Africa · Humanitarian Response
Collection
Humanitarian needs, response plans, and funding data for Africa. Sources: OCHA HRP, IFRC appeals, 5W matrices, and multi-sector needs assessments. • 150 items • Updated
year int64 2.01k 2.02k | name stringclasses 10
values | pin int64 0 11.6M | tar int64 0 11.6M | committed int64 0 1.8B | funded int64 0 1.44B | refugees int64 0 661k | idps int64 0 3.1M | inform float64 0 9.01 | fs int64 0 7.24M | nutrition int64 0 5.08M | health int64 0 7.5M | wash int64 0 4.5M | education int64 0 3.08M | hazards float64 0 9.55 | vulnerability float64 0 8.93 | copingcapacity float64 0 9.57 | priorities stringclasses 9
values | esa_source stringclasses 1
value | esa_processed stringdate 2026-04-16 00:00:00 2026-04-16 00:00:00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,013 | ERI | 0 | 0 | 0 | 0 | 0 | 0 | 4.7 | 0 | 0 | 0 | 0 | 0 | 2.1 | 6.23 | 7.94 | Not Available | HDX | 2026-04-16 |
2,015 | BRN | 0 | 0 | 307,000,000 | 26,000,000 | 57,000 | 78,948 | 0.76 | 0 | 0 | 0 | 0 | 0 | 4.59 | 0.81 | 0.12 | null | HDX | 2026-04-16 |
2,013 | SSD | 4,500,000 | 3,000,000 | 1,072,037,430 | 773,745,283 | 224,930 | 159,134 | 6.96 | 4,100,000 | 3,700,000 | 3,500,000 | 0 | 255,000 | 6.96 | 6.41 | 7.56 | 2013 Data not Available | HDX | 2026-04-16 |
2,014 | DJI | 300,000 | 250,000 | 74,085,087 | 20,563,729 | 24,425 | 0 | 4.65 | 257,000 | 277,786 | 300,000 | 0 | 0 | 2.8 | 5.15 | 6.99 | 2014 Data not Available | HDX | 2026-04-16 |
2,014 | BRN | 0 | 0 | 0 | 0 | 54,179 | 78,948 | 0.76 | 0 | 0 | 0 | 0 | 0 | 0.12 | 0.79 | 4.62 | Not Available | HDX | 2026-04-16 |
2,012 | DJI | 206,000 | 206,000 | 79,310,556 | 31,661,994 | 18,658 | 0 | 4.94 | 206,000 | 172,500 | 222,500 | 2,500,000 | 0 | 2.82 | 5.83 | 7.34 | 2012 Data not Available | HDX | 2026-04-16 |
2,012 | UGA | 0 | 0 | 0 | 0 | 172,923 | 30,136 | 6.28 | 0 | 0 | 0 | 0 | 0 | 5.43 | 6.09 | 7.49 | 2012 Data not Available | HDX | 2026-04-16 |
2,012 | RWA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | HDX | 2026-04-16 |
2,013 | RWA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | HDX | 2026-04-16 |
2,011 | ETH | 3,500,000 | 3,500,000 | 454,400,000 | 290,600,000 | 254,905 | 325,000 | 6.74 | 3,500,000 | 3,500,000 | 0 | 4,000,000 | 0 | 5.8 | 6.43 | 8.19 | 2012 Data not Available | HDX | 2026-04-16 |
2,015 | DJI | 140,000 | 250,000 | 82,000,000 | 8,200,000 | 17,400 | 0 | 4.49 | 257,000 | 277,786 | 300,000 | 0 | 0 | 6.76 | 4.81 | 2.79 | Not Available | HDX | 2026-04-16 |
2,012 | ETH | 3,900,000 | 3,900,000 | 189,400,000 | 133,700,000 | 367,000 | 350,000 | 6.55 | 3,900,000 | 3,900,000 | 0 | 4,239,188 | 0 | 5.25 | 6.6 | 8.12 | 2012 Data not Available | HDX | 2026-04-16 |
2,015 | KEN | 1,500,000 | 1,500,000 | 0 | 0 | 593,000 | 309,200 | 6.21 | 1,500,000 | 0 | 0 | 0 | 0 | 6.57 | 6.32 | 5.78 | Transformational governance, human capital development, inclusive and sustainable economic growth, environmental sustainability, land management and human security. | HDX | 2026-04-16 |
