ISTAT Microdata Extractor – Aspetti della Vita Quotidiana (AVQ)

This project provides tools for navigating and processing the ISTAT microdata. It includes the Python class ISTATMicrodataExtractor with structured methods to explore, query, and analyze the microdata efficiently.

Available microdata:

• AVQ: Indagine sugli Aspetti della Vita Quotidiana (AVQ) delle famiglie italiane
• HBS: Indagine sulle spese delle famiglie italiane

📦 Project Structure

The central component is the ISTATMicrodataExtractor class, which offers:

• 🚀 Simplified access to the dataset structure
• 🧠 Attribute encoding utilities
• 🔎 Filtering and pairing logic for household members
• 📊 Joint and conditional distribution tools
• 📁 Integration-ready design for larger analytical pipelines

📚 Dataset Overview

Aspetti della Vita Quotidiana (AVQ) is an annual survey by ISTAT capturing detailed aspects of daily life in Italian households. It includes information on:

• Demographics
• Education and employment
• Health and access to services
• Household composition and living conditions
• Digital device usage and internet access
• Family dynamics and caregiving
• Purchase habits

🧩 Key Features of ISTATMicrodataExtractor

Installing & Setup

git clone git@github.com:Clearbox-AI/ISTAT-microdata-extractor.git

pip install -r path/to/ISTAT-microdata-extractor/requirements.txt

pip install -e path/to/ISTAT-microdata-extractor

Updating version

To update your local version go to your local folder and run:

git pull origin main

pip install -e ISTAT-microdata-extractor

To setup the data, unzip the data folder you need here and provide the path to the unzipped folder to the load_data() method of your ISTATMicrodataExtractor class.

Unlike raw data, this data was processed to allow some methods of the class BIMicrodataExtractor to work smoothly.

📊 Examples

from microdata_extractor import ISTATMicrodataExtractor

# Supposing your AVQ Microdata ISTAT is stored in "AVQ_2023_IT"
mde = ISTATMicrodataExtractor(df_name="AVQ",year=2023)
mde.load_data("AVQ_2023_IT")

# Consult the available attribute categories 
mde.attribute_categories

# Filter attributes by relevant categories
_ = mde.get_attributes_by_categories("demographics","sport", "health_conditions", condition="or")

# Check encodings for categorical variables
encoding = mde.get_attribute_metadata("FREQSPO", print_output=True)

# Filter main dataset based on user-defined rules
# Tuples within the same inner list are AND-ed, tuples belonging to different inner lists are OR-ed
# The following rules express: (age>=18 AND BMI<=3)  OR  (age<18 AND BMIMIN==1)
rules = [
    [("ETAMi",">=",7),("BMI","<=",3)],  # Adults (age>=18) AND BMI==[1,2,3]
                                        # OR
    [("ETAMi","<",7),("BMIMIN","==",1)] # minors (age<18) AND BMIMIN==1
]

df_filtered = mde.filter(rules)

Check out the Examples folder for more!

Contacts

📧 info@clearbox.ai

🌐 www.clearbox.ai