Olympics Data Analysis

Goal

Use Olympics medals/athletes data to:

Rank countries by medals
See trends over years
Identify top sports

Analysis pipeline

diagram Olympics medal analysis pipeline mermaid

  flowchart LR
  A["Raw results"] --> B["Count
(medals per country)"]
  B --> C["Rank
(sort_values, top 10)"]
  C --> D["Visualize
(bar chart)"]
  D --> E["Trend
(medals by year)"]

From raw results to a ranked leaderboard of countries and sports.

Step 1: Load

Load

import pandas as pd
 
df = pd.read_csv("data/olympics.csv")
print(df.head())

Load

import pandas as pd
 
df = pd.read_csv("data/olympics.csv")
print(df.head())

Step 2: Basic counts

Medal counts

medals = df.groupby("country").size().sort_values(ascending=False).head(10)
print(medals)

Medal counts

medals = df.groupby("country").size().sort_values(ascending=False).head(10)
print(medals)

Step 3: Plot top countries

Top countries

import matplotlib.pyplot as plt
 
plt.figure(figsize=(10, 4))
plt.bar(medals.index, medals.values)
plt.title("Top 10 countries by medals")
plt.xticks(rotation=25)
plt.tight_layout()
plt.show()

Top countries

import matplotlib.pyplot as plt
 
plt.figure(figsize=(10, 4))
plt.bar(medals.index, medals.values)
plt.title("Top 10 countries by medals")
plt.xticks(rotation=25)
plt.tight_layout()
plt.show()

Visualize it

sketch Top countries by medal count p5.js

Horizontal bars rank countries from most to fewest medals.

Deliverable

Top 10 countries
Change over years
Sports that contribute most

🧪 Try It Yourself

Exercise 1 – Medal counts per country

Exercise 2 – Top N ranking

Exercise 3 – Medals by year (trend)

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