social-ES 1.0.1

Python library to ingest, clean, and transform up-to-date Spanish demographic, socioeconomic, and other social-related datasets from the National Statistics Institute (INE) and other sources.

social_ES

A Python library to ingest, clean, and transform up-to-date Spanish demographic, socioeconomic, and other social-related datasets from multiple data source entities. At this moment, only INE (Instituto Nacional de Estadística — Spanish National Statistics Institute) is supported.

🎯 Overview

social_ES automates the discovery, download, and cleaning of official Spanish statistics from INE's data portal. Instead of manually navigating INE's website and wrangling raw CSV/TSV exports, each function in the library scrapes the relevant dataset, normalizes column names and geographic codes (autonomous community, province, municipality, district, census section), and returns a ready-to-use pandas.DataFrame.

Downloaded and processed data is cached locally in your working directory, so subsequent calls reuse the cached files instead of re-downloading from INE.

Key Features

• 📥 Automated INE Scraping: Discovers and downloads the latest published data directly from INE's dissemination portal, no manual exports needed
• 🧹 Cleaning & Normalization: Consistent column naming, numeric parsing (Spanish decimal format), and geographic code harmonization
• 🗂️ Local Caching: Results are persisted as .tsv/.parquet files under your working directory to avoid redundant downloads
• 🌍 Multi-level Geography: Data available at autonomous community, province, municipality, district, or census-section level depending on the dataset
• 🔎 Filtering: Most functions support filtering by municipality_code and years
• 🔗 hypercadaster_ES Integration: EssentialCharacteristicsOfPopulationAndHouseholds links Census 2021 indicators to building-level data exported from hypercadaster_ES

🚀 Installation

Install from PyPI:

pip install social_ES

Or, for development from source:

git clone https://github.com/BeeGroup-cimne/social_ES.git
cd social_ES
pip install .

📖 Quick Start

from social_ES import INE

# Define a working directory where downloaded/processed data will be cached
wd = "/path/to/your/data"

# Household income distribution at census-section level
atlas_df = INE.HouseholdIncomeDistributionAtlas(wd=wd)

# Population census, filtered to a specific municipality and years
population_df = INE.PopulationCensus(wd=wd, municipality_code="08900", years=[2021, 2022])

# Education and employment census (relative shares)
education_df = INE.EducationAndEmploymentCensus(wd=wd, mode="relative")

# Consumer Price Index by category
cpi_df = INE.ConsumerPriceIndex(wd=wd)

See examples/get_ine.ipynb for a full worked example, including the output format of each function.

📊 Available Datasets

Function	Description	Geographic level	Key arguments
RelationAutonomousCommunityAndProvince()	Static lookup table mapping autonomous community codes/names to province codes/names	Province	—
MunicipalityNamesToMunicipalityCodes()	Official INE dictionary of municipality names and codes	Municipality	—
HouseholdIncomeDistributionAtlas(wd, municipality_code, years)	Household income distribution (Atlas de Distribución de Renta de los Hogares)	Census section	municipality_code, years
PopulationCensus(wd, municipality_code, years)	Population census counts	Province / municipality	municipality_code, years
EducationAndEmploymentCensus(wd, municipality_code, years, mode)	Education level and employment status	Province / municipality	mode="relative"|"absolute"
HouseholdsPriceIndex(wd, municipality_code, years)	Housing price index (whole, new, and second-hand market)	Autonomous community, quarterly	municipality_code, years
HouseholdsRentalPriceIndex(wd, municipality_code, years)	Housing rental price index	Municipality / district	municipality_code, years
AggregatedElectricityConsumption(wd, municipality_code, years)	Aggregated electricity consumption percentiles (2021)	Municipality / district	municipality_code, years
ConsumerPriceIndex(wd, years)	Consumer Price Index (CPI) broken down by COICOP category	National	years
EssentialCharacteristicsOfPopulationAndHouseholds(wd, hypercadaster_ES_input_pkl_file)	Census 2021 population and household characteristics, linked to building-level data	Building (via hypercadaster_ES)	hypercadaster_ES_input_pkl_file

Most functions accept municipality_code as either a single code (str) or a list of codes, and years as a list of years to filter to. When omitted, the full dataset is returned.

💾 Caching Behavior

On first call, each function downloads the relevant data from INE and stores a processed copy under:

{wd}/INE/{FunctionName}/

Subsequent calls with the same wd read from this cache instead of hitting INE again. To force a refresh, delete the corresponding cache file/folder.

🎯 Key Applications

• Urban & Regional Analysis: Combine income, population, and housing indicators at municipality or census-section level
• Building-level Socioeconomic Profiling: Join Census 2021 household characteristics to cadastral building data via hypercadaster_ES
• Energy & Social Studies: Cross-reference aggregated electricity consumption with income and demographic indicators

📄 License

This project is licensed under the EUPL v1.2. See the license for details.

Authors

• Jose Manuel Broto - [e-mail]
• Gerard Mor - [e-mail]

Copyright (c) 2025 Jose Manuel Broto, Gerard Mor