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Datasets:
electricsheepafrica
/
africa-nigeria-consumer-price-index-and-inflation-1b50c949

Tasks:
Tabular Classification
Tabular Regression
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 +311 -242
README.md CHANGED
@@ -5,256 +5,283 @@ language:
5
  task_categories:
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  - tabular-classification
7
  - tabular-regression
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- multilinguality: monolingual
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  size_categories:
10
  - 1K<n<10K
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  tags:
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- - tabular
13
- - zip
14
- - africa
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- - nigeria
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- - official-statistics
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- - open-data
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- - economics
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- pretty_name: "Consumer Price Index and Inflation | Africa (Nigeria official open data)"
 
 
 
 
 
 
 
 
 
 
 
 
20
  ---
21
 
22
- # Consumer Price Index and Inflation | Africa (Nigeria official open data)
23
 
24
- 1,940 rows - 1 Africa country - 2026 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
25
 
26
  ![rows](https://img.shields.io/badge/rows-1940-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-2026-orange)
29
  ![indicators](https://img.shields.io/badge/indicators-0-purple)
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  ![license](https://img.shields.io/badge/license-other-lightgrey)
31
 
32
  ## TL;DR
33
 
34
- This dataset packages one official `ZIP` resource from **Nigeria** as
35
- ML-ready Parquet. The source file is the provenance boundary; all usable
36
- indicators or tabular columns from the resource stay together in this repo.
37
 
38
- ## About the source
39
 
40
- - **Source:** [Consumer Price Index and Inflation](https://microdata.nigerianstat.gov.ng/index.php/catalog/154/related-materials)
41
- - **Publisher:** National Bureau of Statistics, Nigeria
42
- - **Resource:** [June 2026 CPI Report](https://microdata.nigerianstat.gov.ng/index.php/catalog/154/download/1431)
43
- - **Format:** `ZIP`
44
- - **License:** [Other open license]()
45
- - **Packaging mode:** `tabular_resource`
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
46
 
47
- ## Geographic coverage
48
 
49
- 1 Africa country:
50
 
51
- | Country | Rows | First year | Last year | Name |
52
- |---------|-----:|-----------:|----------:|------|
53
  | `NGA` | 1,940 | 2026 | 2026 | `Nigeria` |
54
 
