TidyTuesday (2026) Week 6: Winter Olympics 2026

This week we’re exploring the event schedule for the 2026 Winter Olympics in Milan-Cortina, Italy. The dataset contains detailed information about all 1,866 Olympic events, including both competition and training sessions across various winter sport disciplines.

Chart A

Chart B

Timeline of Mens and Womens Medal Events

1. Python code

import pandas as pd
import numpy as np
import plotly.express as px
from datetime import datetime


schedule = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-02-10/schedule.csv')


medal_events = schedule[schedule["is_medal_event"] != False]
medal_events = medal_events[["event_description", "venue_name", "start_datetime_local", "end_datetime_local"]]

def categorize_gender(description):
    if pd.isna(description):
        return "Other"
    description_lower = description.lower()
    if "men" in description_lower and "women" not in description_lower:
        return "Male"
    elif "women" in description_lower:
        return "Female"
    elif any(word in description_lower for word in ["team", "mixed", "relay", "2-man", "2-woman", "4-man", "pair"]):
        return "Team/Mixed"
    else:
        return "Other"

medal_events['event_gender'] = medal_events['event_description'].apply(categorize_gender)


medal_events['start_datetime_local'] = pd.to_datetime(medal_events['start_datetime_local'])
medal_events['end_datetime_local'] = pd.to_datetime(medal_events['end_datetime_local'])


medal_events_men = medal_events[medal_events["event_gender"] == "Male"]
fig_men = px.timeline(
    medal_events_men,
    x_start="start_datetime_local",
    x_end="end_datetime_local",
    y="event_description",
    color="venue_name",
    title="Olympic Medal Events Timeline: Male",
    labels={
        "event_description": "Event",
        "venue_name": "Venue"
    }
)

fig_men.update_xaxes(
    dtick="D1",  # Every day
    tickformat="%d.%m.%Y",  # Day.Month.Year format
    tickangle=45,
    title="Date"
)

# fig_men.show()

### Frauen-Events
medal_events_women = medal_events[medal_events["event_gender"] == "Female"] 
fig_women = px.timeline(
    medal_events_women, 
    x_start="start_datetime_local",
    x_end="end_datetime_local",
    y="event_description",
    color="venue_name",
    title="Olympic Medal Events Timeline: Female",
    labels={
        "event_description": "Event",
        "venue_name": "Venue"
    }
)

# fig_women.show()