Total MEPs: 0
Overview of MEPs by country and political group. Use the filters above to refine the data.
Browse all Members of the European Parliament (current and historic). Click "Show More" to load additional entries. To refresh or build the MEP dataset (API + historic + group normalization), use Data → Build MEP Dataset.
| ID | Name | Country | Role/Affiliation | Status | Speeches | Actions |
|---|
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| Date | Title | Number of Speeches | Details |
|---|
Select or search a macro topic and click Load to show the top 10 speakers (MEP, Country, Group, Speeches).
| MEP | Country | Group | Speeches |
|---|---|---|---|
| Click Load to show the top 10 speakers for the topic (or for all topics if left empty). | |||
The downloaded file is a SQLite database. You can open it with DB Browser for SQLite, Datasette, DuckDB, or the `sqlite3` CLI.
Quick local check:
sqlite3 /path/to/ep_data_snapshot.db ".tables"
sqlite3 /path/to/ep_data_snapshot.db "SELECT COUNT(*) FROM individual_speeches;"
sqlite3 /path/to/ep_data_snapshot.db "SELECT activity_date, speaker_name, macro_topic FROM individual_speeches LIMIT 20;"
If you want CSVs, generate them locally from this file instead of exporting the entire dataset through the website runtime.
You are working with a local SQLite database file at /path/to/ep_data_snapshot.db. Use sqlite3 or a Python/Node SQLite client to inspect the schema first, identify the main tables, and summarize the key columns. Then answer my questions by querying the database directly instead of guessing. Rules: - Start by listing tables and row counts. - Inspect the schema for individual_speeches, sittings, and meps. - Prefer SQL queries over loading the entire database into memory. - When exporting large results, write them to files in batches. - Show me the SQL you used and briefly explain the result. My task is: [replace this with your actual analysis request]