55
- ## Indicators or Resource Contents
56
 
57
- - This source file is packaged as a normalized tabular resource.
58
 
59
  ## Schema
60
 
61
  | Column | Type | Description | Example |
62
  |--------|------|-------------|---------|
63
- | `source_record_id` | `string` | Stable row identifier for tabular resources. | `nbs-nada-154-1431:cpi-1new-june2026-xlsx-table1:0` |
64
- | `country_iso3` | `string` | ISO3 country code. | `NGA` |
65
- | `country_name` | `string` | Country name. | `Nigeria` |
66
- | `source_sheet` | `string` | Workbook sheet name, when the source is a spreadsheet. | `cpi_1New_June2026.xlsx::Table1` |
67
- | `year` | `Int64` | Observation year. | `2026` |
68
- | `2024` | `float64` | Source column. | `` |
69
- | `jan` | `string` | Source column. | `Feb` |
70
- | `d_86_73563050706824` | `float64` | Source column. | `89.43951980870575` |
71
- | `d_76_7152746388152` | `float64` | Source column. | `78.50919563355824` |
72
- | `d_2_6402068440885955` | `float64` | Source column. | `3.117391648426618` |
73
- | `d_29_899058738813608` | `float64` | Source column. | `31.698232462346965` |
74
- | `d_14_892137270617866` | `float64` | Source column. | `16.582783756187652` |
75
- | `d_88_92092325261503` | `float64` | Source column. | `90.76638226361568` |
76
- | `d_80_54925315106031` | `float64` | Source column. | `82.04601641383275` |
77
- | `d_2_126278705302127` | `float64` | Source column. | `2.0753934434057726` |
78
- | `d_23_435510161655188` | `float64` | Source column. | `24.670151928587103` |
79
- | `d_11_814382848853782` | `float64` | Source column. | `13.289124536124646` |
80
- | `d_85_11460096832012` | `float64` | Source column. | `88.34014970998146` |
81
- | `d_73_73297683298414` | `float64` | Source column. | `75.75700952028821` |
82
- | `d_3_2119205600616567` | `float64` | Source column. | `3.78965383725631` |
83
- | `d_35_41327723358097` | `float64` | Source column. | `37.91994663367743` |
84
- | `d_17_305655193734765` | `float64` | Source column. | `19.389625735997967` |
85
- | `d_88_52822351156745` | `float64` | Source column. | `90.45065003949202` |
86
- | `d_80_05598302862008` | `float64` | Source column. | `81.56960923646007` |
87
- | `d_2_2405367816081423` | `float64` | Source column. | `2.171540839372412` |
88
- | `d_23_591766815849624` | `float64` | Source column. | `25.126897555194237` |
89
- | `d_11_7639097930617` | `float64` | Source column. | `13.356710870969051` |
90
- | `2024_2` | `string` | Source column. | `2024-02-01 00:00:00` |
91
- | `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2026` |
92
- | `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2026` |
93
- | `source_period_label` | `string` | Human-readable period inferred from source resource metadata. | `2026` |
94
  | `source_provider` | `string` | Publishing organization. | `National Bureau of Statistics, Nigeria` |
95
- | `source_dataset` | `string` | Source package title. | `Consumer Price Index and Inflation` |
96
- | `source_resource` | `string` | Source resource title. | `June 2026 CPI Report` |
97
- | `source_package_id` | `string` | CKAN package UUID. | `NGA-NBS-CPI` |
98
- | `source_resource_id` | `string` | CKAN resource UUID. | `nbs-nada-154-1431` |
99
- | `source_url` | `string` | Original source resource URL. | `https://microdata.nigerianstat.gov.ng/index.php/catalog/154/download/143` |
100
  | `license_id` | `string` | Source license identifier. | `other-open` |
101
- | `retrieved_at` | `string` | UTC retrieval timestamp. | `2026-07-19T04:13:01Z` |
102
- | `column_1` | `float64` | Source column. | `` |
103
- | `column_2` | `string` | Source column. | `` |
104
- | `monthly` | `float64` | Source column. | `` |
105
- | `monthly_2` | `float64` | Source column. | `` |
106
- | `d_12_month_average_2` | `float64` | Source column. | `` |
107
- | `month_on_change_2` | `float64` | Source column. | `` |
108
- | `year_on_change_2` | `float64` | Source column. | `` |
109
- | `d_12_month_average_change_2` | `float64` | Source column. | `` |
110
- | `monthly_3` | `float64` | Source column. | `` |
111
- | `monthly_4` | `float64` | Source column. | `` |
112
- | `d_12_month_average_4` | `float64` | Source column. | `` |
113
- | `month_on_change_4` | `float64` | Source column. | `` |
114
- | `year_on_change_4` | `float64` | Source column. | `` |
115
- | `d_12_month_average_change_4` | `float64` | Source column. | `` |
116
- | `monthly_5` | `float64` | Source column. | `` |
117
- | `monthly_6` | `float64` | Source column. | `` |
118
- | `d_12_month_average_6` | `float64` | Source column. | `` |
119
- | `month_on_change_6` | `float64` | Source column. | `` |
120
- | `year_on_change_6` | `float64` | Source column. | `` |
121
- | `d_12_month_average_change_6` | `float64` | Source column. | `` |
122
- | `monthly_8` | `float64` | Source column. | `` |
123
- | `d_12_month_average_8` | `float64` | Source column. | `` |
124
- | `month_on_change_8` | `float64` | Source column. | `` |
125
- | `year_on_change_8` | `float64` | Source column. | `` |
126
- | `d_12_month_average_change_8` | `float64` | Source column. | `` |
127
- | `2025` | `float64` | Source column. | `` |
128
- | `d_110_68` | `float64` | Source column. | `` |
129
- | `d_110_7` | `float64` | Source column. | `` |
130
- | `d_110_9` | `float64` | Source column. | `` |
131
- | `d_111_47` | `float64` | Source column. | `` |
132
- | `d_110_33` | `float64` | Source column. | `` |
133
- | `d_110_5` | `float64` | Source column. | `` |
134
- | `d_108_91` | `float64` | Source column. | `` |
135
- | `d_110_41` | `float64` | Source column. | `` |
136
- | `d_110_79` | `float64` | Source column. | `` |
137
- | `d_110_82` | `float64` | Source column. | `` |
138
- | `d_110_71` | `float64` | Source column. | `` |
139
- | `d_106_39` | `float64` | Source column. | `` |
140
- | `d_110_64` | `float64` | Source column. | `` |
141
- | `d_114_7989` | `float64` | Source column. | `` |
142
- | `d_112_734` | `float64` | Source column. | `` |
143
- | `d_107_6146` | `float64` | Source column. | `` |
144
- | `d_111_482` | `float64` | Source column. | `` |
145
- | `d_109_417` | `float64` | Source column. | `` |
146
- | `d_112_7659` | `float64` | Source column. | `` |
147
- | `d_107_5387` | `float64` | Source column. | `` |
148
- | `d_106_8527` | `float64` | Source column. | `` |
149
- | `d_104_8804` | `float64` | Source column. | `` |
150
- | `d_114_1385` | `float64` | Source column. | `` |
151
- | `d_104_653` | `float64` | Source column. | `` |
152
- | `d_112_0402` | `float64` | Source column. | `` |
153
