Arduino Microcontroller Prototyping Course for Hsinchu Science Park Families

1. What Is Seaborn?

Seaborn is a high-level statistical visualization library built on Matplotlib. It targets clean EDA charts with less boilerplate, built-in themes, and direct Pandas integration.

2. Strengths

• Reads DataFrame columns for axes and legends
• Themes: darkgrid, whitegrid, dark, white, ticks
• Distribution, categorical, relational, and matrix plot families

3. Figure-Level vs Axes-Level

• Figure-level (relplot, catplot, displot): own the whole figure; support faceting with col/row
• Axes-level (scatterplot, boxplot, histplot): draw on one Matplotlib ax inside custom layouts

4. Healthcare Sample + Scatter

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="whitegrid")

df = pd.read_csv("healthcare_patient_analytics_seaborn.csv")
sample_df = df.sample(n=500, random_state=42)

plt.figure(figsize=(9, 6))
sns.scatterplot(
    data=sample_df,
    x="Treatment Cost",
    y="Recovery Score",
    hue="Visit Type"
)
plt.title("Treatment Cost vs. Recovery Score by Visit Type")
plt.show()

hue auto-colors Emergency vs Routine and builds the legend.

5. Faceting with relplot

sns.relplot(
    data=sample_df,
    x="Treatment Cost",
    y="Recovery Score",
    hue="Visit Type",
    col="Gender"
)
plt.show()

One call splits Female/Male into side-by-side panels with shared scales.

6. vs Matplotlib

Seaborn often needs one call where Matplotlib needs scatter + labels + title separately, and default markers/themes tend to be clearer for dense EDA samples.