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Datasets:
electricsheepafrica
/
africa-nigeria-foreign-trade-in-goods-statistics-78a5781e

Tasks:
Tabular Regression
Time Series Forecasting
Modalities:
Tabular
Text
Document
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
tabular
africa
open-data
official-statistics
nigeria
national-bureau-of-statistics-nigeria
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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Standardize Electric Sheep Africa dataset card

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  1. README.md +326 -256
README.md CHANGED
@@ -3,271 +3,299 @@ license: other
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  language:
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  - en
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  task_categories:
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- - tabular-classification
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  - tabular-regression
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- multilinguality: monolingual
 
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  size_categories:
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  - 1K<n<10K
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  tags:
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- - tabular
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- - xlsx
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- - africa
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- - nigeria
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- - official-statistics
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- - open-data
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- pretty_name: "Foreign Trade in Goods Statistics | Africa (Nigeria official open data)"
 
 
 
 
 
 
 
 
 
 
 
 
 
19
  ---
20
 
21
- # Foreign Trade in Goods Statistics | Africa (Nigeria official open data)
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- 1,366 rows - 1 Africa country - 2020-2024 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
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  ![rows](https://img.shields.io/badge/rows-1366-blue)
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  ![countries](https://img.shields.io/badge/countries-1-green)
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- ![years](https://img.shields.io/badge/years-2020-2024-orange)
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  ![indicators](https://img.shields.io/badge/indicators-0-purple)
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  ![license](https://img.shields.io/badge/license-other-lightgrey)
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31
  ## TL;DR
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- This dataset packages one official `XLSX` resource from **Nigeria** as
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- ML-ready Parquet. The source file is the provenance boundary; all usable
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- indicators or tabular columns from the resource stay together in this repo.
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37
- ## About the source
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39
- - **Source:** [Foreign Trade in Goods Statistics](https://microdata.nigerianstat.gov.ng/index.php/catalog/84/related-materials)
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- - **Publisher:** National Bureau of Statistics, Nigeria
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- - **Resource:** [Foreign Trade Statistics Report Q3 2024 Tables](https://microdata.nigerianstat.gov.ng/index.php/catalog/84/download/1063)
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- - **Format:** `XLSX`
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- - **License:** [Other open license]()
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- - **Packaging mode:** `tabular_resource`
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
45
 