- | `d_2_8345337043662937` | `float64` | Source column. | `` |
154
- | `d_27_60615142006779` | `float64` | Source column. | `` |
155
- | `d_32_953136913890404` | `float64` | Source column. | `` |
156
- | `2025_2` | `string` | Source column. | `` |
157
- | `all_items` | `float64` | Source column. | `` |
158
- | `all_items_2` | `float64` | Source column. | `` |
159
- | `all_items_less_farm_produce` | `float64` | Source column. | `` |
160
- | `all_items_less_farm_produce_2` | `float64` | Source column. | `` |
161
- | `all_items_less_farm_produce_and_energy` | `float64` | Source column. | `` |
162
- | `core_index_all_items_less_farm_produce_and_energy` | `float64` | Source column. | `` |
163
- | `imported_food` | `float64` | Source column. | `` |
164
- | `imported_food_2` | `float64` | Source column. | `` |
165
- | `food` | `float64` | Source column. | `` |
166
- | `food_2` | `float64` | Source column. | `` |
167
- | `farm_produce` | `float64` | Source column. | `` |
168
- | `energy` | `float64` | Source column. | `` |
169
- | `services` | `float64` | Source column. | `` |
170
- | `goods` | `float64` | Source column. | `` |
171
- | `food_non_alcoholic_bev` | `float64` | Source column. | `` |
172
- | `food_and_non_alcoholic_beverages` | `float64` | Source column. | `` |
173
- | `alcoholic_beverage_tobacco_and_kola` | `float64` | Source column. | `` |
174
- | `alcoholic_beverages_tobacco_and_narcotics` | `float64` | Source column. | `` |
175
- | `clothing_and_footwear` | `float64` | Source column. | `` |
176
- | `clothing_and_footwear_2` | `float64` | Source column. | `` |
177
- | `housing_water_electricity_gas_and_other_fuel` | `float64` | Source column. | `` |
178
- | `housing_water_electricity_gas_and_other_fuels` | `float64` | Source column. | `` |
179
- | `furnishings_household_equipment_maintenance` | `float64` | Source column. | `` |
180
- | `furnishings_household_equipment_and_routine_household_ma` | `float64` | Source column. | `` |
181
- | `health` | `float64` | Source column. | `` |
182
- | `health_2` | `float64` | Source column. | `` |
183
- | `transport` | `float64` | Source column. | `` |
184
- | `transport_2` | `float64` | Source column. | `` |
185
- | `communication` | `float64` | Source column. | `` |
186
- | `information_and_communication` | `float64` | Source column. | `` |
187
- | `recreation_culture` | `float64` | Source column. | `` |
188
- | `recreation_sport_and_culture` | `float64` | Source column. | `` |
189
- | `education` | `float64` | Source column. | `` |
190
- | `education_services` | `float64` | Source column. | `` |
191
- | `restaurant_hotels` | `float64` | Source column. | `` |
192
- | `restaurants_and_accomodation_services` | `float64` | Source column. | `` |
193
- | `insurance_and_financial_services` | `float64` | Source column. | `` |
194
- | `miscellaneous_goods_services` | `float64` | Source column. | `` |
195
- | `personal_care_social_protection_and_miscellaneous_goods` | `float64` | Source column. | `` |
196
- | `month_on` | `float64` | Source column. | `` |
197
- | `month_on_2` | `float64` | Source column. | `` |
198
- | `year_on` | `float64` | Source column. | `` |
199
- | `year_on_2` | `float64` | Source column. | `` |
200
- | `d_12_month_average` | `float64` | Source column. | `` |
201
- | `d_111_1857` | `float64` | Source column. | `` |
202
- | `d_111_0659` | `float64` | Source column. | `` |
203
- | `d_111_2949` | `float64` | Source column. | `` |
204
- | `d_112_6677` | `float64` | Source column. | `` |
205
- | `d_111_1456` | `float64` | Source column. | `` |
206
- | `d_111_3583` | `float64` | Source column. | `` |
207
- | `d_108_8475` | `float64` | Source column. | `` |
208
- | `d_110_6382` | `float64` | Source column. | `` |
209
- | `d_111_4726` | `float64` | Source column. | `` |
210
- | `d_111_563254` | `float64` | Source column. | `` |
211
- | `d_115_610117` | `float64` | Source column. | `` |
212
- | `d_113_577006` | `float64` | Source column. | `` |
213
- | `d_107_09951` | `float64` | Source column. | `` |
214
- | `d_112_3353` | `float64` | Source column. | `` |
215
- | `d_110_265294` | `float64` | Source column. | `` |
216
- | `d_114_115116` | `float64` | Source column. | `` |
217
- | `d_107_251684` | `float64` | Source column. | `` |
218
- | `d_106_967382` | `float64` | Source column. | `` |
219
- | `d_105_019004` | `float64` | Source column. | `` |
220
- | `d_114_504004` | `float64` | Source column. | `` |
221
- | `d_104_834431` | `float64` | Source column. | `` |
222
- | `d_112_120888` | `float64` | Source column. | `` |
223
- | `d_3_1471831870236997` | `float64` | Source column. | `` |
224
- | `d_29_450518009532345` | `float64` | Source column. | `` |
225
- | `d_35_2544420471057` | `float64` | Source column. | `` |
226
- | `d_109_4697` | `float64` | Source column. | `` |
227
- | `d_109_7349` | `float64` | Source column. | `` |
228
- | `d_109_783` | `float64` | Source column. | `` |
229
- | `d_109_253` | `float64` | Source column. | `` |
230
- | `d_108_6858` | `float64` | Source column. | `` |
231
- | `d_108_8997` | `float64` | Source column. | `` |
232
- | `d_109_107` | `float64` | Source column. | `` |
233
- | `d_109_7192` | `float64` | Source column. | `` |
234
- | `d_109_3588` | `float64` | Source column. | `` |
235
- | `d_108_7702` | `float64` | Source column. | `` |
236
- | `d_113_0705` | `float64` | Source column. | `` |
237
- | `d_111_0863` | `float64` | Source column. | `` |
238
- | `d_110_7025` | `float64` | Source column. | `` |
239
- | `d_109_7418` | `float64` | Source column. | `` |
240
- | `d_108_288` | `float64` | Source column. | `` |
241
- | `d_108_814` | `float64` | Source column. | `` |
242
- | `d_108_4919` | `float64` | Source column. | `` |
243
- | `d_106_324` | `float64` | Source column. | `` |
244
- | `d_104_228` | `float64` | Source column. | `` |
245
- | `d_113_228` | `float64` | Source column. | `` |
246
- | `d_104_169` | `float64` | Source column. | `` |
247
- | `d_111_8739` | `float64` | Source column. | `` |
248
- | `d_3_1803934501831037` | `float64` | Source column. | `` |
249
- | `d_25_037360107444258` | `float64` | Source column. | `` |
250
- | `d_30_7876601999111` | `float64` | Source column. | `` |
251
- | `state` | `string` | Source column. | `` |
252
- | `food_3` | `float64` | Source column. | `` |
253
- | `all_items_3` | `float64` | Source column. | `` |
254
- | `food_4` | `float64` | Source column. | `` |
255
- | `all_items_4` | `float64` | Source column. | `` |
256
- | `food_5` | `float64` | Source column. | `` |
257
- | `all_items_5` | `float64` | Source column. | `` |
258
 
