European Regions NUTS2: GDP per Capita

Map visualization of GDP per capita by region

Keywords

GDP per capita

Summary

The chart displays regional GDP per capita adjusted for purchasing power standards (PPS) at the NUTS 2 level, highlighting economic disparities across European regions.

# Libraries
# ======================================
import eurostat
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.patches as mpatches

# EUROSTAT Data
# ======================================
# Get Data from Eurostat
dataset = eurostat.get_data_df('TGS00005')
df = pd.DataFrame(dataset)

# Select and rename columns
df = df[['geo\TIME_PERIOD', '2019']]
df = df.rename(columns={'geo\\TIME_PERIOD': 'geo'})

# GEOPANDAS Data
# ======================================
# Read GEOJSON file
geojson_url = "https://gisco-services.ec.europa.eu/distribution/v2/nuts/geojson/NUTS_RG_60M_2021_4326_LEVL_2.geojson"
gdf = gpd.read_file(geojson_url)

# Merge dataframes
gdf = gdf.merge(df, left_on='NUTS_ID', right_on='geo')

# Filter french islands
gdf = gdf[~gdf['NUTS_ID'].str.startswith('FRY')]
gdf = gdf[~gdf['NUTS_ID'].str.startswith('ES70')]
gdf = gdf[~gdf['NUTS_ID'].str.startswith('PT20')]
gdf = gdf[~gdf['NUTS_ID'].str.startswith('PT30')]

# STATIC Map
# ======================================
# Font Style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Open Sans'], 'font.size': 10})

# Create figure
fig, ax = plt.subplots(1, 1, figsize=(12, 12))

# Define palette range
ranges = [7000, 16000, 22000, 27000, 32000, 38000, 80000]
colors = ['#d66d35', '#e5a53e', '#efcd94', '#d5dcef', '#88a0d0', '#4b6cb0']  # Colores específicos para cada rango
labels = [
    '≥ 5 700 to 12 800',
    '≥ 12 800 to 17 600',
    '≥ 17 600 to 22 000',
    '≥ 22 000 to 27 000',
    '≥ 27 000 to 32 200',
    '≥ 32 200 to 70 400'
]

# Plot Map
gdf.plot(
    column='2019', 
    ax=ax, 
    legend=False,
    cmap=mcolors.ListedColormap(colors), 
    linewidth=0.15, 
    edgecolor='black', 
    norm=mcolors.BoundaryNorm(boundaries=ranges, ncolors=len(colors))
)

# Custom Legend
patches = [mpatches.Patch(color=color, label=label) for color, label in zip(colors, labels)]
legend = ax.legend(
    handles=patches, 
    loc='upper left', 
    fontsize=8, 
    title="GDP per capita", 
    title_fontsize=9, 
    labelspacing=0.5, 
    borderpad=0.3
)
legend.get_title().set_fontweight('bold')
plt.subplots_adjust(right=0.8)

# Configuration
plt.text(0, 1.05, 'Regional GDP per capita', fontsize=13, fontweight='bold', ha='left', transform=plt.gca().transAxes)
plt.text(0, 1.02, 'By NUTS 2 Region (Europe)', fontsize=9, color='#262626', ha='left', transform=plt.gca().transAxes)
ax.set_axis_off()

 # Add Year label
formatted_date = 2019 
ax.text(1, 1.06, f'{formatted_date}',
    transform=ax.transAxes,
    fontsize=22, ha='right', va='top',
    fontweight='bold', color='#D3D3D3')

# Add Data Source
plt.text(0, -0.1, 'Data Source: Eurostat (2024), GDP and main components (output, expenditure and income)', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight = 'bold',
    color='gray')

# Add Notes
plt.text(0, -0.12, 'Notes: Gross domestic product (GDP) at current market prices by NUTS 2 region', 
    transform=plt.gca().transAxes,
    fontsize=8, 
    color='gray')

# Add author
plt.text(1, -0.1, '@guillemmaya.com', 
    transform=plt.gca().transAxes, 
    fontsize=8, 
    color='#212121', 
    ha='right')

# Save it!
plt.savefig('C:/Users/guill/Downloads/FIG_EUROSTAT_Map_GDP.png', format='png', bbox_inches='tight')

# Plot it!
plt.show()