46
- ## Geographic coverage
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- 1 Africa country:
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- | Country | Rows | First year | Last year | Name |
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- |---------|-----:|-----------:|----------:|------|
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  | `NGA` | 1,366 | 2020 | 2024 | `Nigeria` |
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54
- ## Indicators or Resource Contents
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56
- - This source file is packaged as a normalized tabular resource.
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58
  ## Schema
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60
  | Column | Type | Description | Example |
61
  |--------|------|-------------|---------|
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- | `source_record_id` | `string` | Stable row identifier for tabular resources. | `nbs-nada-84-1063:product-ranking:0` |
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- | `country_iso3` | `string` | ISO3 country code. | `NGA` |
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- | `country_name` | `string` | Country name. | `Nigeria` |
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- | `source_sheet` | `string` | Workbook sheet name, when the source is a spreadsheet. | `PRODUCT RANKING` |
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- | `year` | `Float64` | Observation year. | `2024.0` |
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- | `d_2710125000` | `string` | Source column. | `2710192100` |
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- | `motor_spirit_ordinary` | `string` | Source column. | `Gas oil` |
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- | `d_3320052110168_958` | `float64` | Source column. | `1331479375775.495` |
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- | `d_22_62533407342313` | `float64` | Source column. | `9.073702667654931` |
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- | `d_2709000000` | `float64` | Source column. | `2711110000.0` |
72
- | `petroleum_oils_and_oils_obtained_from_bituminous_mineral` | `string` | Source column. | `Natural gas` |
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- | `d_13406367224105_592` | `float64` | Source column. | `2108158471640.0` |
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- | `d_65_44035947521692` | `float64` | Source column. | `10.29053179796434` |
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- | `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2024` |
76
- | `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2024` |
77
- | `source_period_label` | `string` | Human-readable period inferred from source resource metadata. | `2024` |
78
  | `source_provider` | `string` | Publishing organization. | `National Bureau of Statistics, Nigeria` |
79
- | `source_dataset` | `string` | Source package title. | `Foreign Trade in Goods Statistics` |
80
- | `source_resource` | `string` | Source resource title. | `Foreign Trade Statistics Report Q3 2024 Tables` |
81
- | `source_package_id` | `string` | CKAN package UUID. | `NGA-NBS-FTS` |
82
- | `source_resource_id` | `string` | CKAN resource UUID. | `nbs-nada-84-1063` |
83
- | `source_url` | `string` | Original source resource URL. | `https://microdata.nigerianstat.gov.ng/index.php/catalog/84/download/1063` |
84
  | `license_id` | `string` | Source license identifier. | `other-open` |
85
- | `retrieved_at` | `string` | UTC retrieval timestamp. | `2026-07-19T04:13:01Z` |
86
- | `ranking` | `string` | Source column. | `` |
87
- | `code` | `string` | Source column. | `` |
88
- | `country_of_destination` | `string` | Source column. | `` |
89
- | `value` | `float64` | Numeric observation value. | `` |
90
- | `crude_oil` | `float64` | Source column. | `` |
91
- | `non_crude_oil_value` | `float64` | Source column. | `` |
92
- | `share_of_world_export` | `float64` | Source column. | `` |
93
- | `ranking_2` | `string` | Source column. | `` |
94
- | `code_2` | `string` | Source column. | `` |
95
- | `country_of_origin` | `string` | Source column. | `` |
96
- | `value_2` | `float64` | Source column. | `` |
97
- | `share_of_world_import` | `float64` | Source column. | `` |
98
- | `product_code` | `float64` | Source column. | `` |
99
- | `description` | `string` | Source column. | `` |
100
- | `july_value` | `float64` | Source column. | `` |
101
- | `august_value` | `float64` | Source column. | `` |
102
- | `september_value` | `float64` | Source column. | `` |
103
- | `quarter_3_value` | `float64` | Source column. | `` |
104
- | `2020` | `float64` | Source column. | `` |
105
- | `jan_dec` | `string` | Source column. | `` |
106
- | `d_12700943_807826` | `float64` | Source column. | `` |
107
- | `d_12522684_44384747` | `float64` | Source column. | `` |
108
- | `d_178259_36397852935` | `float64` | Source column. | `` |
109
- | `d_25223628_251673467` | `float64` | Source column. | `` |
110
- | `d_9444655_98263812` | `float64` | Source column. | `` |
111
- | `d_3078028_4612093493` | `float64` | Source column. | `` |
112
- | `d_1433402_5234599148` | `float64` | Source column. | `` |
113
- | `d_49_64664210437945` | `float64` | Source column. | `` |
114
- | `d_75_42037831415917` | `float64` | Source column. | `` |
115
- | `d_11_446447683700606` | `float64` | Source column. | `` |
116
- | `d_25_111807952349185` | `float64` | Source column. | `` |
117
- | `d_34_75129384107341` | `float64` | Source column. | `` |
118
- | `d_1` | `float64` | Source column. | `` |
119
- | `live_animals_animal_products` | `string` | Source column. | `` |
120
- | `d_454519_497526` | `float64` | Source column. | `` |
121
- | `d_551225_96482` | `float64` | Source column. | `` |
122
- | `d_549643_085907` | `float64` | Source column. | `` |
123
- | `d_597473_381483` | `float64` | Source column. | `` |
124
- | `d_132143_067024` | `float64` | Source column. | `` |
125
- | `d_183622_158499` | `float64` | Source column. | `` |
126
- | `d_154675_055025` | `float64` | Source column. | `` |
127
- | `d_189004_962979` | `float64` | Source column. | `` |
128
- | `d_414602_479166` | `float64` | Source column. | `` |
129
- | `d_17` | `float64` | Source column. | `` |
130
- | `vehicles_aircraft_and_parts_thereof_vessels_etc` | `string` | Source column. | `` |
131
- | `d_841996_8506065101` | `float64` | Source column. | `` |
132
- | `d_654167_001405` | `float64` | Source column. | `` |
133
- | `d_364783_14640246803` | `float64` | Source column. | `` |
134
- | `d_118508_51106255999` | `float64` | Source column. | `` |
135
- | `d_103280_23637861` | `float64` | Source column. | `` |
136
- | `d_185037_908764` | `float64` | Source column. | `` |
137
- | `d_30262_433039167998` | `float64` | Source column. | `` |
138
- | `d_17943_590918759997` | `float64` | Source column. | `` |
139
- | `d_667108_937973` | `float64` | Source column. | `` |
140
- | `d_20486389_94592132` | `float64` | Source column. | `` |
141
- | `d_406882_65486` | `float64` | Source column. | `` |
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- | `d_75018_470833` | `float64` | Source column. | `` |
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- | `d_331864_184027` | `float64` | Source column. | `` |
144
- | `d_1571578_663792` | `float64` | Source column. | `` |
145
- | `d_1026145_825876` | `float64` | Source column. | `` |
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- | `d_135365_023891` | `float64` | Source column. | `` |
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- | `d_283307_47592` | `float64` | Source column. | `` |
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- | `d_126760_33810499986` | `float64` | Source column. | `` |
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- | `d_4659558_709137` | `float64` | Source column. | `` |
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- | `d_395300_57961` | `float64` | Source column. | `` |
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- | `d_282149_181712` | `float64` | Source column. | `` |
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- | `d_1163118_008113` | `float64` | Source column. | `` |
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- | `d_272050_672054` | `float64` | Source column. | `` |
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- | `d_269948_729163` | `float64` | Source column. | `` |