259
  ## Usage
260
 
@@ -266,51 +293,93 @@ df = ds["train"].to_pandas()
266
  print(df.head())
267
  ```
268
 
269
- ### Filter to one country
270
 
271
  ```python
272
- sample_country = df[df["country_iso3"] == "NGA"]
 
273
  ```
274
 
275
- ### Work with indicators
276
 
277
  ```python
278
- if "indicator_id" in df.columns:
279
- print(df["indicator_id"].value_counts().head())
280
- sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])
281
  ```
282
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
283
  ## Citation
284
 
285
  ```bibtex
286
  @misc{electric_sheep_africa_africa_nigeria_consumer_price_index_and_inflation_1b50c949_2026,
287
- title = {Consumer Price Index and Inflation | Africa (Nigeria official open data)},
288
  author = {National Bureau of Statistics, Nigeria},
289
  year = {2026},
290
  url = {https://microdata.nigerianstat.gov.ng/index.php/catalog/154/related-materials},
291
- publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
292
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-consumer-price-index-and-inflation-1b50c949}}
293
  }
294
  ```
295
 
296
  ## License
297
 
298
- Released under [Other open license]().
299
-
300
- Original data (c) National Bureau of Statistics, Nigeria. When using this dataset, please cite both the
301
- original source above and the Electric Sheep Africa repackaging.
302
 
303
- ## About Electric Sheep
 
 
304
 
305
- Electric Sheep Africa is part of the Electric Sheep mission: a unified,
306
- ML-ready data layer for Africa on Hugging Face. We pull data from authoritative
307
- open sources, normalize the schemas, package as Parquet, and publish with
308
- consistent dataset cards so researchers and developers can use `load_dataset()`
309
- to start working in seconds.
310
 
311
- Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica)
312
 
313
  ---
314
 
315
- Provenance: ingested 2026-07-19 via the Electric Sheep pipeline. Source URL:
316
- https://microdata.nigerianstat.gov.ng/index.php/catalog/154/download/1431
 
5
  task_categories:
6
  - tabular-classification
7
  - tabular-regression
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
+ - "june-2026-cpi-report"
21
+ - "cpi-june-2026-zip"
22
+ - "0"
23
+ - "document"
24
+ - "cpi"
25
+ - "report-doc"
26
+ configs:
27
+ - config_name: default
28
+ data_files:
29
+ - split: train
30
+ path: data/train-00000-of-00001.parquet
31
+ pretty_name: "Consumer Price Index and Inflation | Africa (National Bureau of Statistics, Nigeria)"
32
  ---
33
 
34
+ # Consumer Price Index and Inflation | Africa (National Bureau of Statistics, Nigeria)
35
 
36
+ **1,940 rows** - **1 Africa country/area** - **2026** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
37
 
38
  ![rows](https://img.shields.io/badge/rows-1940-blue)
39
  ![countries](https://img.shields.io/badge/countries-1-green)
40
+ ![period](https://img.shields.io/badge/period-2026-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 **1,940 rows** from **National Bureau of Statistics, Nigeria**, covering **Consumer Price Index and Inflation**. 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: Document, Report [doc/rep]
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 | 1,940 |
67
+ | Countries/areas | 1 |
68
+ | First period | 2026 |
69
+ | Last period | 2026 |
70
+ | Indicators | 0 |
71
+ | Columns | 195 |
72
+ | Source format | ZIP |
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` | 1,940 | 2026 | 2026 | `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-154-1431:cpi-1new-june2026-xlsx-table1:0` |
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. | `cpi_1New_June2026.xlsx::Table1` |
94
+ | `year` | `int64` | Observation year. | `2026` |
95
+ | `2024` | `double` | Source column from the original resource. | `` |
96
+ | `jan` | `string` | Source column from the original resource. | `Feb` |
97
+ | `d_86_73563050706824` | `double` | Source column from the original resource. | `89.43951980870575` |
98
+ | `d_76_7152746388152` | `double` | Source column from the original resource. | `78.50919563355824` |
99
+ | `d_2_6402068440885955` | `double` | Source column from the original resource. | `3.117391648426618` |
100
+ | `d_29_899058738813608` | `double` | Source column from the original resource. | `31.698232462346965` |
101
+ | `d_14_892137270617866` | `double` | Source column from the original resource. | `16.582783756187652` |
102
+ | `d_88_92092325261503` | `double` | Source column from the original resource. | `90.76638226361568` |
103
+ | `d_80_54925315106031` | `double` | Source column from the original resource. | `82.04601641383275` |
104
+ | `d_2_126278705302127` | `double` | Source column from the original resource. | `2.0753934434057726` |
105
+ | `d_23_435510161655188` | `double` | Source column from the original resource. | `24.670151928587103` |
106
+ | `d_11_814382848853782` | `double` | Source column from the original resource. | `13.289124536124646` |
107
+ | `d_85_11460096832012` | `double` | Source column from the original resource. | `88.34014970998146` |
108
+ | `d_73_73297683298414` | `double` | Source column from the original resource. | `75.75700952028821` |
109
+ | `d_3_2119205600616567` | `double` | Source column from the original resource. | `3.78965383725631` |