2,015 | SOM | 3,200,000 | 2,760,000 | 862,579,628 | 281,000,000 | 13,200 | 1,110,000 | 8.83 | 3,000,000 | 1,300,000 | 3,200,000 | 300,000 | 1,700,000 | 9.55 | 8.36 | 8.63 | Inclusive politics, security, justice, economic foundations, revenue and services | HDX | 2026-04-16 |
2,012 | SOM | 4,097,000 | 4,097,000 | 1,164,634,356 | 589,872,503 | 2,128 | 1,360,000 | 9.01 | 4,097,000 | 834,800 | 4,000,000 | 206,000 | 1,800,000 | 8.64 | 8.93 | 9.49 | 2012 Data not Available | HDX | 2026-04-16 |
2,011 | BRN | 0 | 0 | 0 | 0 | 0 | 0 | 0.73 | 0 | 0 | 0 | 0 | 0 | 0.12 | 0.78 | 4.19 | Not Available | HDX | 2026-04-16 |
2,015 | UGA | 23,000 | 23,000 | 0 | 0 | 433,029 | 30,136 | 6.41 | 0 | 23,000 | 30,000 | 0 | 100,000 | 7.15 | 5.99 | 6.16 | Governance and human rights, sustainable livelihoods, quality social services. | HDX | 2026-04-16 |
2,013 | SDN | 7,236,500 | 4,000,000 | 985,120,878 | 549,458,369 | 163,900 | 2,900,000 | 7.58 | 7,236,500 | 5,078,153 | 6,059,692 | 0 | 3,080,159 | 7.29 | 7.6 | 7.84 | 2013 Data not Available | HDX | 2026-04-16 |
2,014 | SOM | 3,180,000 | 2,000,000 | 933,070,303 | 458,027,750 | 2,669 | 1,106,751 | 9.01 | 3,170,000 | 756,000 | 3,170,000 | 300,000 | 1,740,000 | 8.63 | 8.85 | 9.57 | 2014 Data not Available | HDX | 2026-04-16 |
2,011 | DJI | 210,000 | 210,000 | 33,264,338 | 19,370,114 | 20,611 | 0 | 4.93 | 210,000 | 172,500 | 222,500 | 1,941,000 | 0 | 2.81 | 5.74 | 7.43 | 2011 Data not Available | HDX | 2026-04-16 |
2,015 | SDN | 6,100,000 | 5,400,000 | 1,035,894,093 | 393,000,000 | 229,226 | 3,100,000 | 7.24 | 3,500,000 | 0 | 0 | 0 | 0 | 7.26 | 7.18 | 7.29 | Not Available | HDX | 2026-04-16 |
2,012 | ERI | 0 | 0 | 0 | 0 | 0 | 0 | 4.84 | 0 | 0 | 0 | 0 | 0 | 2.22 | 6.34 | 8.04 | Not Available | HDX | 2026-04-16 |
2,012 | SSD | 4,700,000 | 4,700,000 | 1,156,130,815 | 570,658,539 | 207,492 | 170,000 | 7.72 | 4,700,000 | 1,194,876 | 3,587,318 | 0 | 463,707 | 7.3 | 7.78 | 8.09 | 2012 Data not Available | HDX | 2026-04-16 |
2,012 | BRN | 0 | 0 | 0 | 0 | 0 | 0 | 0.72 | 0 | 0 | 0 | 0 | 0 | 0.12 | 0.77 | 4.16 | Not Available | HDX | 2026-04-16 |
2,012 | KEN | 2,100,000 | 2,100,000 | 795,005,122 | 411,119,116 | 586,068 | 309,200 | 6.31 | 2,200,000 | 362,000 | 3,200,000 | 3,380,000 | 1,555,000 | 5.13 | 6.83 | 7.16 | 2012 Data not Available | HDX | 2026-04-16 |
2,013 | BRN | 0 | 0 | 0 | 0 | 0 | 0 | 0.76 | 0 | 0 | 0 | 0 | 0 | 0.12 | 0.77 | 4.75 | Not Available | HDX | 2026-04-16 |
2,014 | UGA | 452,523 | 452,523 | 0 | 0 | 422,435 | 30,136 | 6.41 | 0 | 452,523 | 90,000 | 0 | 20,000 | 6.16 | 5.98 | 7.16 | 2014 Data not Available | HDX | 2026-04-16 |
2,011 | SSD | 2,550,000 | 2,550,000 | 619,673,235 | 377,760,780 | 0 | 0 | 7.81 | 2,550,000 | 827,551 | 3,304,197 | 0 | 754,616 | 6.99 | 8.31 | 8.18 | 2011 Data not Available | HDX | 2026-04-16 |
2,014 | KEN | 0 | 0 | 0 | 0 | 551,352 | 309,200 | 6.09 | 0 | 0 | 0 | 0 | 0 | 5.78 | 6.53 | 5.97 | 2014 Data not Available | HDX | 2026-04-16 |
2,011 | UGA | 0 | 0 | 0 | 0 | 133,115 | 73,239 | 5.74 | 0 | 0 | 0 | 0 | 0 | 3.78 | 6.59 | 7.57 | 2012 Data not Available | HDX | 2026-04-16 |