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- | `d_194120_93440600001` | `float64` | Source column. | `` |
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- | `d_2082870_6040789997` | `float64` | Source column. | `` |
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- | `d_5968827_229148` | `float64` | Source column. | `` |
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- | `d_140594_462842` | `float64` | Source column. | `` |
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- | `d_1104562_3755939999` | `float64` | Source column. | `` |
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- | `d_3227000_370761` | `float64` | Source column. | `` |
161
- | `d_1496670_0199509999` | `float64` | Source column. | `` |
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- | `d_94096_550889` | `float64` | Source column. | `` |
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- | `d_2374887_865016526` | `float64` | Source column. | `` |
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- | `d_841332_5030647159` | `float64` | Source column. | `` |
165
- | `d_1533555_3619518103` | `float64` | Source column. | `` |
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- | `d_850175_86742181` | `float64` | Source column. | `` |
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- | `d_382197_7374368` | `float64` | Source column. | `` |
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- | `d_287047_38127052004` | `float64` | Source column. | `` |
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- | `d_53873_22074622` | `float64` | Source column. | `` |
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- | `d_127057_52796827012` | `float64` | Source column. | `` |
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- | `d_4868971_671319975` | `float64` | Source column. | `` |
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- | `d_116892_18503157001` | `float64` | Source column. | `` |
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- | `d_310162_230131095` | `float64` | Source column. | `` |
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- | `d_1072294_56642487` | `float64` | Source column. | `` |
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- | `d_440198_56906246004` | `float64` | Source column. | `` |
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- | `d_565612_78356893` | `float64` | Source column. | `` |
177
- | `d_1361963_97495592` | `float64` | Source column. | `` |
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- | `d_1001847_3621451296` | `float64` | Source column. | `` |
179
- | `d_4307663_291501557` | `float64` | Source column. | `` |
180
- | `d_100540_78569555` | `float64` | Source column. | `` |
181
- | `d_1880450_3436235301` | `float64` | Source column. | `` |
182
- | `d_633481_581818185` | `float64` | Source column. | `` |
183
- | `d_1693190_5803642925` | `float64` | Source column. | `` |
184
- | `d_120985_748587608` | `float64` | Source column. | `` |
185
- | `d_12522684_443847476` | `float64` | Source column. | `` |
186
- | `northern_africa` | `string` | Source column. | `` |
187
- | `algeria` | `string` | Source column. | `` |
188
- | `d_572_75597417` | `float64` | Source column. | `` |
189
- | `d_641_0005017130001` | `float64` | Source column. | `` |
190
- | `d_1418_357439287` | `float64` | Source column. | `` |
191
- | `d_1813_447706867` | `float64` | Source column. | `` |
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- | `d_3622_83277961` | `float64` | Source column. | `` |
193
- | `d_531_418697751` | `float64` | Source column. | `` |
194
- | `northern_africa_2` | `string` | Source column. | `` |
195
- | `algeria_2` | `string` | Source column. | `` |
196
- | `d_226_09702` | `float64` | Source column. | `` |
197
- | `d_3089_55777` | `float64` | Source column. | `` |
198
- | `d_19230_986895` | `float64` | Source column. | `` |
199
- | `d_495_810859` | `float64` | Source column. | `` |
200
- | `d_2864_970058` | `float64` | Source column. | `` |
201
- | `d_1356_942783` | `float64` | Source column. | `` |
202
- | `hs_code` | `float64` | Source column. | `` |
203
- | `products_description` | `string` | Source column. | `` |
204
- | `value_million` | `float64` | Source column. | `` |
205
- | `hs_code_2` | `float64` | Source column. | `` |
206
- | `products_description_2` | `string` | Source column. | `` |
207
- | `value_million_2` | `float64` | Source column. | `` |
208
- | `d_1685181_229068` | `float64` | Source column. | `` |
209
- | `d_56148_411868` | `float64` | Source column. | `` |
210
- | `d_161277_661645` | `float64` | Source column. | `` |
211
- | `d_2895774_94723` | `float64` | Source column. | `` |
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- | `d_120216_559547` | `float64` | Source column. | `` |
213
- | `d_2013771_731769` | `float64` | Source column. | `` |
214
- | `d_1121645_66003` | `float64` | Source column. | `` |
215
- | `d_4157705_570431` | `float64` | Source column. | `` |
216
- | `d_489221_457018` | `float64` | Source column. | `` |
217
- | `d_0_57922` | `float64` | Source column. | `` |
218
- | `d_1_food_and_beverage` | `string` | Source column. | `` |
219
- | `d_1804392_195325` | `float64` | Source column. | `` |
220
- | `d_2924630_378086` | `float64` | Source column. | `` |
221
- | `d_2864125_2981439997` | `float64` | Source column. | `` |
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- | `d_3825114_26213` | `float64` | Source column. | `` |
223
- | `d_498089_10916` | `float64` | Source column. | `` |
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- | `d_776581_027595` | `float64` | Source column. | `` |
225
- | `d_765153_621472` | `float64` | Source column. | `` |
226
- | `d_1134792_1321780002` | `float64` | Source column. | `` |
227
- | `d_1591992_326445` | `float64` | Source column. | `` |
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- | `period` | `string` | Source column. | `` |
229
- | `imports` | `float64` | Source column. | `` |
230
- | `exports` | `float64` | Source column. | `` |
231
- | `domestic_export` | `float64` | Source column. | `` |
232
- | `re_exports` | `float64` | Source column. | `` |
233
- | `trade_balance` | `float64` | Source column. | `` |
234
- | `2024` | `float64` | Source column. | `` |
235
- | `jan_sep` | `string` | Source column. | `` |
236
- | `d_57199371_75096349` | `float64` | Source column. | `` |
237
- | `d_2892775_604916276` | `float64` | Source column. | `` |
238
- | `d_1158258_362560994` | `float64` | Source column. | `` |
239
- | `d_199606_97612359398` | `float64` | Source column. | `` |
240
- | `d_183976_72611676998` | `float64` | Source column. | `` |
241
- | `d_1790328_9405883688` | `float64` | Source column. | `` |
242
- | `d_41502394_73686` | `float64` | Source column. | `` |
243
- | `d_9472030_403797518` | `float64` | Source column. | `` |
244
- | `d_5_057355555426269` | `float64` | Source column. | `` |
245
- | `d_2_024949448053132` | `float64` | Source column. | `` |
246
- | `d_0_34896707780751407` | `float64` | Source column. | `` |
247
- | `d_0_3216411657767395` | `float64` | Source column. | `` |
248
- | `d_3_129980078073518` | `float64` | Source column. | `` |
249
- | `d_72_55743108080715` | `float64` | Source column. | `` |
250
- | `d_16_559675594055747` | `float64` | Source column. | `` |
251
- | `sectors` | `string` | Source column. | `` |
252
- | `july` | `float64` | Source column. | `` |
253
- | `august` | `float64` | Source column. | `` |
254
- | `september` | `float64` | Source column. | `` |
255
- | `q3_2024` | `float64` | Source column. | `` |
256
- | `share_of_total_exports` | `float64` | Source column. | `` |
257
- | `hs10` | `string` | Source column. | `` |
258
- | `value_n_million` | `float64` | Source column. | `` |
259
- | `sector` | `string` | Source column. | `` |
260
- | `region` | `string` | Source column. | `` |
261
- | `regions` | `string` | Source column. | `` |
262
- | `product` | `float64` | Source column. | `` |
263
- | `product_description` | `string` | Source column. | `` |
264
- | `country_description` | `string` | Source column. | `` |
265
- | `value_n` | `float64` | Source column. | `` |
266
- | `mode_of_transport` | `string` | Source column. | `` |
267
- | `share_of_domestic_exports` | `string` | Source column. | `` |
268
- | `rank` | `float64` | Source column. | `` |
269
- | `ports` | `string` | Source column. | `` |
270
- | `share_of_total_export` | `float64` | Source column. | `` |
271
 
