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1 value
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2026-04-18 00:00:00
2026-04-18 00:00:00
KOULIKORO
Koulikoro
258,339
1
0.91
0.09
0
0
0
235,088.49
23,250.51
0
0
0
0
1
0.88
0.12
0
0
0
227,338.32
31,000.68
0
0
0
0
HDX
2026-04-18
null
Dire
134,559
1
0.91
0.07
0.02
0
0
122,448.69
9,419.13
2,691.18
0
0
2,691.18
1
0.84
0.12
0.04
0
0
113,029.56
16,147.08
5,382.36
0
0
5,382.36
HDX
2026-04-18
null
Djenné
255,577
1
0.9
0.1
0
0
0
230,019.3
25,557.7
0
0
0
0
1
0.83
0.15
0.02
0
0
212,128.91
38,336.55
5,111.54
0
0
5,111.54
HDX
2026-04-18
null
Kolondièba
247,104
1
0.84
0.15
0.01
0
0
207,567.36
37,065.6
2,471.04
0
0
2,471.04
1
0.81
0.17
0.02
0
0
200,154.24
42,007.68
4,942.08
0
0
4,942.08
HDX
2026-04-18
null
Barouéli
248,816
1
0.91
0.09
0
0
0
226,422.56
22,393.44
0
0
0
0
1
0.89
0.11
0
0
0
221,446.24
27,369.76
0
0
0
0
HDX
2026-04-18
null
Niafunké
215,275
2
0.67
0.26
0.06
0.01
0
144,234.25
55,971.5
12,916.5
2,152.75
0
15,069.25
2
0.58
0.3
0.1
0.02
0
124,859.5
64,582.5
21,527.5
4,305.5
0
25,833
HDX
2026-04-18
null
Kéniéba
241,665
1
0.84
0.16
0
0
0
202,998.6
38,666.4
0
0
0
0
1
0.81
0.17
0.02
0
0
195,748.65
41,083.05
4,833.3
0
0
4,833.3
HDX
2026-04-18
null
Yanfolila
260,909
1
0.87
0.13
0
0
0
226,990.83
33,918.17
0
0
0
0
1
0.81
0.17
0.02
0
0
211,336.29
44,354.53
5,218.18
0
0
5,218.18
HDX
2026-04-18
null
Tominian
271,216
1
0.82
0.15
0.03
0
0
222,397.12
40,682.4
8,136.48
0
0
8,136.48
1
0.8
0.16
0.03
0
0
216,972.8
43,394.56
8,136.48
0
0
8,136.48
HDX
2026-04-18
null
Tin-Essako
9,829
1
0.82
0.12
0.06
0
0
8,059.78
1,179.48
589.74
0
0
589.74
2
0.8
0.14
0.06
0
0
7,863.2
1,376.06
589.74
0
0
589.74
HDX
2026-04-18
3.       Répéter les mêmes étapes pour la situation projetée
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null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
HDX
2026-04-18
TOMBOUCTOU
Tombouctou
156,237
2
0.74
0.2
0.06
0
0
115,615.38
31,247.4
9,374.22
0
0
9,374.22
2
0.66
0.25
0.09
0
0
103,116.42
39,059.25
14,061.33
0
0
14,061.33
HDX
2026-04-18
null
Niono
447,517
1
0.85
0.15
0
0
0
380,389.45
67,127.55
0
0
0
0
1
0.89
0.11
0
0
0
398,290.13
49,226.87
0
0
0
0
HDX
2026-04-18
SIAKASSO
Sikasso
901,497
1
0.89
0.11
0
0
0
802,332.33
99,164.67
0
0
0
0
1
0.86
0.13
0.01
0
0
775,287.42
117,194.61
9,014.97
0
0
9,014.97
HDX
2026-04-18
MOPTI
Mopti
452,388
1
0.94
0.05
0.01
0
0
425,244.72
22,619.4
4,523.88
0
0
4,523.88
1
0.92
0.06
0.02
0
0
416,196.96
27,143.28
9,047.76
0
0
9,047.76
HDX
2026-04-18
TOTAL GENERAL
null
15,599,002
null
null
null
null
null
null
13,587,342.08
1,892,885.7
114,764.6
4,009.62
0
118,774.22
null
null
null
null
null
null
13,045,914.2
2,235,422.41
305,077.12
9,876.11
0
314,953.23
HDX
2026-04-18
null
Yelimané
216,483
1
0.92
0.08
0
0
0
199,164.36
17,318.64
0
0
0
0
1
0.89
0.1
0.01
0
0
192,669.87
21,648.3
2,164.83
0
0
2,164.83
HDX
2026-04-18
null
G.Rharous
136,242
2
0.65
0.28
0.07
0
0
88,557.3
38,147.76
9,536.94
0
0
9,536.94
2
0.57