2,011 | ERI | 0 | 0 | 0 | 0 | 0 | 0 | 4.75 | 0 | 0 | 0 | 0 | 0 | 2 | 6.76 | 7.92 | Not Available | HDX | 2026-04-16 |
2,013 | KEN | 1,700,000 | 1,700,000 | 69,982,984 | 24,753,575 | 530,959 | 309,200 | 6.2 | 2,100,000 | 2,999,937 | 5,000,000 | 4,500,000 | 566,217 | 5.78 | 6.72 | 6.13 | 2013 Data not Available | HDX | 2026-04-16 |
2,014 | ETH | 2,736,490 | 2,736,490 | 451,900,000 | 271,140,000 | 660,987 | 834,959 | 6.58 | 2,736,490 | 264,298 | 6,800,000 | 2,750,000 | 522,799 | 5.32 | 6.43 | 8.33 | 2014 Data not Available | HDX | 2026-04-16 |
2,015 | RWA | 0 | 0 | 0 | 0 | 137,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | HDX | 2026-04-16 |
2,011 | KEN | 3,750,000 | 3,750,000 | 741,818,150 | 528,690,146 | 557,340 | 309,200 | 6.11 | 3,750,000 | 475,000 | 7,500,000 | 2,880,000 | 1,707,000 | 5.16 | 6.15 | 7.21 | 2011 Data not Available | HDX | 2026-04-16 |
2,013 | DJI | 139,000 | 139,000 | 663,311,782 | 370,453,728 | 19,949 | 0 | 4.96 | 139,000 | 344,545 | 152,000 | 2,750,000 | 0 | 2.89 | 5.82 | 7.26 | 2013 Data not Available | HDX | 2026-04-16 |
2,013 | UGA | 0 | 0 | 0 | 0 | 206,985 | 30,136 | 5.5 | 0 | 0 | 0 | 0 | 0 | 3.63 | 6.06 | 7.58 | 2013 Data not Available | HDX | 2026-04-16 |
2,015 | ERI | 900,000 | 900,000 | 0 | 0 | 2,902 | 0 | 4.62 | 0 | 0 | 0 | 0 | 0 | 7.75 | 5.75 | 2.22 | Basic Social Services, national capacity development, food security and sustainable livelihoods, environment sustainability, gender equity and women advancement. | HDX | 2026-04-16 |
2,014 | SDN | 6,100,000 | 6,100,000 | 985,696,822 | 549,652,696 | 287,709 | 3,100,000 | 7.54 | 6,100,000 | 4,600,000 | 6,100,000 | 1,258,204 | 2,700,000 | 7.29 | 7.42 | 7.93 | 2014 Data not Available | HDX | 2026-04-16 |
2,014 | SSD | 11,600,000 | 11,600,000 | 1,801,753,424 | 1,435,985,168 | 248,152 | 1,504,768 | 7.7 | 4,300,000 | 3,600,000 | 5,800,000 | 0 | 993,300 | 6.96 | 7.47 | 8.77 | 2014 Data not Available | HDX | 2026-04-16 |
Humanitarian Data for Eastern Africa Region
Publisher: OCHA Regional Office for Southern and Eastern Africa (ROSEA) · Source: HDX · License: cc-by-igo · Updated: 2023-03-02
Abstract
This dataset includes the Refugee, IDPs, People in need by Sector, Humanitarian Funding, inform indexes and humanitarian priorites data for countries in the wider horn of Africa Region from 2011 to date
Each row in this dataset represents time-series observations. Data was last updated on HDX on 2023-03-02. Geographic scope: BDI, DJI, ERI, ETH, KEN, RWA, SOM, SSD, and 2 others.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Food security and nutrition |
| Unit of observation | Time-series observations |
| Rows (total) | 50 |
| Columns | 20 (16 numeric, 4 categorical, 0 datetime) |
| Train split | 40 rows |
| Test split | 10 rows |
| Geographic scope | BDI, DJI, ERI, ETH, KEN, RWA, SOM, SSD, and 2 others |
| Publisher | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| HDX last updated | 2023-03-02 |
Variables
Geographic — year (range 2011.0–2015.0), vulnerability (range 0.0–8.93), copingcapacity (range 0.0–9.57).