272
  ## Usage
273
 
@@ -279,51 +307,93 @@ df = ds["train"].to_pandas()
279
  print(df.head())
280
  ```
281
 
282
- ### Filter to one country
283
 
284
  ```python
285
- sample_country = df[df["country_iso3"] == "NGA"]
 
286
  ```
287
 
288
- ### Work with indicators
289
 
290
  ```python
291
- if "indicator_id" in df.columns:
292
- print(df["indicator_id"].value_counts().head())
293
- sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])
294
  ```
295
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
296
  ## Citation
297
 
298
  ```bibtex
299
  @misc{electric_sheep_africa_africa_nigeria_foreign_trade_in_goods_statistics_78a5781e_2024,
300
- title = {Foreign Trade in Goods Statistics | Africa (Nigeria official open data)},
301
  author = {National Bureau of Statistics, Nigeria},
302
  year = {2024},
303
  url = {https://microdata.nigerianstat.gov.ng/index.php/catalog/84/related-materials},
304
- publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
305
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-foreign-trade-in-goods-statistics-78a5781e}}
306
  }
307
  ```
308
 
309
  ## License
310
 
311
- Released under [Other open license]().
312
-
313
- Original data (c) National Bureau of Statistics, Nigeria. When using this dataset, please cite both the
314
- original source above and the Electric Sheep Africa repackaging.
315
 
316
- ## About Electric Sheep
 
 
317
 
318
- Electric Sheep Africa is part of the Electric Sheep mission: a unified,
319
- ML-ready data layer for Africa on Hugging Face. We pull data from authoritative
320
- open sources, normalize the schemas, package as Parquet, and publish with
321
- consistent dataset cards so researchers and developers can use `load_dataset()`
322
- to start working in seconds.
323
 
324
- Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica)
325
 
326
  ---
327
 
328
- Provenance: ingested 2026-07-19 via the Electric Sheep pipeline. Source URL:
329
- https://microdata.nigerianstat.gov.ng/index.php/catalog/84/download/1063
 
3
  language:
4
  - en
5
  task_categories:
 
6
  - tabular-regression
7
+ - time-series-forecasting
8
+ multilinguality: multilingual
9
  size_categories:
10
  - 1K<n<10K
11
  tags:
12
+ - "tabular"
13
+ - "africa"
14
+ - "open-data"
15
+ - "official-statistics"
16
+ - "nigeria"
17
+ - "national-bureau-of-statistics-nigeria"
18
+ - "economics"
19
+ - "national-economy"
20
+ - "foreign-trade-statistics-report-q3-2024-tables"
21
+ - "q3-2024-foreign-trade-statistics-tables-xlsx"
22
+ - "0"
23
+ - "document"
24
+ - "table-tbl"
25
+ - "other-materials"
26
+ configs:
27
+ - config_name: default
28
+ data_files:
29
+ - split: train
30
+ path: data/train-00000-of-00001.parquet
31
+ pretty_name: "Foreign Trade in Goods Statistics | Africa (National Bureau of Statistics, Nigeria)"
32
  ---
33
 
34
+ # Foreign Trade in Goods Statistics | Africa (National Bureau of Statistics, Nigeria)
35
 
36
+ **1,366 rows** - **1 Africa country/area** - **2020-2024** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
37
 
38
  ![rows](https://img.shields.io/badge/rows-1366-blue)
39
  ![countries](https://img.shields.io/badge/countries-1-green)
40
+ ![period](https://img.shields.io/badge/period-2020--2024-orange)
41
  ![indicators](https://img.shields.io/badge/indicators-0-purple)
42
  ![license](https://img.shields.io/badge/license-other-lightgrey)
43
 
44
  ## TL;DR
45
 
46
+ This dataset contains **1,366 rows** from **National Bureau of Statistics, Nigeria**, covering **Foreign Trade in Goods Statistics**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.
 
 
47
 
48
+ ## What This Dataset Measures
49
 
50
+ Economic datasets help analysts examine production, prices, public finance, trade flows, market conditions, and macroeconomic change.
51
+
52
+ Source-provided context: Table [tbl]
53
+
54
+ ## How To Read This Dataset
55
+
56
+ - **One row means:** one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
57
+ - **Primary geography column:** `country_iso3`.
58
+ - **Best time column:** `year`.
59
+ - **Time coverage basis:** year.
60
+ - **Recommended join keys:** `country_iso3` where available plus source-specific keys.
61
+
62
+ ## Coverage
63
+
64
+ | Dimension | Value |
65
+ |---|---:|
66
+ | Rows | 1,366 |
67
+ | Countries/areas | 1 |
68
+ | First period | 2020 |
69
+ | Last period | 2024 |
70
+ | Indicators | 0 |
71
+ | Columns | 209 |
72
+ | Source format | XLSX |
73
 
74
+ ## Geographic Coverage
75
 
76
+ Top areas shown below, sorted by row count when available:
77
 
78
+ | Area | Rows | First year | Last year | Name |
79
+ |------|-----:|-----------:|----------:|------|
80
  | `NGA` | 1,366 | 2020 | 2024 | `Nigeria` |
81
 
82
+ ## Indicators, Variables, Or Resource Contents
83
 
84
+ - This repo preserves one source tabular resource with its usable columns kept together.
85
 
86
  ## Schema
87
 
88
  | Column | Type | Description | Example |
89
  |--------|------|-------------|---------|
90
+ | `source_record_id` | `string` | Stable row identifier assigned during Electric Sheep Africa engineering. | `nbs-nada-84-1063:product-ranking:0` |
91
+ | `country_iso3` | `string` | ISO3 country or area code. | `NGA` |
92
+ | `country_name` | `string` | Country or area name. | `Nigeria` |
93
+ | `source_sheet` | `string` | Source column from the original resource. | `PRODUCT RANKING` |
94
+ | `year` | `double` | Observation year. | `2024.0` |
95
+ | `d_2710125000` | `string` | Source column from the original resource. | `2710192100` |
96
+ | `motor_spirit_ordinary` | `string` | Source column from the original resource. | `Gas oil` |
97
+ | `d_3320052110168_958` | `double` | Source column from the original resource. | `1331479375775.495` |
98
+ | `d_22_62533407342313` | `double` | Source column from the original resource. | `9.073702667654931` |
99
+ | `d_2709000000` | `double` | Source column from the original resource. | `2711110000.0` |
100
