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Data from the ninth round of the European Social Survey covering attitudes on health, climate change, democratic values, and social trust across 30+ European countries. Used in data engineering for longitudinal survey analysis, cross-national comparison pipelines, and social science research datasets in Python.
ESS Round 9 data is downloadable as CSV or SPSS from the ESS website. Engineers load it with `pandas.read_csv()` or `pyreadstat.read_sav()`, applying design weights for nationally representative estimates. The `esspy` library simplifies ESS-specific analysis tasks.
ESS Round 9 microdata trains AI models for European public opinion analysis. The module on justice and fairness provides labeled attitude data for fine-tuning sentiment models. Build a RAG system on ESS Round 9 country reports to answer questions about European social attitudes with survey-backed evidence.
# pip install pyreadstat pandas
import pyreadstat, pandas as pd
# Download ESS Round 9 SPSS file from https://ess-search.nsd.no/
df, meta = pyreadstat.read_sav("ESS9e03_2.sav")
print(f"Respondents: {len(df)}")
# Sense of belonging by country
belong = df.groupby("cntry")["atchctr"].mean().sort_values(ascending=False)
print(belong.head(15))Official dataset source
More datasets used by Python data engineers.
Eurostat, the statistical office of the European Union, offers a comprehensive database of statistical data covering various domains such as economy, population, employment, environment and social issues.
The European Social Survey collects cross-national data on social attitudes, political engagement, values, and wellbeing through biennial surveys across 30+ European countries. Used in data engineering for longitudinal social science analysis, cross-national comparison pipelines, and building attitudinal trend dashboards in Python.
A longitudinal cross-national survey measuring social, political, moral, and religious values across European countries since 1981. Used in data engineering for social science research pipelines, cultural change analysis, and building time-series survey datasets for comparative European studies in Python.