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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.
ESS data is downloadable as SPSS, Stata, or CSV from the ESS website after registration. Engineers use `pyreadstat` or `pandas.read_spss()` to load the weighted microdata. Cross-national analysis requires careful handling of country-specific variables and design weights.
ESS survey data trains AI models for public opinion prediction and social attitude classification. Fine-tune text classifiers on ESS open-text responses, or build a RAG system indexed on ESS Round reports so LLMs can answer 'How has trust in national governments changed across Europe since 2002?'
# pip install pyreadstat pandas
import pyreadstat, pandas as pd
# Download ESS SPSS file from https://ess-search.nsd.no/
df, meta = pyreadstat.read_sav("ESS10.sav")
print(f"Respondents: {len(df)}, Variables: {len(df.columns)}")
# Trust in national parliament by country (0=no trust, 10=complete trust)
trust = df.groupby("cntry")["trnprl"].mean().sort_values(ascending=False)
print(trust.head(10))Official dataset source
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