+ | `petroleum_oils_and_oils_obtained_from_bituminous_mineral` | `string` | Source column from the original resource. | `Natural gas` |
101
+ | `d_13406367224105_592` | `double` | Source column from the original resource. | `2108158471640.0` |
102
+ | `d_65_44035947521692` | `double` | Source column from the original resource. | `10.29053179796434` |
103
+ | `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2024` |
104
+ | `source_period_end_year` | `int64` | End year inferred from source metadata. | `2024` |
105
+ | `source_period_label` | `string` | Source column from the original resource. | `2024` |
106
  | `source_provider` | `string` | Publishing organization. | `National Bureau of Statistics, Nigeria` |
107
+ | `source_dataset` | `string` | Source dataset or package title. | `Foreign Trade in Goods Statistics` |
108
+ | `source_resource` | `string` | Source resource title, table name, or file name. | `Foreign Trade Statistics Report Q3 2024 Tables` |
109
+ | `source_package_id` | `string` | Source package identifier. | `NGA-NBS-FTS` |
110
+ | `source_resource_id` | `string` | Source resource identifier. | `nbs-nada-84-1063` |
111
+ | `source_url` | `string` | Original source URL or download URL. | `https://microdata.nigerianstat.gov.ng/index.php/catalog/84/download/1063` |
112
  | `license_id` | `string` | Source license identifier. | `other-open` |
113
+ | `retrieved_at` | `string` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-07-19T04:13:01Z` |
114
+ | `ranking` | `string` | Source column from the original resource. | `` |
115
+ | `code` | `string` | Source column from the original resource. | `` |
116
+ | `country_of_destination` | `string` | Source column from the original resource. | `` |
117
+ | `value` | `double` | Numeric observation value. | `` |
118
+ | `crude_oil` | `double` | Source column from the original resource. | `` |
119
+ | `non_crude_oil_value` | `double` | Source column from the original resource. | `` |
120
+ | `share_of_world_export` | `double` | Source column from the original resource. | `` |
121
+ | `ranking_2` | `string` | Source column from the original resource. | `` |
122
+ | `code_2` | `string` | Source column from the original resource. | `` |
123
+ | `country_of_origin` | `string` | Source column from the original resource. | `` |
124
+ | `value_2` | `double` | Source column from the original resource. | `` |
125
+ | `share_of_world_import` | `double` | Source column from the original resource. | `` |
126
+ | `product_code` | `double` | Source column from the original resource. | `` |
127
+ | `description` | `string` | Source column from the original resource. | `` |
128
+ | `july_value` | `double` | Source column from the original resource. | `` |
129
+ | `august_value` | `double` | Source column from the original resource. | `` |
130
+ | `september_value` | `double` | Source column from the original resource. | `` |
131
+ | `quarter_3_value` | `double` | Source column from the original resource. | `` |
132
+ | `2020` | `double` | Source column from the original resource. | `` |
133
+ | `jan_dec` | `string` | Source column from the original resource. | `` |
134
+ | `d_12700943_807826` | `double` | Source column from the original resource. | `` |
135
+ | `d_12522684_44384747` | `double` | Source column from the original resource. | `` |
136
+ | `d_178259_36397852935` | `double` | Source column from the original resource. | `` |
137
+ | `d_25223628_251673467` | `double` | Source column from the original resource. | `` |
138
+ | `d_9444655_98263812` | `double` | Source column from the original resource. | `` |
139
+ | `d_3078028_4612093493` | `double` | Source column from the original resource. | `` |
140
+ | `d_1433402_5234599148` | `double` | Source column from the original resource. | `` |
141
+ | `d_49_64664210437945` | `double` | Source column from the original resource. | `` |
142
+ | `d_75_42037831415917` | `double` | Source column from the original resource. | `` |
143
+ | `d_11_446447683700606` | `double` | Source column from the original resource. | `` |
144
+ | `d_25_111807952349185` | `double` | Source column from the original resource. | `` |
145
+ | `d_34_75129384107341` | `double` | Source column from the original resource. | `` |
146
+ | `d_1` | `double` | Source column from the original resource. | `` |
147
+ | `live_animals_animal_products` | `string` | Source column from the original resource. | `` |
148
+ | `d_454519_497526` | `double` | Source column from the original resource. | `` |
149
+ | `d_551225_96482` | `double` | Source column from the original resource. | `` |
150
+ | `d_549643_085907` | `double` | Source column from the original resource. | `` |
151
+ | `d_597473_381483` | `double` | Source column from the original resource. | `` |
152
+ | `d_132143_067024` | `double` | Source column from the original resource. | `` |
153
+ | `d_183622_158499` | `double` | Source column from the original resource. | `` |
154
+ | `d_154675_055025` | `double` | Source column from the original resource. | `` |
155
+ | `d_189004_962979` | `double` | Source column from the original resource. | `` |
156
+ | `d_414602_479166` | `double` | Source column from the original resource. | `` |
157
+ | `d_17` | `double` | Source column from the original resource. | `` |
158
+ | `vehicles_aircraft_and_parts_thereof_vessels_etc` | `string` | Source column from the original resource. | `` |
159
+ | `d_841996_8506065101` | `double` | Source column from the original resource. | `` |
160
+ | `d_654167_001405` | `double` | Source column from the original resource. | `` |
161
+ | `d_364783_14640246803` | `double` | Source column from the original resource. | `` |
162
+ | `d_118508_51106255999` | `double` | Source column from the original resource. | `` |
163
+ | `d_103280_23637861` | `double` | Source column from the original resource. | `` |
164
+ | `d_185037_908764` | `double` | Source column from the original resource. | `` |