0.33
0.1
0
0
77,657.94
44,959.86
13,624.2
0
0
13,624.2
HDX
2026-04-18
null
Kangaba
123,150
1
0.93
0.07
0
0
0
114,529.5
8,620.5
0
0
0
0
1
0.9
0.1
0
0
0
110,835
12,315
0
0
0
0
HDX
2026-04-18
null
Bla
347,933
1
0.89
0.11
0
0
0
309,660.37
38,272.63
0
0
0
0
1
0.86
0.14
0
0
0
299,222.38
48,710.62
0
0
0
0
HDX
2026-04-18
KAYES
Kayes
629,362
1
0.87
0.13
0
0
0
547,544.94
81,817.06
0
0
0
0
1
0.84
0.15
0.01
0
0
528,664.08
94,404.3
6,293.62
0
0
6,293.62
HDX
2026-04-18
null
Ménaka
66,777
1
0.88
0.1
0.02
0
0
58,763.76
6,677.7
1,335.54
0
0
1,335.54
1
0.81
0.14
0.05
0
0
54,089.37
9,348.78
3,338.85
0
0
3,338.85
HDX
2026-04-18
2.       Pour la SITUATION COURANTE
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null
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null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
HDX
2026-04-18
null
Kita
530,463
1
0.84
0.15
0.01
0
0
445,588.92
79,569.45
5,304.63
0
0
5,304.63
1
0.82
0.16
0.02
0
0
434,979.66
84,874.08
10,609.26
0
0
10,609.26
HDX
2026-04-18
SEGOU
Ségou
853,789
1
0.87
0.13
0
0
0
742,796.43
110,992.57
0
0
0
0
1
0.85
0.15
0
0
0
725,720.65
128,068.35
0
0
0
0
HDX
2026-04-18
4.       Dans la dernière ligne « Total », inscrire la population totale du pays, la population totale en Phase3, 4 et 5 pour la situation courante pour le pays et la population totale en Phase3, 4 et 5 pour la situation projetée pour le pays. Ne rien inscrire dans les autres
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null
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null
null
null
null
null
null
null
null
null
null
null
null
null
HDX
2026-04-18
null
Goundam
185,687
2
0.74
0.2
0.05
0.01
0
137,408.38
37,137.4
9,284.35
1,856.87
0
11,141.22
2
0.62
0.25
0.1
0.03
0
115,125.94
46,421.75
18,568.7
5,570.61
0
24,139.31
HDX
2026-04-18
c.        Calculer pour finir, en utilisant les pourcentages estimés de ménages en Phase 3, 4 et 5 l’estimation de population totale en insécurité alimentaire pour chaque entité administrative de 3ème niveau
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null
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null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
HDX
2026-04-18
null
Yorosso
259,546
1
0.88
0.12
0
0
0
228,400.48
31,145.52
0
0
0
0
1
0.85
0.13
0.02
0
0
220,614.1
33,740.98
5,190.92
0
0
5,190.92
HDX
2026-04-18
KIDAL
Kidal
41,006
2
0.68
0.25
0.07
0
0
27,884.08
10,251.5
2,870.42
0
0
2,870.42
2
0.66
0.27
0.07
0
0
27,063.96
11,071.62
2,870.42
0
0
2,870.42
HDX
2026-04-18
null
Youwarou
133,082
1
0.82
0.16
0.02
0
0
109,127.24
21,293.12
2,661.64
0
0
2,661.64
2
0.73
0.25
0.02
0
0
97,149.86
33,270.5
2,661.64
0
0
2,661.64
HDX
2026-04-18
TOTAL REGION
null
83,001
null
null
null
null
null
null
60,692.42
16,666.14
5,642.44
0
0
5,642.44
null
null
null
null
null
null
59,839.44
17,600.98
5,560.58
0
0
5,560.58
HDX
2026-04-18
null
Diéma
259,720
1
0.86
0.14
0
0
0
223,359.2
36,360.8
0
0
0
0
1
0.82
0.16
0.02
0
0
212,970.4
41,555.2
