标签：#datavisualization

Here is a question I could not answer from the headlines: which European countries are actually losing people the fastest, in absolute terms or per capita? Those are two different questions, and they give two different answers. So I pulled the open data and ran the numbers. The headline figure Across the 19 European countries in the 2024 dataset, 17 recorded a net loss of native-born residents . Only two were net positive. So the "brain drain" story is not a handful of outliers, it is the default state of the continent. But the interesting part is who tops the ranking, because it depends entirely on how you measure. Load the data yourself The dataset is public on GitHub (CC BY 4.0). Every number below is reproducible with a few lines of pandas. No download, no API key, it reads the raw CSV straight from the repo: import pandas as pd url = ( " https://raw.githubusercontent.com/DatapulseResearch/ " " brain-drain-eu/main/data/net_migration_native_born_2024.csv " ) df = pd . read_csv ( url ) print ( df . shape ) # (19, 3) print ( df . columns . tolist ()) # ['country', 'net_migration', 'per_1000_residents'] # How many countries lost native-born residents? losers = ( df [ " net_migration " ] < 0 ). sum () print ( f " { losers } of { len ( df ) } countries had a net loss " ) # 17 of 19 net_migration is the raw count for 2024 (negative means a net loss of native-born residents). per_1000_residents is the same flow normalized by population size. The absolute ranking: Germany runs away with it Sort by the raw count and one country dominates: worst_absolute = df . sort_values ( " net_migration " ). head ( 5 ) print ( worst_absolute [[ " country " , " net_migration " ]]) country net _ migration 0 Germany - 91067 ... Germany loses -91,067 native-born residents, far more than anyone else in absolute terms. If you stop reading here, the story writes itself: "Germany, Europe's biggest brain drain." Plenty of coverage did exactly that. The counterintuitive finding: the ranking inve