Visualizing China’s Olympic Medal History with Python and Plotly

Hello, I'm Peter. This article visualizes China's Olympic medal performance from 1984 to 2016 using Python and Plotly.

Data Preparation

The raw data was collected from the web and organized into two formats:

Wide table : each row contains all fields for a given Olympic edition.

Long table : each row records a single metric, allowing repeated fields.

Both tables are used for different visualizations.

Import Libraries

# Data processing
import pandas as
import numpy as

# Plotting
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots  # for multi‑subplot figures

Total Medal Count Trend

A line chart shows the total number of medals China won at each Summer Olympics.

fig = px.line(df, x="地点", y="总数", text="总数")
fig.update_layout(title="中国获得总奖牌数")
fig.show()

Medal Distribution by Gender

A scatter plot compares gold medals won by men and women across editions.

fig = px.scatter(df, x="地点", y="总数", color="金牌", size="总数", text="总数")
fig.show()

Multi‑Metric Subplots

Using make_subplots, the script creates a 4×2 grid showing total medals, gold, silver, bronze, gold‑rank, male gold, female gold, and gold‑share.

# Set rows and columns
fig = make_subplots(rows=4, cols=2,
    subplot_titles=["奖牌总数","金牌","银牌","铜牌","金牌榜","男子金牌","女子金牌","金牌占比"])
# Add traces (example for total medals)
fig.add_trace(go.Scatter(x=df["年份"].tolist(), y=df["总数"].tolist(), name="总数"), 1, 1)
# ... (additional traces omitted for brevity)
fig.update_layout(height=600, width=800, title_text="奥运会奖牌可视化")
fig.show()

Radar Chart

The radar chart displays gold, silver, and bronze counts for each Olympic edition.

categories = ['金牌','银牌','铜牌']
fig = go.Figure()
fig.add_trace(go.Scatterpolar(r=df.iloc[0,2:5].tolist(), theta=categories, fill='tonext', name='洛杉矶-1984'))
# ... (additional years omitted)
fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[0,54])), showlegend=True)
fig.show()

Pie Charts by Year

Donut charts compare the proportion of gold, silver, and bronze medals for each Olympic edition.

fig = make_subplots(rows=3, cols=3, specs=[[{'type':'domain'}]*3]*3,
    subplot_titles=["1984-洛杉矶","1988-汉城","1992-巴塞罗那","1996-亚特兰大","2000-悉尼","2004-雅典","2008-北京","2012-伦敦","2016-里约热内卢"])
fig.add_trace(go.Pie(labels=df2["奖牌"][:3].tolist(), values=df2["数量"][:3].tolist(), name="1984-洛杉矶"),1,1)
# ... (additional pies omitted)
fig.update_traces(hole=0.2)
fig.show()

Sunburst Diagram

A sunburst visualizes the hierarchical relationship between medal type, Olympic edition, and count.

px.sunburst(df2, path=["奖牌","地点"], values="数量", color="年份", color_continuous_scale="RdBu")

Conclusion

The visual analysis shows that China’s most successful Olympics was the 2008 Beijing Games, with the highest total medal count and gold‑medal share. Across all editions, women’s gold medals consistently outperformed men’s, highlighting the strength of the Chinese female athletes.