TidyTuesday Week 31: British Library Funding

This week we’re exploring Income Inequality Before and After Taxes, as processed and visualized by Joe Hasell at Our World in Data

::: #### 1. Python code

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


income_inequality_processed = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-05/income_inequality_processed.csv')
income_inequality_raw = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-05/income_inequality_raw.csv')

countries = ["United States", "Germany", "France", "Italy", "Spain", "UK"]

df = income_inequality_processed[income_inequality_processed["Entity"].isin(countries)]

# Only that after 20008

df = df[df["Year"] >= 2008]

heatmap_data_b4_tax = df.pivot(index="Entity", columns="Year", values="gini_mi_eq")
heatmap_data_aft_tax = df.pivot(index="Entity", columns="Year", values="gini_dhi_eq")


fig, axes = plt.subplots(1, 2, figsize=(18, 8), sharey=True)

sns.heatmap(heatmap_data_b4_tax, 
            cmap="YlOrRd", 
            ax=axes[0])
axes[0].set_title("Before-Tax Income Inequality")
axes[0].set_xlabel("Year")
axes[0].set_ylabel("Country")

sns.heatmap(heatmap_data_aft_tax, 
            cmap="YlOrRd", 
            ax=axes[1])
axes[1].set_title("After-Tax Income Inequality")
axes[1].set_xlabel("Year")
axes[1].set_ylabel("")
plt.show()