110
+ | `d_35_41327723358097` | `double` | Source column from the original resource. | `37.91994663367743` |
111
+ | `d_17_305655193734765` | `double` | Source column from the original resource. | `19.389625735997967` |
112
+ | `d_88_52822351156745` | `double` | Source column from the original resource. | `90.45065003949202` |
113
+ | `d_80_05598302862008` | `double` | Source column from the original resource. | `81.56960923646007` |
114
+ | `d_2_2405367816081423` | `double` | Source column from the original resource. | `2.171540839372412` |
115
+ | `d_23_591766815849624` | `double` | Source column from the original resource. | `25.126897555194237` |
116
+ | `d_11_7639097930617` | `double` | Source column from the original resource. | `13.356710870969051` |
117
+ | `2024_2` | `string` | Source column from the original resource. | `2024-02-01 00:00:00` |
118
+ | `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2026` |
119
+ | `source_period_end_year` | `int64` | End year inferred from source metadata. | `2026` |
120
+ | `source_period_label` | `string` | Source column from the original resource. | `2026` |
121
  | `source_provider` | `string` | Publishing organization. | `National Bureau of Statistics, Nigeria` |
122
+ | `source_dataset` | `string` | Source dataset or package title. | `Consumer Price Index and Inflation` |
123
+ | `source_resource` | `string` | Source resource title, table name, or file name. | `June 2026 CPI Report` |
124
+ | `source_package_id` | `string` | Source package identifier. | `NGA-NBS-CPI` |
125
+ | `source_resource_id` | `string` | Source resource identifier. | `nbs-nada-154-1431` |
126
+ | `source_url` | `string` | Original source URL or download URL. | `https://microdata.nigerianstat.gov.ng/index.php/catalog/154/download/...` |
127
  | `license_id` | `string` | Source license identifier. | `other-open` |
128
+ | `retrieved_at` | `string` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-07-19T04:13:01Z` |
129
+ | `column_1` | `double` | Source column from the original resource. | `` |
130
+ | `column_2` | `string` | Source column from the original resource. | `` |
131
+ | `monthly` | `double` | Source column from the original resource. | `` |
132
+ | `monthly_2` | `double` | Source column from the original resource. | `` |
133
+ | `d_12_month_average_2` | `double` | Source column from the original resource. | `` |
134
+ | `month_on_change_2` | `double` | Source column from the original resource. | `` |
135
+ | `year_on_change_2` | `double` | Source column from the original resource. | `` |
136
+ | `d_12_month_average_change_2` | `double` | Source column from the original resource. | `` |
137
+ | `monthly_3` | `double` | Source column from the original resource. | `` |
138
+ | `monthly_4` | `double` | Source column from the original resource. | `` |
139
+ | `d_12_month_average_4` | `double` | Source column from the original resource. | `` |
140
+ | `month_on_change_4` | `double` | Source column from the original resource. | `` |
141
+ | `year_on_change_4` | `double` | Source column from the original resource. | `` |
142
+ | `d_12_month_average_change_4` | `double` | Source column from the original resource. | `` |
143
+ | `monthly_5` | `double` | Source column from the original resource. | `` |
144
+ | `monthly_6` | `double` | Source column from the original resource. | `` |
145
+ | `d_12_month_average_6` | `double` | Source column from the original resource. | `` |
146
+ | `month_on_change_6` | `double` | Source column from the original resource. | `` |
147
+ | `year_on_change_6` | `double` | Source column from the original resource. | `` |
148
+ | `d_12_month_average_change_6` | `double` | Source column from the original resource. | `` |
149
+ | `monthly_8` | `double` | Source column from the original resource. | `` |
150
+ | `d_12_month_average_8` | `double` | Source column from the original resource. | `` |
151
+ | `month_on_change_8` | `double` | Source column from the original resource. | `` |
152
+ | `year_on_change_8` | `double` | Source column from the original resource. | `` |
153
+ | `d_12_month_average_change_8` | `double` | Source column from the original resource. | `` |
154
+ | `2025` | `double` | Source column from the original resource. | `` |
155
+ | `d_110_68` | `double` | Source column from the original resource. | `` |
156
+ | `d_110_7` | `double` | Source column from the original resource. | `` |
157
+ | `d_110_9` | `double` | Source column from the original resource. | `` |
158
+ | `d_111_47` | `double` | Source column from the original resource. | `` |
159
+ | `d_110_33` | `double` | Source column from the original resource. | `` |
160
+ | `d_110_5` | `double` | Source column from the original resource. | `` |
161
+ | `d_108_91` | `double` | Source column from the original resource. | `` |
162
+ | `d_110_41` | `double` | Source column from the original resource. | `` |
163
+ | `d_110_79` | `double` | Source column from the original resource. | `` |
164
+ | `d_110_82` | `double` | Source column from the original resource. | `` |
165
+ | `d_110_71` | `double` | Source column from the original resource. | `` |
166
+ | `d_106_39` | `double` | Source column from the original resource. | `` |