165
+ | `d_30262_433039167998` | `double` | Source column from the original resource. | `` |
166
+ | `d_17943_590918759997` | `double` | Source column from the original resource. | `` |
167
+ | `d_667108_937973` | `double` | Source column from the original resource. | `` |
168
+ | `d_20486389_94592132` | `double` | Source column from the original resource. | `` |
169
+ | `d_406882_65486` | `double` | Source column from the original resource. | `` |
170
+ | `d_75018_470833` | `double` | Source column from the original resource. | `` |
171
+ | `d_331864_184027` | `double` | Source column from the original resource. | `` |
172
+ | `d_1571578_663792` | `double` | Source column from the original resource. | `` |
173
+ | `d_1026145_825876` | `double` | Source column from the original resource. | `` |
174
+ | `d_135365_023891` | `double` | Source column from the original resource. | `` |
175
+ | `d_283307_47592` | `double` | Source column from the original resource. | `` |
176
+ | `d_126760_33810499986` | `double` | Source column from the original resource. | `` |
177
+ | `d_4659558_709137` | `double` | Source column from the original resource. | `` |
178
+ | `d_395300_57961` | `double` | Source column from the original resource. | `` |
179
+ | `d_282149_181712` | `double` | Source column from the original resource. | `` |
180
+ | `d_1163118_008113` | `double` | Source column from the original resource. | `` |
181
+ | `d_272050_672054` | `double` | Source column from the original resource. | `` |
182
+ | `d_269948_729163` | `double` | Source column from the original resource. | `` |
183
+ | `d_194120_93440600001` | `double` | Source column from the original resource. | `` |
184
+ | `d_2082870_6040789997` | `double` | Source column from the original resource. | `` |
185
+ | `d_5968827_229148` | `double` | Source column from the original resource. | `` |
186
+ | `d_140594_462842` | `double` | Source column from the original resource. | `` |
187
+ | `d_1104562_3755939999` | `double` | Source column from the original resource. | `` |
188
+ | `d_3227000_370761` | `double` | Source column from the original resource. | `` |
189
+ | `d_1496670_0199509999` | `double` | Source column from the original resource. | `` |
190
+ | `d_94096_550889` | `double` | Source column from the original resource. | `` |
191
+ | `d_2374887_865016526` | `double` | Source column from the original resource. | `` |
192
+ | `d_841332_5030647159` | `double` | Source column from the original resource. | `` |
193
+ | `d_1533555_3619518103` | `double` | Source column from the original resource. | `` |
194
+ | `d_850175_86742181` | `double` | Source column from the original resource. | `` |
195
+ | `d_382197_7374368` | `double` | Source column from the original resource. | `` |
196
+ | `d_287047_38127052004` | `double` | Source column from the original resource. | `` |
197
+ | `d_53873_22074622` | `double` | Source column from the original resource. | `` |
198
+ | `d_127057_52796827012` | `double` | Source column from the original resource. | `` |
199
+ | `d_4868971_671319975` | `double` | Source column from the original resource. | `` |
200
+ | `d_116892_18503157001` | `double` | Source column from the original resource. | `` |
201
+ | `d_310162_230131095` | `double` | Source column from the original resource. | `` |
202
+ | `d_1072294_56642487` | `double` | Source column from the original resource. | `` |
203
+ | `d_440198_56906246004` | `double` | Source column from the original resource. | `` |
204
+ | `d_565612_78356893` | `double` | Source column from the original resource. | `` |
205
+ | `d_1361963_97495592` | `double` | Source column from the original resource. | `` |
206
+ | `d_1001847_3621451296` | `double` | Source column from the original resource. | `` |
207
+ | `d_4307663_291501557` | `double` | Source column from the original resource. | `` |
208
+ | `d_100540_78569555` | `double` | Source column from the original resource. | `` |
209
+ | `d_1880450_3436235301` | `double` | Source column from the original resource. | `` |
210
+ | `d_633481_581818185` | `double` | Source column from the original resource. | `` |
211
+ | `d_1693190_5803642925` | `double` | Source column from the original resource. | `` |
212
+ | `d_120985_748587608` | `double` | Source column from the original resource. | `` |
213
+ | `d_12522684_443847476` | `double` | Source column from the original resource. | `` |
214
+ | `northern_africa` | `string` | Source column from the original resource. | `` |
215
+ | `algeria` | `string` | Source column from the original resource. | `` |
216
+ | `d_572_75597417` | `double` | Source column from the original resource. | `` |
217
+ | `d_641_0005017130001` | `double` | Source column from the original resource. | `` |
218
+ | `d_1418_357439287` | `double` | Source column from the original resource. | `` |
219
+ | `d_1813_447706867` | `double` | Source column from the original resource. | `` |
220
+ | `d_3622_83277961` | `double` | Source column from the original resource. | `` |
221
+ | `d_531_418697751` | `double` | Source column from the original resource. | `` |
222
+ | `northern_africa_2` | `string` | Source column from the original resource. | `` |
223
+ | `algeria_2` | `string` | Source column from the original resource. | `` |
224
+ | `d_226_09702` | `double` | Source column from the original resource. | `` |
225
+ | `d_3089_55777` | `double` | Source column from the original resource. | `` |
226
+ | `d_19230_986895` | `double` | Source column from the original resource. | `` |
227
+ | `d_495_810859` | `double` | Source column from the original resource. | `` |
228
+ | `d_2864_970058` | `double` | Source column from the original resource. | `` |
229
+ | `d_1356_942783` | `double` | Source column from the original resource. | `` |
230
+ | `hs_code` | `double` | Source column from the original resource. | `` |
231
