electricsheepafrica/africa-mozambique-inquerito-sobre-o-impacto-da-covid-19-2020-473a3596

Inquerito Sobre O Impacto Da Covid 19 2020 | Africa (INSTITUTO NACIONAL DE ESTATISTICA)

106 rows - 1 Africa country/area - 2021 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 106 rows from INSTITUTO NACIONAL DE ESTATISTICA, covering Inquerito Sobre O Impacto Da Covid 19 2020. 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	106
Countries/areas	1
First period	2021
Last period	2021
Indicators	0
Columns	41
Source format	XLS

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MOZ	106	2021	2021	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-77-569: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.	2021
d_1	string	Source column from the original resource.	2.
provincia	string	Source column from the original resource.	DISTRITO_____________________________________________________________...
source_period_start_year	int64	Start year inferred from source metadata.	2021
source_period_end_year	int64	End year inferred from source metadata.	2021
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2021
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	INSTITUTO NACIONAL DE ESTATISTICA
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	INQUERITO SOBRE O IMPACTO DA COVID-19 2020
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	QUESTIONÁRIO SOBRE O IMPACTO DA COVID-19 NOS AGREGADOS FAMILIARES COV...
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	MOZ-INE.ITCOVID19AF20.V1.C341.1
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	moz-ine-nada-77-569
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://mozdata.ine.gov.mz/index.php/catalog/77/download/569
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
column_1	string	Source column from the original resource.	``
qual_e_a_sua_relacao_de_parentesco_com_o_chefe_de_agrega	string	Source column from the original resource.	``
qual_e_o_nivel_mais_alto_de_ensino_que_nome_concluiu	string	Source column from the original resource.	``
nos_ultimos_7_dias_o_seu_agregado_familiar_precisou_de_c	string	Source column from the original resource.	``
ha_criancas_entre_os_6_e_18_anos_de_idade	string	Source column from the original resource.	``
qual_e_a_principal_atividade_da_empresa_ou_organizacao_e	string	Source column from the original resource.	``
qual_e_a_principal_actividade_da_empresa_ou_organizacao	string	Source column from the original resource.	``
qual_e_a_principal_actividade_da_empresa_ou_organizacao_2	string	Source column from the original resource.	``
quais_sao_os_principais_motivos_pelos_quais_o_nome_nao_c	string	Source column from the original resource.	``
nos_ultimos_12_meses_quais_foram_as_fontes_de_subsistenc	string	Source column from the original resource.	``
column_2	string	Source column from the original resource.	``
p1	string	Source column from the original resource.	``
p2	string	Source column from the original resource.	``
p3	string	Source column from the original resource.	``
p4	string	Source column from the original resource.	``
p5	string	Source column from the original resource.	``
p6	string	Source column from the original resource.	``
p7	string	Source column from the original resource.	``
p8	string	Source column from the original resource.	``
column_4	string	Source column from the original resource.	``
observacoes_do_a_inquiridor_a_para_ser_preenchido_imedia	string	Source column from the original resource.	``
id_individual	double	Source column from the original resource.	``
por_que_o_nome_saiu_do_agregado_familiar	string	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mozambique-inquerito-sobre-o-impacto-da-covid-19-2020-473a3596")
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: INSTITUTO NACIONAL DE ESTATISTICA
• Publisher: INSTITUTO NACIONAL DE ESTATISTICA
• Portal: https://mozdata.ine.gov.mz
• Resource: QUESTIONÁRIO SOBRE O IMPACTO DA COVID-19 NOS AGREGADOS FAMILIARES COVID-19
• License: other-open
• Retrieved/generated: 2026-08-07T22:11:00Z
• Hugging Face repo: electricsheepafrica/africa-mozambique-inquerito-sobre-o-impacto-da-covid-19-2020-473a3596

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_sobre_o_impacto_da_covid_19_2020_473a3596_2021,
  title        = {Inquerito Sobre O Impacto Da Covid 19 2020 | Africa (INSTITUTO NACIONAL DE ESTATISTICA)},
  author       = {INSTITUTO NACIONAL DE ESTATISTICA},
  year         = {2021},
  url          = {https://mozdata.ine.gov.mz/index.php/catalog/77/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mozambique-inquerito-sobre-o-impacto-da-covid-19-2020-473a3596}}
}

License

Released under other-open.

Original data is published by INSTITUTO NACIONAL DE ESTATISTICA. 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/77/related-materials