5,194.4
0
0
5,194.4
HDX
2026-04-18
null
Kolokani
285,189
1
0.83
0.15
0.02
0
0
236,706.87
42,778.35
5,703.78
0
0
5,703.78
1
0.81
0.16
0.03
0
0
231,003.09
45,630.24
8,555.67
0
0
8,555.67
HDX
2026-04-18
null
Macina
289,550
1
0.89
0.11
0
0
0
257,699.5
31,850.5
0
0
0
0
1
0.86
0.14
0
0
0
249,013
40,537
0
0
0
0
HDX
2026-04-18
null
Kati
1,173,570
1
0.89
0.11
0
0
0
1,044,477.3
129,092.7
0
0
0
0
1
0.83
0.15
0.02
0
0
974,063.1
176,035.5
23,471.4
0
0
23,471.4
HDX
2026-04-18
null
Bankass
324,694
1
0.97
0.03
0
0
0
314,953.18
9,740.82
0
0
0
0
1
0.93
0.06
0.01
0
0
301,965.42
19,481.64
3,246.94
0
0
3,246.94
HDX
2026-04-18
null
Dioïla
599,739
1
0.9
0.1
0
0
0
539,765.1
59,973.9
0
0
0
0
1
0.87
0.12
0.01
0
0
521,772.93
71,968.68
5,997.39
0
0
5,997.39
HDX
2026-04-18
GAO
Gao
293,730
1
0.86
0.14
0
0
0
252,607.8
41,122.2
0
0
0
0
1
0.85
0.11
0.04
0
0
249,670.5
32,310.3
11,749.2
0
0
11,749.2
HDX
2026-04-18
TOTAL REGION
null
665,000
null
null
null
null
null
null
576,471.68
82,529.78
5,998.54
0
0
5,998.54
null
null
null
null
null
null
550,399.2
82,478.82
32,121.98
0
0
32,121.98
HDX
2026-04-18
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
HDX
2026-04-18
null
Kadiolo
298,557
1
0.85
0.15
0
0
0
253,773.45
44,783.55
0
0
0
0
1
0.82
0.17
0.01
0
0
244,816.74
50,754.69
2,985.57
0
0
2,985.57
HDX
2026-04-18
null
Tessalit
19,550
1
0.84
0.1
0.06
0
0
16,422
1,955
1,173
0
0
1,173
1
0.9
0.07
0.03
0
0
17,595
1,368.5
586.5
0
0
586.5
HDX
2026-04-18
null
Bourem
142,686
1
0.86
0.13
0.01
0
0
122,709.96
18,549.18
1,426.86
0
0
1,426.86
1
0.81
0.15
0.04
0
0
115,575.66
21,402.9
5,707.44
0
0
5,707.44
HDX
2026-04-18
TOTAL REGION
null
3,242,001
null
null
null
null
null
null
2,847,645.46
377,645.36
16,710.18
0
0
16,710.18
null
null
null
null
null
null
2,735,438.59
452,227.67
54,334.74
0
0
54,334.74
HDX
2026-04-18
TOTAL REGION
null
2,971,001
null
null
null
null
null
null
2,632,394.98
329,935
8,671.02
0
0
8,671.02
null
null
null
null
null
null
2,516,219.88
408,479.28
46,301.84
0
0
46,301.84
HDX
2026-04-18
null
San
409,178
1
0.9
0.1
0
0
0
368,260.2
40,917.8
0
0
0
0
1
0.86
0.14
0
0
0
351,893.08
57,284.92
0
0
0
0
HDX
2026-04-18
null
Abeïbara
12,616
2
0.66
0.26
0.08
0
0
8,326.56
3,280.16
1,009.28
0
0
1,009.28
2
0.58
0.3
0.12
0
0
7,317.28
3,784.8
1,513.92
0
0
1,513.92
HDX
2026-04-18
Estimation des populations en insécurité alimentaire aigüe par zone d’analyse
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null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
HDX
2026-04-18
null
Ténenkou
199,793
1
0.84
0.15
0.01
0
0
167,826.12
29,968.95
1,997.93
0
0
1,997.93
2
0.78
0.18
0.04
0
0
155,838.54
35,962.74
7,991.72
0
0
7,991.72
HDX
2026-04-18
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
HDX
2026-04-18
1.       Reporter dans « Population Totale » les chiffres les plus récents de population pour l’entité administrative de 3ème niveau. Ces chiffres doivent se trouver dans le Tableau 1. Faire cela pour toutes les zones d’analyse.