167
+ | `d_110_64` | `double` | Source column from the original resource. | `` |
168
+ | `d_114_7989` | `double` | Source column from the original resource. | `` |
169
+ | `d_112_734` | `double` | Source column from the original resource. | `` |
170
+ | `d_107_6146` | `double` | Source column from the original resource. | `` |
171
+ | `d_111_482` | `double` | Source column from the original resource. | `` |
172
+ | `d_109_417` | `double` | Source column from the original resource. | `` |
173
+ | `d_112_7659` | `double` | Source column from the original resource. | `` |
174
+ | `d_107_5387` | `double` | Source column from the original resource. | `` |
175
+ | `d_106_8527` | `double` | Source column from the original resource. | `` |
176
+ | `d_104_8804` | `double` | Source column from the original resource. | `` |
177
+ | `d_114_1385` | `double` | Source column from the original resource. | `` |
178
+ | `d_104_653` | `double` | Source column from the original resource. | `` |
179
+ | `d_112_0402` | `double` | Source column from the original resource. | `` |
180
+ | `d_2_8345337043662937` | `double` | Source column from the original resource. | `` |
181
+ | `d_27_60615142006779` | `double` | Source column from the original resource. | `` |
182
+ | `d_32_953136913890404` | `double` | Source column from the original resource. | `` |
183
+ | `2025_2` | `string` | Source column from the original resource. | `` |
184
+ | `all_items` | `double` | Source column from the original resource. | `` |
185
+ | `all_items_2` | `double` | Source column from the original resource. | `` |
186
+ | `all_items_less_farm_produce` | `double` | Source column from the original resource. | `` |
187
+ | `all_items_less_farm_produce_2` | `double` | Source column from the original resource. | `` |
188
+ | `all_items_less_farm_produce_and_energy` | `double` | Source column from the original resource. | `` |
189
+ | `core_index_all_items_less_farm_produce_and_energy` | `double` | Source column from the original resource. | `` |
190
+ | `imported_food` | `double` | Source column from the original resource. | `` |
191
+ | `imported_food_2` | `double` | Source column from the original resource. | `` |
192
+ | `food` | `double` | Source column from the original resource. | `` |
193
+ | `food_2` | `double` | Source column from the original resource. | `` |
194
+ | `farm_produce` | `double` | Source column from the original resource. | `` |
195
+ | `energy` | `double` | Source column from the original resource. | `` |
196
+ | `services` | `double` | Source column from the original resource. | `` |
197
+ | `goods` | `double` | Source column from the original resource. | `` |
198
+ | `food_non_alcoholic_bev` | `double` | Source column from the original resource. | `` |
199
+ | `food_and_non_alcoholic_beverages` | `double` | Source column from the original resource. | `` |
200
+ | `alcoholic_beverage_tobacco_and_kola` | `double` | Source column from the original resource. | `` |
201
+ | `alcoholic_beverages_tobacco_and_narcotics` | `double` | Source column from the original resource. | `` |
202
+ | `clothing_and_footwear` | `double` | Source column from the original resource. | `` |
203
+ | `clothing_and_footwear_2` | `double` | Source column from the original resource. | `` |
204
+ | `housing_water_electricity_gas_and_other_fuel` | `double` | Source column from the original resource. | `` |
205
+ | `housing_water_electricity_gas_and_other_fuels` | `double` | Source column from the original resource. | `` |
206
+ | `furnishings_household_equipment_maintenance` | `double` | Source column from the original resource. | `` |
207
+ | `furnishings_household_equipment_and_routine_household_ma` | `double` | Source column from the original resource. | `` |
208
+ | `health` | `double` | Source column from the original resource. | `` |
209
+ | `health_2` | `double` | Source column from the original resource. | `` |
210
+ | `transport` | `double` | Source column from the original resource. | `` |
211
+ | `transport_2` | `double` | Source column from the original resource. | `` |
212
+ | `communication` | `double` | Source column from the original resource. | `` |
213
+ | `information_and_communication` | `double` | Source column from the original resource. | `` |
214
+ | `recreation_culture` | `double` | Source column from the original resource. | `` |
215
+ | `recreation_sport_and_culture` | `double` | Source column from the original resource. | `` |
216
+ | `education` | `double` | Source column from the original resource. | `` |
217
+ | `education_services` | `double` | Source column from the original resource. | `` |
218
+ | `restaurant_hotels` | `double` | Source column from the original resource. | `` |
219
+ | `restaurants_and_accomodation_services` | `double` | Source column from the original resource. | `` |
220
+ | `insurance_and_financial_services` | `double` | Source column from the original resource. | `` |
221
+ | `miscellaneous_goods_services` | `double` | Source column from the original resource. | `` |
222
+ | `personal_care_social_protection_and_miscellaneous_goods` | `double` | Source column from the original resource. | `` |
223