+ | `products_description` | `string` | Source column from the original resource. | `` |
232
+ | `value_million` | `double` | Source column from the original resource. | `` |
233
+ | `hs_code_2` | `double` | Source column from the original resource. | `` |
234
+ | `products_description_2` | `string` | Source column from the original resource. | `` |
235
+ | `value_million_2` | `double` | Source column from the original resource. | `` |
236
+ | `d_1685181_229068` | `double` | Source column from the original resource. | `` |
237
+ | `d_56148_411868` | `double` | Source column from the original resource. | `` |
238
+ | `d_161277_661645` | `double` | Source column from the original resource. | `` |
239
+ | `d_2895774_94723` | `double` | Source column from the original resource. | `` |
240
+ | `d_120216_559547` | `double` | Source column from the original resource. | `` |
241
+ | `d_2013771_731769` | `double` | Source column from the original resource. | `` |
242
+ | `d_1121645_66003` | `double` | Source column from the original resource. | `` |
243
+ | `d_4157705_570431` | `double` | Source column from the original resource. | `` |
244
+ | `d_489221_457018` | `double` | Source column from the original resource. | `` |
245
+ | `d_0_57922` | `double` | Source column from the original resource. | `` |
246
+ | `d_1_food_and_beverage` | `string` | Source column from the original resource. | `` |
247
+ | `d_1804392_195325` | `double` | Source column from the original resource. | `` |
248
+ | `d_2924630_378086` | `double` | Source column from the original resource. | `` |
249
+ | `d_2864125_2981439997` | `double` | Source column from the original resource. | `` |
250
+ | `d_3825114_26213` | `double` | Source column from the original resource. | `` |
251
+ | `d_498089_10916` | `double` | Source column from the original resource. | `` |
252
+ | `d_776581_027595` | `double` | Source column from the original resource. | `` |
253
+ | `d_765153_621472` | `double` | Source column from the original resource. | `` |
254
+ | `d_1134792_1321780002` | `double` | Source column from the original resource. | `` |
255
+ | `d_1591992_326445` | `double` | Source column from the original resource. | `` |
256
+ | `period` | `string` | Source column from the original resource. | `` |
257
+ | `imports` | `double` | Source column from the original resource. | `` |
258
+ | `exports` | `double` | Source column from the original resource. | `` |
259
+ | `domestic_export` | `double` | Source column from the original resource. | `` |
260
+ | `re_exports` | `double` | Source column from the original resource. | `` |
261
+ | `trade_balance` | `double` | Source column from the original resource. | `` |
262
+ | `2024` | `double` | Source column from the original resource. | `` |
263
+ | `jan_sep` | `string` | Source column from the original resource. | `` |
264
+ | `d_57199371_75096349` | `double` | Source column from the original resource. | `` |
265
+ | `d_2892775_604916276` | `double` | Source column from the original resource. | `` |
266
+ | `d_1158258_362560994` | `double` | Source column from the original resource. | `` |
267
+ | `d_199606_97612359398` | `double` | Source column from the original resource. | `` |
268
+ | `d_183976_72611676998` | `double` | Source column from the original resource. | `` |
269
+ | `d_1790328_9405883688` | `double` | Source column from the original resource. | `` |
270
+ | `d_41502394_73686` | `double` | Source column from the original resource. | `` |
271
+ | `d_9472030_403797518` | `double` | Source column from the original resource. | `` |
272
+ | `d_5_057355555426269` | `double` | Source column from the original resource. | `` |
273
+ | `d_2_024949448053132` | `double` | Source column from the original resource. | `` |
274
+ | `d_0_34896707780751407` | `double` | Source column from the original resource. | `` |
275
+ | `d_0_3216411657767395` | `double` | Source column from the original resource. | `` |
276
+ | `d_3_129980078073518` | `double` | Source column from the original resource. | `` |
277
+ | `d_72_55743108080715` | `double` | Source column from the original resource. | `` |
278
+ | `d_16_559675594055747` | `double` | Source column from the original resource. | `` |
279
+ | `sectors` | `string` | Source column from the original resource. | `` |
280
+ | `july` | `double` | Source column from the original resource. | `` |
281
+ | `august` | `double` | Source column from the original resource. | `` |
282
+ | `september` | `double` | Source column from the original resource. | `` |
283
+ | `q3_2024` | `double` | Source column from the original resource. | `` |
284
+ | `share_of_total_exports` | `double` | Source column from the original resource. | `` |
285
+ | `hs10` | `string` | Source column from the original resource. | `` |
286
+ | `value_n_million` | `double` | Source column from the original resource. | `` |
287
+ | `sector` | `string` | Source column from the original resource. | `` |
288
+ | `region` | `string` | Source column from the original resource. | `` |
289
+ | `regions` | `string` | Source column from the original resource. | `` |
290
+ | `product` | `double` | Source column from the original resource. | `` |
291
+ | `product_description` | `string` | Source column from the original resource. | `` |
292
+ | `country_description` | `string` | Source column from the original resource. | `` |
293
+ | `value_n` | `double` | Source column from the original resource. | `` |
294
+ | `mode_of_transport` | `string` | Source column from the original resource. | `` |
295
+ | `share_of_domestic_exports` | `string` | Source column from the original resource. | `` |
296
+ | `rank` | `double` | Source column from the original resource. | `` |
297
+ | `ports` | `string` | Source column from the original resource. | `` |
298
+ | `share_of_total_export` | `double` | Source column from the original resource. | `` |
299
 
300
  ## Usage
301
 
 
307
  print(df.head())
308
  ```
309
 
