electricsheepafrica/africa-zimbabwe-current-situation-fewsnet-ipc-classification

Zimbabwe Current Situation FEWS NET Acute Food Insecurity Classifications Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-03

---

Abstract

Zimbabwe Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the projection_start, projection_end column(s). Geographic scope: ZWE.

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

---

Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	8,925
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	7,140 rows
Test split	1,785 rows
Geographic scope	ZWE
Publisher	FEWS NET
HDX last updated	2026-04-03

---

Variables

Geographic — country (Zimbabwe), country_code (ZW), fewsnet_region (Southern Africa), unit_type (fsc_admin_lhz, fsc_admin, national_park), specialization_type and 2 others.

Temporal — datacollectionperiod (range 158624.0–377224.0), reporting_date.

Outcome / Measurement — value (range 1.0–3.0).

Identifier / Metadata — source_organization (FEWS NET), source_document (Food Security Outlook, Zimbabwe), geographic_unit_full_name (Agrofisheries, Binga, Matabeleland North, Zimbabwe, Highveld Prime Cereal and Cash Crop Resettlement, Mount Darwin, Mashonaland Central, Zimbabwe, Urban, Chipinge Urban, Manicaland, Zimbabwe), geographic_unit_name (Urban, Highveld Prime Cereal and Cash Crop Resettlement, Highveld Prime Communal), fnid (ZW2013C3150102, ZW2013C3150408, ZW2013C3130401) and 8 others.

Other — geographic_group (Southern Africa), classification_scale, is_allowing_for_assistance, projection_start, projection_end and 12 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-zimbabwe-current-situation-fewsnet-ipc-classification")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
source_organization	object	0.0%	FEWS NET
source_document	object	0.0%	Food Security Outlook, Zimbabwe
country	object	0.0%	Zimbabwe
country_code	object	0.0%	ZW
geographic_group	object	0.0%	Southern Africa
fewsnet_region	object	0.0%	Southern Africa
geographic_unit_full_name	object	0.0%	Agrofisheries, Binga, Matabeleland North, Zimbabwe, Highveld Prime Cereal and Cash Crop Resettlement, Mount Darwin, Mashonaland Central, Zimbabwe, Urban, Chipinge Urban, Manicaland, Zimbabwe
geographic_unit_name	object	0.0%	Urban, Highveld Prime Cereal and Cash Crop Resettlement, Highveld Prime Communal
unit_type	object	0.0%	fsc_admin_lhz, fsc_admin, national_park
fnid	object	0.0%	ZW2013C3150102, ZW2013C3150408, ZW2013C3130401
classification_scale	object	0.0%
scenario_name	object	0.0%
preference_rating	int64	0.0%	90.0 – 90.0 (mean 90.0)
is_allowing_for_assistance	bool	0.0%
projection_start	datetime64[ns]	0.0%
projection_end	datetime64[ns]	0.0%
status	object	0.0%
value	float64	2.7%	1.0 – 3.0 (mean 1.804)
description	object	2.7%
id	int64	0.0%	24370140.0 – 41220997.0 (mean 27126732.8629)
datacollectionperiod	int64	0.0%	158624.0 – 377224.0 (mean 205164.4)
datacollection	int64	0.0%	168345.0 – 388159.0 (mean 216058.2229)
scenario	object	0.0%
geographic_unit	int64	0.0%	29495.0 – 193436.0 (mean 82038.0743)
datasourceorganization	int64	0.0%	1.0 – 1.0 (mean 1.0)
datasourcedocument	int64	0.0%	6601.0 – 6601.0 (mean 6601.0)
dataseries	int64	0.0%	6481300.0 – 6792669.0 (mean 6565296.5143)
dataseries_name	object	0.0%
specialization_type	object	0.0%
dataseries_specialization_type	object	0.0%
data_usage_policy	object	0.0%
created	datetime64[ns]	0.0%
modified	datetime64[ns]	0.0%
status_changed	datetime64[ns]	0.0%
collection_status	object	0.0%
collection_status_changed	datetime64[ns]	0.0%
collection_schedule	object	0.0%
reporting_date	datetime64[ns]	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

---

Numeric Summary

Column	Min	Max	Mean	Median
preference_rating	90.0	90.0	90.0	90.0
value	1.0	3.0	1.804	2.0
id	24370140.0	41220997.0	27126732.8629	24499017.0
datacollectionperiod	158624.0	377224.0	205164.4	159493.0
datacollection	168345.0	388159.0	216058.2229	168692.0
geographic_unit	29495.0	193436.0	82038.0743	29722.0
datasourceorganization	1.0	1.0	1.0	1.0
datasourcedocument	6601.0	6601.0	6601.0	6601.0
dataseries	6481300.0	6792669.0	6565296.5143	6488101.0

---

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. 3 column(s) with >80% missing values were removed: pct_phase3, pct_phase4, pct_phase5. 7 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 FEWS NET 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_zimbabwe_current_situation_fewsnet_ipc_classification,
  title     = {Zimbabwe Current Situation FEWS NET Acute Food Insecurity Classifications Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/zimbabwe_current_situation_fewsnet_ipc_classification},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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