+ | `month_on` | `double` | Source column from the original resource. | `` |
224
+ | `month_on_2` | `double` | Source column from the original resource. | `` |
225
+ | `year_on` | `double` | Source column from the original resource. | `` |
226
+ | `year_on_2` | `double` | Source column from the original resource. | `` |
227
+ | `d_12_month_average` | `double` | Source column from the original resource. | `` |
228
+ | `d_111_1857` | `double` | Source column from the original resource. | `` |
229
+ | `d_111_0659` | `double` | Source column from the original resource. | `` |
230
+ | `d_111_2949` | `double` | Source column from the original resource. | `` |
231
+ | `d_112_6677` | `double` | Source column from the original resource. | `` |
232
+ | `d_111_1456` | `double` | Source column from the original resource. | `` |
233
+ | `d_111_3583` | `double` | Source column from the original resource. | `` |
234
+ | `d_108_8475` | `double` | Source column from the original resource. | `` |
235
+ | `d_110_6382` | `double` | Source column from the original resource. | `` |
236
+ | `d_111_4726` | `double` | Source column from the original resource. | `` |
237
+ | `d_111_563254` | `double` | Source column from the original resource. | `` |
238
+ | `d_115_610117` | `double` | Source column from the original resource. | `` |
239
+ | `d_113_577006` | `double` | Source column from the original resource. | `` |
240
+ | `d_107_09951` | `double` | Source column from the original resource. | `` |
241
+ | `d_112_3353` | `double` | Source column from the original resource. | `` |
242
+ | `d_110_265294` | `double` | Source column from the original resource. | `` |
243
+ | `d_114_115116` | `double` | Source column from the original resource. | `` |
244
+ | `d_107_251684` | `double` | Source column from the original resource. | `` |
245
+ | `d_106_967382` | `double` | Source column from the original resource. | `` |
246
+ | `d_105_019004` | `double` | Source column from the original resource. | `` |
247
+ | `d_114_504004` | `double` | Source column from the original resource. | `` |
248
+ | `d_104_834431` | `double` | Source column from the original resource. | `` |
249
+ | `d_112_120888` | `double` | Source column from the original resource. | `` |
250
+ | `d_3_1471831870236997` | `double` | Source column from the original resource. | `` |
251
+ | `d_29_450518009532345` | `double` | Source column from the original resource. | `` |
252
+ | `d_35_2544420471057` | `double` | Source column from the original resource. | `` |
253
+ | `d_109_4697` | `double` | Source column from the original resource. | `` |
254
+ | `d_109_7349` | `double` | Source column from the original resource. | `` |
255
+ | `d_109_783` | `double` | Source column from the original resource. | `` |
256
+ | `d_109_253` | `double` | Source column from the original resource. | `` |
257
+ | `d_108_6858` | `double` | Source column from the original resource. | `` |
258
+ | `d_108_8997` | `double` | Source column from the original resource. | `` |
259
+ | `d_109_107` | `double` | Source column from the original resource. | `` |
260
+ | `d_109_7192` | `double` | Source column from the original resource. | `` |
261
+ | `d_109_3588` | `double` | Source column from the original resource. | `` |
262
+ | `d_108_7702` | `double` | Source column from the original resource. | `` |
263
+ | `d_113_0705` | `double` | Source column from the original resource. | `` |
264
+ | `d_111_0863` | `double` | Source column from the original resource. | `` |
265
+ | `d_110_7025` | `double` | Source column from the original resource. | `` |
266
+ | `d_109_7418` | `double` | Source column from the original resource. | `` |
267
+ | `d_108_288` | `double` | Source column from the original resource. | `` |
268
+ | `d_108_814` | `double` | Source column from the original resource. | `` |
269
+ | `d_108_4919` | `double` | Source column from the original resource. | `` |
270
+ | `d_106_324` | `double` | Source column from the original resource. | `` |
271
+ | `d_104_228` | `double` | Source column from the original resource. | `` |
272
+ | `d_113_228` | `double` | Source column from the original resource. | `` |
273
+ | `d_104_169` | `double` | Source column from the original resource. | `` |
274
+ | `d_111_8739` | `double` | Source column from the original resource. | `` |
275
+ | `d_3_1803934501831037` | `double` | Source column from the original resource. | `` |
276
+ | `d_25_037360107444258` | `double` | Source column from the original resource. | `` |
277
+ | `d_30_7876601999111` | `double` | Source column from the original resource. | `` |
278
+ | `state` | `string` | Source column from the original resource. | `` |
279
+ | `food_3` | `double` | Source column from the original resource. | `` |
280
+ | `all_items_3` | `double` | Source column from the original resource. | `` |
281
+ | `food_4` | `double` | Source column from the original resource. | `` |
282
+ | `all_items_4` | `double` | Source column from the original resource. | `` |
283
+ | `food_5` | `double` | Source column from the original resource. | `` |
284
+ | `all_items_5` | `double` | Source column from the original resource. | `` |
285
 
286
  ## Usage
287
 
 
293
  print(df.head())
294
  ```
295
 
