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state
stringclasses
37 values
year
int64
2.02k
2.03k
onset_day_of_year
int64
90
149
cessation_day_of_year
int64
250
309
total_rainfall_mm
float64
301
2.5k
dry_spell_days
int64
1
19
Kano
2,024
101
266
896.9
7
Cross River
2,022
129
256
2,435.7
10
Osun
2,023
102
261
2,224.5
8
Ondo
2,023
123
283
2,132.1
7
Abia
2,025
119
301
2,361.5
7
Rivers
2,023
95
255
2,404.2
5
Borno
2,022
147
275
408
11
Osun
2,024
146
264
1,915.6
4
Gombe
2,025
98
291
1,376.3
5
Akwa Ibom
2,022
128
300
1,643.4
13
Delta
2,022
138
303
2,030.5
11
Zamfara
2,022
91
279
724.2
10
Abia
2,022
145
251
1,587.5
14
Kogi
2,024
143
301
1,079.3
13
Abia
2,022
142
257
1,918.4
9
Yobe
2,023
119
287
459.9
5
Katsina
2,024
134
253
766.6
12
Kano
2,023
114
299
883.7
12
Kogi
2,022
104
293
1,365.7
7
Kebbi
2,025
132
309
685.6
7
Lagos
2,024
101
262
1,587.6
8
Benue
2,023
148
303
1,244.5
9
Kano
2,022
119
302
675.3
13
Niger
2,022
136
258
1,193.3
8
Kano
2,022
134
271
953.5
10
Ogun
2,025
93
258
2,293.9
6
Zamfara
2,022
120
288
638.5
8
Sokoto
2,022
99
289
755.9
9
Adamawa
2,025
105
279
1,397.5
11
Abia
2,022
149
280
1,771.3
5
Adamawa
2,024
108
256
1,044.7
8
Kaduna
2,022
92
259
1,312
9
Katsina
2,022
125
261
906.5
11
Enugu
2,022
140
290
1,806.4
8
Lagos
2,025
140
289
2,427.2
9
Adamawa
2,025
116
298
1,141
5
Bauchi
2,024
148
306
1,237.4
11
Plateau
2,023
106
291
1,061.8
7
Abia
2,022
109
300
2,121.5
12
Rivers
2,023
104
297
2,179.5
6
Benue
2,025
146
281
1,302.3
7
Ebonyi
2,023
106
305
2,265.5
9
Kaduna
2,022
125
290
1,042.2
11
Plateau
2,022
140
304
1,028.5
8
Ondo
2,025
133
283
1,748.5
9
Borno
2,024
123
286
502.9
7
Benue
2,024
119
254
1,446
15
Adamawa
2,025
145
275
1,313.4
7
Oyo
2,023
119
262
1,812.6
5
Oyo
2,025
149
306
1,857.9
9
Edo
2,022
120
278
2,452.9
9
Jigawa
2,022
136
253
500.1
7
Jigawa
2,025
94
268
583.4
10
Anambra
2,022
90
250
2,361.2
4
Nasarawa
2,024
127
285
1,063.3
6
FCT
2,022
130
279
1,412.5
11
Kano
2,023
137
268
787.1
7
Borno
2,023
116
289
452
4
Cross River
2,024
97
263
1,619.1
10
Lagos
2,024
146
256
2,066.7
6
Imo
2,025
128
280
1,606.6
9
Ondo
2,022
137
275
1,548.6
3
Borno
2,024
99
272
442.2
5
Imo
2,023
111
290
2,184.6
6
Abia
2,025
100
258
1,936.7
7
Rivers
2,023
129
304
2,087.4
10
Anambra
2,024
149
257
1,758.1
7
Ogun
2,023
138
272
2,221.4
11
Katsina
2,022
96
266
805.6
6
Kebbi
2,023
97
281
860.5
6
Sokoto
2,023
120
269
985.6
9
Ondo
2,022
97
301
2,396.9
10
Lagos
2,022
138
256
2,396.8
7
Imo
2,025
148
292
1,509
11
Kano
2,023
94
274
689.1
4
Benue
2,024
133
266
1,117.6
7
Ekiti
2,023
119
267
1,563
9
Lagos
2,022
134
254
1,665.4
6
Enugu
2,023
131
256
2,190.8
9
Ondo
2,025
124
262
1,707.6
8
Gombe
2,023
99
279
1,244.5
7
Zamfara
2,023
103
276
736.4
6
Kogi
2,023
144
258
1,166.7
7
Kwara
2,023
107
266
1,373.8
7
Anambra
2,025
147
254
2,437.3
7
Jigawa
2,024
100
294
301
5
Kogi
2,023
107
268
1,215.1
5
Taraba
2,024
125
276
1,126.1
9
Imo
2,025
110
275
2,100.9
12
Bauchi
2,022
117
289
1,132.4
9
Taraba
2,022
138
307
1,257.6
8
Gombe
2,023
100
274
1,035.7
10
Ogun
2,025
142
264
2,053.3
5
FCT
2,024
111
284
1,133.8
9
Gombe
2,025
141
282
1,179.5
7
Rivers
2,023
119
305
2,364.5
12
Niger
2,024
147
267
1,002.9
4
Cross River
2,023
95
276
1,968.8
10
Benue
2,022
116
265
1,182.2
9
Kebbi
2,025
93
266
858.7
7
End of preview. Expand in Data Studio

Africa Synth Agriculture Seasonal Rainfall Patterns Nigeria | Africa (Electric Sheep Africa metadata inventory)

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

size sector downloads license

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: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Nigeria Agriculture – Seasonal Rainfall Patterns Dataset Description Annual rainfall onset, cessation, total, dry spells by state. Category: Weather & ClimateRows: 500Format: CSV, ParquetLicense: MITSynthetic: Yes (generated using reference data from FAO, NBS, NiMet, FMARD) Dataset Structure Schema… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-seasonal-rainfall-patterns-nigeria.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-synth-agriculture-seasonal-rainfall-patterns-nigeria
Sector climate_environment
Topic tags nigeria, agriculture, food-systems, synthetic, weather-and-climate
Modalities tabular, text
Formats parquet
Size category n<1K
Countries Nigeria
ISO3 coverage NGA
Last modified on HF 2026-04-14 22:23:51+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-synth-agriculture-seasonal-rainfall-patterns-nigeria")
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, language.
  • 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_synth_agriculture_seasonal_rainfall_patterns_nigeria_2026,
  title        = {Africa Synth Agriculture Seasonal Rainfall Patterns Nigeria | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-seasonal-rainfall-patterns-nigeria},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-seasonal-rainfall-patterns-nigeria}}
}

License

Released under mit.

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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