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null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
HDX
2026-04-18
null
Banamba
234,290
1
0.92
0.08
0
0
0
215,546.8
18,743.2
0
0
0
0
1
0.9
0.09
0.01
0
0
210,861
21,086.1
2,342.9
0
0
2,342.9
HDX
2026-04-18
TOTAL REGION
null
2,444,999
null
null
null
null
null
null
2,115,193.52
324,500.85
5,304.63
0
0
5,304.63
null
null
null
null
null
null
2,038,762.12
371,468.41
34,768.47
0
0
34,768.47
HDX
2026-04-18
TOTAL REGION
null
828,000
null
null
null
null
null
null
608,264
171,923.19
43,803.19
4,009.62
0
47,812.81
null
null
null
null
null
null
533,789.36
211,170.44
73,164.09
9,876.11
0
83,040.2
HDX
2026-04-18
null
Bafoulabé
286,548
1
0.9
0.1
0
0
0
257,893.2
28,654.8
0
0
0
0
1
0.84
0.15
0.01
0
0
240,700.32
42,982.2
2,865.48
0
0
2,865.48
HDX
2026-04-18
null
Koro
444,640
1
0.89
0.1
0.01
0
0
395,729.6
44,464
4,446.4
0
0
4,446.4
1
0.86
0.12
0.02
0
0
382,390.4
53,356.8
8,892.8
0
0
8,892.8
HDX
2026-04-18

Mali: 2015/2016 Food Security IPC analysis | Africa (original)

Size category: n<1K - Formats: parquet - Sector: humanitarian_development - Engineered by Electric Sheep Africa

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TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: Mali: 2015/2016 Food Security IPC analysis Publisher: OCHA Mali · Source: HDX · License: cc-by-igo · Updated: 2023-03-03 Abstract The data represents the IPC (Integrated Food Security Phase Classification) analysis as of November 2015 for Mali and its related projection for June - August 2016. Each row in this dataset represents tabular records. Data was last updated on HDX on 2023-03-03. Geographic scope: MLI. Curated into ML-ready Parquet format by Electric Sheep… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mali-2015-2016-food-security-ipc-analysis.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-mali-2015-2016-food-security-ipc-analysis
Sector humanitarian_development
Topic tags humanitarian, hdx, electric-sheep-africa, food-security, integrated-food-security-phase-classification-ipc, nutrition, mli
Modalities tabular, text
Formats parquet
Size category n<1K
Countries Mali
ISO3 coverage MLI
Last modified on HF 2026-04-20 08:36:52+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mali-2015-2016-food-security-ipc-analysis")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_mali_2015_2016_food_security_ipc_analysis_2026,
  title        = {Mali: 2015/2016 Food Security IPC analysis | Africa (original)},
  author       = {original},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-mali-2015-2016-food-security-ipc-analysis},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mali-2015-2016-food-security-ipc-analysis}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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