TidyTuesday (2026) Week 5: Edible Plants

This week we’re exploring edible plants! The Edible Plant Database (EPD) is an outcome of the GROW Observatory, a European Citizen Science project on growing food, soil moisture sensing and land monitoring. It contains information on 146 edible plant species, including their ideal growing conditions and time to harvest and germination.

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()