TidyTuesday Week 25: Measles cases across the world

This week we are exploring measles and rubella cases across the world. This data was downloaded from the World Health Organisation Provisional monthly measles and rubella data on 2025-06-12.

1. Python code

import pandas as pd
import plotly.express as px
import geopandas as gpd

cases_year = pd.read_csv("~/Documents/Coding/Website/data_visualisations/TidyTuesday/2025/Data/cases_year.csv")


europe = cases_year[cases_year["region"] == "EURO"]
# print(europe["country"].unique())
#I don;t agree that the stans are part of Europe so I will remove thme
stans = ["Azerbaijan", "Kazakhstan", "Kyrgyzstan", "Russian Federation", "Tajikistan", "Turkmenistan", "Uzbekistan"]
europe = europe[~europe['country'].isin(stans)]


min_inc = europe['measles_incidence_rate_per_1000000_total_population'].min()
max_inc = europe['measles_incidence_rate_per_1000000_total_population'].max()

fig = px.choropleth(
  europe,
  locations="country",     # column with country names
  locationmode="country names",
  color="measles_incidence_rate_per_1000000_total_population",       # column with measles incidence
  animation_frame="year",
  range_color=[min_inc, max_inc],
  color_continuous_scale="Reds",
  title="Measles Incidence in Europe over Time",
  scope="europe",
  hover_name="country",
    hover_data={"measles_incidence_rate_per_1000000_total_population": True},
    labels={"measles_incidence_rate_per_1000000_total_population": "Measles Incidence (per 1,000,000)"}
)


fig.show()