296
+ ### Inspect Columns
297
 
298
  ```python
299
+ print(df.info())
300
+ print(df.head())
301
  ```
302
 
303
+ ### Filter By Geography
304
 
305
  ```python
306
+ if "country_iso3" in df.columns:
307
+ sample = df[df["country_iso3"] == "NGA"]
 
308
  ```
309
 
310
+ ### Time-Series Pattern
311
+
312
+ ```python
313
+ if "value" in df.columns and "year" in df.columns:
314
+ trend = df.sort_values("year")
315
+ ```
316
+
317
+ ### Pivot For Analysis
318
+
319
+ ```python
320
+ if {"indicator_id", "year", "value"}.issubset(df.columns):
321
+ matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
322
+ print(matrix.tail())
323
+ ```
324
+
325
+ ## Data Quality Notes
326
+
327
+ - Canonical time field: `year`.
328
+ - Missing values are preserved rather than silently imputed.
329
+ - Column names are standardized for machine use; source meanings are preserved where known.
330
+ - Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.
331
+
332
+ ## Source And Provenance
333
+
334
+ - **Source:** [National Bureau of Statistics, Nigeria](https://microdata.nigerianstat.gov.ng/index.php/catalog/154/related-materials)
335
+ - **Publisher:** National Bureau of Statistics, Nigeria
336
+ - **Portal:** [https://microdata.nigerianstat.gov.ng](https://microdata.nigerianstat.gov.ng)
337
+ - **Resource:** [June 2026 CPI Report](https://microdata.nigerianstat.gov.ng/index.php/catalog/154/download/1431)
338
+ - **License:** other-open
339
+ - **Retrieved/generated:** `2026-07-19T04:16:04Z`
340
+ - **Hugging Face repo:** [electricsheepafrica/africa-nigeria-consumer-price-index-and-inflation-1b50c949](https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-consumer-price-index-and-inflation-1b50c949)
341
+
342
+ ## Transformations Applied
343
+
344
+ - Converted the source table to Parquet for efficient analytics and ML workflows.
345
+ - Added or preserved source provenance columns where available.
346
+ - Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
347
+ - Preserved source-reported values without analytical imputation.
348
+
349
+ ## Suggested Analyses
350
+
351
+ - Build time-series dashboards
352
+ - Compare economic indicators
353
+ - Join with population or sector data
354
+ - Build time-series views and period-over-period comparisons
355
+ - Check missingness before modeling
356
+ - Use `country_iso3` as the safest geography join key when present
357
+
358
  ## Citation
359
 
360
  ```bibtex
361
  @misc{electric_sheep_africa_africa_nigeria_consumer_price_index_and_inflation_1b50c949_2026,
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+ title = {Consumer Price Index and Inflation | Africa (National Bureau of Statistics, Nigeria)},
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  author = {National Bureau of Statistics, Nigeria},
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  year = {2026},
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  url = {https://microdata.nigerianstat.gov.ng/index.php/catalog/154/related-materials},
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+ publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa},
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  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-consumer-price-index-and-inflation-1b50c949}}
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  }
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  ```
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  ## License
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+ Released under other-open.
 
 
 
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+ Original data is published by National Bureau of Statistics, Nigeria. Electric Sheep Africa
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+ engineering standardizes the data for discovery, loading, and analysis on
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+ Hugging Face. Cite both the original source and this ML-ready dataset when used.
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+ ## About Electric Sheep Africa
 
 
 
 
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+ Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
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  ---
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+ Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://microdata.nigerianstat.gov.ng/index.php/catalog/154/related-materials