electricsheepafrica/africa-tanzania-national-bureau-of-statistics-8b0cdbbd

National Bureau of Statistics | Africa (National Bureau of Statistics, Tanzania)

660 rows - 1 Africa country/area - 2019-2023 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 660 rows from National Bureau of Statistics, Tanzania, covering National Bureau of Statistics. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

Source-provided context: 16 December, 2023 High Frequency Data end November 2023.xls

How To Read This Dataset

• One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• Primary geography column: country_iso3.
• Best time column: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	660
Countries/areas	1
First period	2019
Last period	2023
Indicators	0
Columns	104
Source format	XLS

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
TZA	660	2019	2023	Tanzania

Indicators, Variables, Or Resource Contents

• This repo preserves one source tabular resource with its usable columns kept together.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	nbs-stat-293082db9a4802:cpi:0
country_iso3	string	ISO3 country or area code.	TZA
country_name	string	Country or area name.	Tanzania
source_sheet	string	Source column from the original resource.	CPI
year	double	Observation year.	2023.0
month	string	Source column from the original resource.	January
2015	double	Source column from the original resource.	78.83527721142686
2016	double	Source column from the original resource.	83.97997411848442
2017	double	Source column from the original resource.	88.3223803528915
2018	string	Source column from the original resource.	91.83567033368875
2019	double	Source column from the original resource.	94.54665032621035
2020	double	Source column from the original resource.	98.06951119106868
2021	double	Source column from the original resource.	101.53330687642342
2022	double	Source column from the original resource.	105.59163825727232
2023	double	Source column from the original resource.	110.808994248678
source_period_start_year	int64	Start year inferred from source metadata.	2023
source_period_end_year	int64	End year inferred from source metadata.	2023
source_period_label	string	Source column from the original resource.	2023
source_provider	string	Publishing organization.	National Bureau of Statistics, Tanzania
source_dataset	string	Source dataset or package title.	National Bureau of Statistics
source_resource	string	Source resource title, table name, or file name.	High Frequency Data end November 2023.xls
source_package_id	string	Source package identifier.	high-frequency-data
source_resource_id	string	Source resource identifier.	nbs-stat-293082db9a4802
source_url	string	Original source URL or download URL.	https://www.nbs.go.tz/uploads/statistics/documents/en-1705429386-High...
license_id	string	Source license identifier.	other-open
retrieved_at	string	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-07-21T20:05:22Z
nationality	string	Source column from the original resource.	``
january	double	Source column from the original resource.	``
february	double	Source column from the original resource.	``
march	double	Source column from the original resource.	``
april	double	Source column from the original resource.	``
may	double	Source column from the original resource.	``
june	double	Source column from the original resource.	``
july	double	Source column from the original resource.	``
august	double	Source column from the original resource.	``
september	double	Source column from the original resource.	``
october	double	Source column from the original resource.	``
november	string	Source column from the original resource.	``
december	string	Source column from the original resource.	``
total	double	Source column from the original resource.	``
quarter	string	Source column from the original resource.	``
manufactured	string	Source column from the original resource.	``
imported	string	Source column from the original resource.	``
exported	string	Source column from the original resource.	``
net_cement_consumption	string	Source column from the original resource.	``
type_of_source	string	Source column from the original resource.	``
saw_milling_and_planing_of_wood	double	Source column from the original resource.	``
manufacture_of_paints_varnishes_and_similar_coatings_pri	double	Source column from the original resource.	``
manufacture_of_cement_lime_and_plaster	double	Source column from the original resource.	``
manufacture_of_basic_iron_and_steel	double	Source column from the original resource.	``
manufacture_of_other_fabricated_metal_products_n_e_c	double	Source column from the original resource.	``
crop	string	Source column from the original resource.	``
january_march	string	Source column from the original resource.	``
april_june	string	Source column from the original resource.	``
july_september	string	Source column from the original resource.	``
october_december	string	Source column from the original resource.	``
d_388172268_88	double	Source column from the original resource.	``
d_3060484_15	double	Source column from the original resource.	``
d_709537_82	double	Source column from the original resource.	``
d_14197	double	Source column from the original resource.	``
d_99975_19	double	Source column from the original resource.	``
d_620_77	double	Source column from the original resource.	``
column	string	Source column from the original resource.	``
column_2	string	Source column from the original resource.	``
column_3	string	Source column from the original resource.	``
column_4	string	Source column from the original resource.	``
column_5	string	Source column from the original resource.	``
column_6	string	Source column from the original resource.	``
column_7	string	Source column from the original resource.	``
column_8	string	Source column from the original resource.	``
column_9	string	Source column from the original resource.	``
column_10	string	Source column from the original resource.	``
column_1	double	Source column from the original resource.	``
dodoma	double	Source column from the original resource.	``
arusha	double	Source column from the original resource.	``
kilimanjaro	double	Source column from the original resource.	``
tanga	double	Source column from the original resource.	``
morogoro	double	Source column from the original resource.	``
kibaha	double	Source column from the original resource.	``
dar_es_salaam	double	Source column from the original resource.	``
kilwa	double	Source column from the original resource.	``
mtwara	double	Source column from the original resource.	``
songea	double	Source column from the original resource.	``
iringa	double	Source column from the original resource.	``
mbeya	double	Source column from the original resource.	``
singida	double	Source column from the original resource.	``
tabora	double	Source column from the original resource.	``
sumbawanga	double	Source column from the original resource.	``
kigoma	double	Source column from the original resource.	``
bukoba	double	Source column from the original resource.	``
mwanza	double	Source column from the original resource.	``
musoma	double	Source column from the original resource.	``
manyara_babati	double	Source column from the original resource.	``
shinyanga	double	Source column from the original resource.	``
same	double	Source column from the original resource.	``
karume_pemba	double	Source column from the original resource.	``
mpanda	double	Source column from the original resource.	``
ilonga	double	Source column from the original resource.	``
zanzibar	double	Source column from the original resource.	``
mahenge	double	Source column from the original resource.	``
tunduru	double	Source column from the original resource.	``
lushoto	double	Source column from the original resource.	``
engarerongai	double	Source column from the original resource.	``
songwe	double	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-tanzania-national-bureau-of-statistics-8b0cdbbd")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "TZA"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• Canonical time field: year.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: National Bureau of Statistics, Tanzania
• Publisher: National Bureau of Statistics, Tanzania
• Portal: https://www.nbs.go.tz
• Resource: High Frequency Data end November 2023.xls
• License: other-open
• Retrieved/generated: 2026-07-21T20:17:09Z
• Hugging Face repo: electricsheepafrica/africa-tanzania-national-bureau-of-statistics-8b0cdbbd

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Profile the distribution of values
• Compare categories or geographies
• Join with complementary public datasets
• Build time-series views and period-over-period comparisons
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_tanzania_national_bureau_of_statistics_8b0cdbbd_2023,
  title        = {National Bureau of Statistics | Africa (National Bureau of Statistics, Tanzania)},
  author       = {National Bureau of Statistics, Tanzania},
  year         = {2023},
  url          = {https://www.nbs.go.tz/statistics/topic/high-frequency-data},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-tanzania-national-bureau-of-statistics-8b0cdbbd}}
}

License

Released under other-open.

Original data is published by National Bureau of Statistics, Tanzania. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

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

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Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://www.nbs.go.tz/statistics/topic/high-frequency-data