electricsheepafrica/africa-mozambique-inquerito-orcamento-familiar-2014-2015-22164a05

Inquerito Orcamento Familiar 2014 2015 | Africa (INE)

510 rows - 1 Africa country/area - 2012 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 510 rows from INE, covering Inquerito Orcamento Familiar 2014 2015. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Transport datasets help analysts examine mobility, infrastructure, passenger movement, logistics, and access to services.

Source-provided context: Document, Questionnaire [doc/qst]

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	510
Countries/areas	1
First period	2012
Last period	2012
Indicators	0
Columns	92
Source format	XLS

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MOZ	510	2012	2012	Mozambique

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.	moz-ine-nada-84-627:capa:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	MOZ
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	Mozambique
source_sheet	string	Source column from the original resource.	CAPA
year	int64	Observation year.	2012
column_1	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.	``
mes_4	string	Source column from the original resource.	ANO .....................................
source_period_start_year	int64	Start year inferred from source metadata.	2012
source_period_end_year	int64	End year inferred from source metadata.	2012
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2012
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	INE
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Inquerito Orcamento Familiar 2014-2015
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Questionário do Agregado Familiar
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	MOZINEIOF2014-2015VERSAO3321
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	moz-ine-nada-84-627
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://mozdata.ine.gov.mz/index.php/catalog/84/download/627
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	other-open
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-07T22:10:37Z
d_1	string	Source column from the original resource.	``
d_3	string	Source column from the original resource.	``
column_7	double	Source column from the original resource.	``
column_9	double	Source column from the original resource.	``
column_10	string	Source column from the original resource.	``
column_12	string	Source column from the original resource.	``
column_13	string	Source column from the original resource.	``
d_6	double	Source column from the original resource.	``
column_17	string	Source column from the original resource.	``
column_8	string	Source column from the original resource.	``
column_11	string	Source column from the original resource.	``
column_16	string	Source column from the original resource.	``
s	string	Source column from the original resource.	``
n	string	Source column from the original resource.	``
ns	double	Source column from the original resource.	``
s_2	double	Source column from the original resource.	``
n_2	double	Source column from the original resource.	``
ns_2	double	Source column from the original resource.	``
s_3	double	Source column from the original resource.	``
n_3	double	Source column from the original resource.	``
ns_3	double	Source column from the original resource.	``
sim	string	Source column from the original resource.	``
nao	double	Source column from the original resource.	``
column_22	string	Source column from the original resource.	``
sim_2	string	Source column from the original resource.	``
nao_2	double	Source column from the original resource.	``
column_28	string	Source column from the original resource.	``
fm	double	Source column from the original resource.	``
fl	double	Source column from the original resource.	``
fp	double	Source column from the original resource.	``
pc	double	Source column from the original resource.	``
su	double	Source column from the original resource.	``
out	double	Source column from the original resource.	``
nh	double	Source column from the original resource.	``
column_14	string	Source column from the original resource.	``
classe	string	Source column from the original resource.	``
d_2	string	Source column from the original resource.	``
column_21	string	Source column from the original resource.	``
column_27	string	Source column from the original resource.	``
a	string	Source column from the original resource.	``
b	string	Source column from the original resource.	``
c	string	Source column from the original resource.	``
d	string	Source column from the original resource.	``
e	string	Source column from the original resource.	``
f	string	Source column from the original resource.	``
column_15	string	Source column from the original resource.	``
g	string	Source column from the original resource.	``
h	string	Source column from the original resource.	``
i	string	Source column from the original resource.	``
j	string	Source column from the original resource.	``
no	string	Source column from the original resource.	``
perguntas_e_filtros	string	Source column from the original resource.	``
codigo_das_categorias	string	Source column from the original resource.	``
passe_a	string	Source column from the original resource.	``
cod	double	Source column from the original resource.	``
produtos	string	Source column from the original resource.	``
d_7	double	Source column from the original resource.	``
d_8	string	Source column from the original resource.	``
p2_nos_ultimos_3_meses_agregado_familiar_comprou_ou_pago	string	Source column from the original resource.	``
codigo	double	Source column from the original resource.	``
tipo_de_bens	string	Source column from the original resource.	``
possui	double	Source column from the original resource.	``
d_12_meses	double	Source column from the original resource.	``
em_mts	double	Source column from the original resource.	``
d_1_considera_que_a_situacao_financeira_do_seu_agregado	string	Source column from the original resource.	``
melhorou_muito	string	Source column from the original resource.	``
d_9_preve_que_a_situacao_financeira_do_seu_agregado_fami	string	Source column from the original resource.	``
melhorar_muito	string	Source column from the original resource.	``
observacoes_do_a_inquiridor_a_para_ser_preenchido_imedia	string	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mozambique-inquerito-orcamento-familiar-2014-2015-22164a05")
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"] == "MOZ"]

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: INE
• Publisher: INE
• Portal: https://mozdata.ine.gov.mz
• Resource: Questionário do Agregado Familiar
• License: other-open
• Retrieved/generated: 2026-08-07T22:11:17Z
• Hugging Face repo: electricsheepafrica/africa-mozambique-inquerito-orcamento-familiar-2014-2015-22164a05

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

• Track mobility over time
• Compare routes or geographies
• Join with economic and population data
• 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_mozambique_inquerito_orcamento_familiar_2014_2015_22164a05_2012,
  title        = {Inquerito Orcamento Familiar 2014 2015 | Africa (INE)},
  author       = {INE},
  year         = {2012},
  url          = {https://mozdata.ine.gov.mz/index.php/catalog/84/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mozambique-inquerito-orcamento-familiar-2014-2015-22164a05}}
}

License

Released under other-open.

Original data is published by INE. 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 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://mozdata.ine.gov.mz/index.php/catalog/84/related-materials