000 | 400,000 | 152,000 | null | null | null | null | null | null | 91,000 | 91,000 | null | null | null | HDX | 2026-04-14 |
2,014 | SSD | 11,600,000 | 4,300,000 | 2,400,000 | 3,600,000 | 3,100,000 | 5,800,000 | 3,100,000 | 5,900,000 | 3,800,000 | 993,300 | 275,000 | 1,900,000 | 1,000,000 | 5,600,000 | 1,200,000 | 427,000 | 427,000 | 3,800,000 | 1,100,000 | June 2014, CRP 2014 | HDX | 2026-04-14 |
2,013 | ETH | null | null | null | 3,900,000 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | OCHA Ethiopia | HDX | 2026-04-14 |
2,014 | SSRP | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | OCHA Ethiopia | HDX | 2026-04-14 |
2,012 | KEN | 2,100,000 | 2,200,000 | 2,200,000 | 362,000 | 362,000 | 3,200,000 | 900,000 | 1,941,000 | 1,959,000 | 1,555,000 | 871,000 | null | null | null | null | null | null | null | null | EHRP 2012 | HDX | 2026-04-14 |
2,012 | SSD | null | 4,700,000 | 2,414,930 | 1,194,876 | 817,992 | 3,587,318 | 3,587,318 | 3,380,000 | 2,610,000 | 463,707 | 324,594 | 681,000 | 525,000 | 740,000 | 680,000 | 614,500 | 614,500 | 9,547,257 | 9,547,257 | MYR 2012 | HDX | 2026-04-14 |
2,013 | SOM | 3,800,000 | 4,748,000 | 3,995,000 | 1,944,500 | 591,000 | 7,770,000 | 3,549,955 | 4,239,188 | 1,950,398 | 848,000 | 600,000 | 1,100,000 | 750,000 | 1,110,000 | 850,000 | null | null | null | null | June 2013, MYR 2013 | HDX | 2026-04-14 |
2,014 | SOM | 3,180,000 | 3,170,000 | 3,170,000 | 756,000 | 660,000 | 3,170,000 | 1,770,000 | 2,750,000 | 1,650,000 | 1,740,000 | 660,000 | 1,100,000 | 495,000 | 1,110,000 | 560,000 | null | null | null | null | Dec 2013, SRP 2014 | HDX | 2026-04-14 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | HDX | 2026-04-14 |
Eastern Africa Region People in Need Per Sector 2011-2015
Publisher: OCHA Regional Office for Southern and Eastern Africa (ROSEA) · Source: HDX · License: cc-by-igo · Updated: 2023-09-28
Abstract
Data on people in need per sector in Kenya, Somalia, Sudan, South Sudan and Ethiopia from 2011 to 2015
Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2023-09-28. Geographic scope: DJI, ETH, KEN, SSD, SDN.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Public health |
| Unit of observation | Country-level aggregates |
| Rows (total) | 25 |
| Columns | 24 (1 numeric, 23 categorical, 0 datetime) |
| Train split | 20 rows |
| Test split | 5 rows |
| Geographic scope | DJI, ETH, KEN, SSD, SDN |
| Publisher | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| HDX last updated | 2023-09-28 |
Variables
Geographic — year (range 2011.0–2014.0), country (SOM, SUD, SSD).
Identifier / Metadata — source, esa_source, esa_processed.
Other — pin (2,100,000, 1,700,000, 3,800,000), fs_pin (3,750,000, 3,200,000, 257,000), fs_tar (2,200,000, 3,750,000, 1,981,000), nut_pin (3,900,000, 172,500, 2,999,937), nut_tar (107,000, 475,000, 591,000) and 14 others.
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-eastern-africa-region-people-in-need-per-sector-2011-2014")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
year |
float64 | 4.0% | 2011.0 – 2014.0 (mean 2012.5) |
country |
object | 4.0% | SOM, SUD, SSD |
pin |
object | 68.0% | 2,100,000, 1,700,000, 3,800,000 |
fs_pin |
object | 20.0% | 3,750,000, 3,200,000, 257,000 |
fs_tar |
object | 24.0% | 2,200,000, 3,750,000, 1,981,000 |
nut_pin |
object | 8.0% | 3,900,000, 172,500, 2,999,937 |
nut_tar |
object | 28.0% | 107,000, 475,000, 591,000 |
health_pin |
object | 20.0% | 222,500, 7,500,000, 7,770,000 |
healthtar |
object | 24.0% | 164,800, 3,700,000, 3,549,955 |
wash_pin |
object | 20.0% | 3,751,000, 2,000,000, 300,000 |
wash_tar |
object | 28.0% | 2,600,000, 2,549,000, 2,500,000 |
edu_pin |
object | 36.0% | |
edu_tar |
object | 40.0% | |
shelter_nfi_pin |
object | 52.0% | |
shelter_nfi_tar |
object | 52.0% | |
protection_pin |
object | 60.0% | |
protection_tar |
object | 56.0% | |
multi_sector_pin |
object | 56.0% | |
multi_sector_tar |
object | 56.0% | |
mine_action_pin |
object | 72.0% | |
mine_action_tar |
object | 72.0% | |
source |
object | 16.0% | |
esa_source |
object | 0.0% | |
esa_processed |
object | 0.0% |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
year |
2011.0 | 2014.0 | 2012.5 | 2012.5 |
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. 20 column(s) with >80% missing values were removed: tar, rch, fs_rch, nut_rch, healthrch, wash_rch.... 2 exact duplicate rows were removed. 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) 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:
pin,fs_tar,nut_tar,healthtar,wash_tar,edu_pin,edu_tar,shelter_nfi_pin.... - This dataset spans 5 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.
Citation
@dataset{hdx_africa_eastern_africa_region_people_in_need_per_sector_2011_2014,
title = {Eastern Africa Region People in Need Per Sector 2011-2015},
author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
year = {2023},
url = {https://data.humdata.org/dataset/eastern-africa-region-people-in-need-per-sector-2011-2014},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.
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