Identifier / Metadata — name (BRN, DJI, ERI), refugees (range 0.0–705000.0), idps (range 0.0–3100000.0), esa_source (HDX), esa_processed (2026-04-16).
Other — pin (range 0.0–11600000.0), tar (range 0.0–11600000.0), committed (range 0.0–1801753424.0), funded (range 0.0–1435985168.0), inform (range 0.0–9.01) and 7 others.
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-humanitarian-data-for-eastern-africa-region")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
year |
int64 | 0.0% | 2011.0 – 2015.0 (mean 2013.0) |
name |
object | 0.0% | BRN, DJI, ERI |
pin |
int64 | 0.0% | 0.0 – 11600000.0 (mean 2181846.4) |
tar |
int64 | 0.0% | 0.0 – 11600000.0 (mean 1924936.4) |
committed |
int64 | 0.0% | 0.0 – 1801753424.0 (mean 421595986.24) |
funded |
int64 | 0.0% | 0.0 – 1435985168.0 (mean 236919011.36) |
refugees |
int64 | 0.0% | 0.0 – 705000.0 (mean 176268.26) |
idps |
int64 | 0.0% | 0.0 – 3100000.0 (mean 494717.34) |
inform |
float64 | 0.0% | 0.0 – 9.01 (mean 5.3198) |
fs |
int64 | 0.0% | 0.0 – 7236500.0 (mean 1980213.94) |
nutrition |
int64 | 0.0% | 0.0 – 7997190.0 (mean 1187566.76) |
health |
int64 | 0.0% | 0.0 – 11544400.0 (mean 2024399.72) |
wash |
int64 | 0.0% | 0.0 – 6400000.0 (mean 826287.84) |
education |
int64 | 0.0% | 0.0 – 8926000.0 (mean 765402.54) |
hazards |
float64 | 0.0% | 0.0 – 9.55 (mean 4.815) |
vulnerability |
float64 | 0.0% | 0.0 – 8.93 (mean 5.5504) |
copingcapacity |
float64 | 0.0% | 0.0 – 9.57 (mean 6.3064) |
priorities |
object | 12.0% | Not Available, 2012 Data not Available, 2013 Data not Available |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-16 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
year |
2011.0 | 2015.0 | 2013.0 | 2013.0 |
pin |
0.0 | 11600000.0 | 2181846.4 | 676261.5 |
tar |
0.0 | 11600000.0 | 1924936.4 | 501761.5 |
committed |
0.0 | 1801753424.0 | 421595986.24 | 80655278.0 |
funded |
0.0 | 1435985168.0 | 236919011.36 | 22658652.0 |
refugees |
0.0 | 705000.0 | 176268.26 | 65668.5 |
idps |
0.0 | 3100000.0 | 494717.34 | 76093.5 |
inform |
0.0 | 9.01 | 5.3198 | 6.205 |
fs |
0.0 | 7236500.0 | 1980213.94 | 257000.0 |
nutrition |
0.0 | 7997190.0 | 1187566.76 | 271042.0 |
health |
0.0 | 11544400.0 | 2024399.72 | 15000.0 |
wash |
0.0 | 6400000.0 | 826287.84 | 0.0 |
education |
0.0 | 8926000.0 | 765402.54 | 0.0 |
hazards |
0.0 | 9.55 | 4.815 | 5.385 |
vulnerability |
0.0 | 8.93 | 5.5504 | 6.37 |
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. 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.
- This dataset spans 10 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_humanitarian_data_for_eastern_africa_region,
title = {Humanitarian Data for Eastern Africa Region},
author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
year = {2023},
url = {https://data.humdata.org/dataset/humanitarian-data-for-eastern-africa-region},
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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