TidyTuesday Week 32: Extreme Weather Attribution Studies

This week we’re exploring extreme weather attribution studies. The dataset comes from Carbon Brief’s article Mapped: How climate change affects extreme weather around the world

::: #### 1. Python code

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import folium
import base64
from io import BytesIO


attribution_studies = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-12/attribution_studies.csv')
attribution_studies_raw = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-12/attribution_studies_raw.csv')


classification_colors = {
    "More severe or more likely to occur": "#e41a1c",
    "Decrease, less severe or less likely to occur": "#377eb8",
    "No discernible human influence": "#4daf4a",
}

df = attribution_studies_raw[attribution_studies_raw["iso_country_code"].notnull()]
df = df[df["classification"] != "Insufficient data/inconclusive"]
df = df[df["cb_region"].str.contains("Heisphere")==False]


df["classification"]


df = df.explode("iso_country_code")


df["iso_country_code"] = df["iso_country_code"].str.strip()

counts = df.groupby(['cb_region', 'classification']).size().reset_index(name='count')

pivot_counts = counts.pivot(index='cb_region',
                             columns='classification',
                             values='count').fillna(0)


pivot_percent = pivot_counts.div(pivot_counts.sum(axis=1), axis=0) * 100


ax = pivot_percent.plot(
    kind='bar',
    stacked=True,
    figsize=(8, 5),
    color=[classification_colors.get(col, "#cccccc") for col in pivot_percent.columns]
)


for container in ax.containers:
    labels = [f"{v.get_height():.1f}%" if v.get_height() > 0 else "" for v in container]
    ax.bar_label(container, labels=labels, label_type='center', fontsize=8, color="white")

plt.ylabel('Percentage')
plt.xlabel("Region")
plt.title('Percentage by Region and Classification')
plt.legend(
    title='Classification',
    loc='lower right',
    bbox_to_anchor=(1.5, -1)
)

plt.show()