310
+ ### Inspect Columns
311
 
312
  ```python
313
+ print(df.info())
314
+ print(df.head())
315
  ```
316
 
317
+ ### Filter By Geography
318
 
319
  ```python
320
+ if "country_iso3" in df.columns:
321
+ sample = df[df["country_iso3"] == "NGA"]
 
322
  ```
323
 
324
+ ### Time-Series Pattern
325
+
326
+ ```python
327
+ if "value" in df.columns and "year" in df.columns:
328
+ trend = df.sort_values("year")
329
+ ```
330
+
331
+ ### Pivot For Analysis
332
+
333
+ ```python
334
+ if {"indicator_id", "year", "value"}.issubset(df.columns):
335
+ matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
336
+ print(matrix.tail())
337
+ ```
338
+
339
+ ## Data Quality Notes
340
+
341
+ - Canonical time field: `year`.
342
+ - Missing values are preserved rather than silently imputed.
343
+ - Column names are standardized for machine use; source meanings are preserved where known.
344
+ - Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.
345
+
346
+ ## Source And Provenance
347
+
348
+ - **Source:** [National Bureau of Statistics, Nigeria](https://microdata.nigerianstat.gov.ng/index.php/catalog/84/related-materials)
349
+ - **Publisher:** National Bureau of Statistics, Nigeria
350
+ - **Portal:** [https://microdata.nigerianstat.gov.ng](https://microdata.nigerianstat.gov.ng)
351
+ - **Resource:** [Foreign Trade Statistics Report Q3 2024 Tables](https://microdata.nigerianstat.gov.ng/index.php/catalog/84/download/1063)
352
+ - **License:** other-open
353
+ - **Retrieved/generated:** `2026-07-19T04:17:01Z`
354
+ - **Hugging Face repo:** [electricsheepafrica/africa-nigeria-foreign-trade-in-goods-statistics-78a5781e](https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-foreign-trade-in-goods-statistics-78a5781e)
355
+
356
+ ## Transformations Applied
357
+
358
+ - Converted the source table to Parquet for efficient analytics and ML workflows.
359
+ - Added or preserved source provenance columns where available.
360
+ - Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
361
+ - Preserved source-reported values without analytical imputation.
362
+
363
+ ## Suggested Analyses
364
+
365
+ - Build time-series dashboards
366
+ - Compare economic indicators
367
+ - Join with population or sector data
368
+ - Build time-series views and period-over-period comparisons
369
+ - Check missingness before modeling
370
+ - Use `country_iso3` as the safest geography join key when present
371
+
372
  ## Citation
373
 
374
  ```bibtex
375
  @misc{electric_sheep_africa_africa_nigeria_foreign_trade_in_goods_statistics_78a5781e_2024,
376
+ title = {Foreign Trade in Goods Statistics | Africa (National Bureau of Statistics, Nigeria)},
377
  author = {National Bureau of Statistics, Nigeria},
378
  year = {2024},
379
  url = {https://microdata.nigerianstat.gov.ng/index.php/catalog/84/related-materials},
380
+ publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa},
381
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-foreign-trade-in-goods-statistics-78a5781e}}
382
  }
383
  ```
384
 
385
  ## License
386
 
387
+ Released under other-open.
 
 
 
388
 
389
+ Original data is published by National Bureau of Statistics, Nigeria. Electric Sheep Africa
390
+ engineering standardizes the data for discovery, loading, and analysis on
391
+ Hugging Face. Cite both the original source and this ML-ready dataset when used.
392
 
393
+ ## About Electric Sheep Africa
 
 
 
 
394
 
395
+ Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
396
 
397
  ---
398
 
399
+ Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://microdata.nigerianstat.gov.ng/index.php/catalog/84/related-materials