ch13s3_SeabornForStatisticalVisualization

[Seaborn](https://seaborn.pydata.org/) is a **high-level data visualization library** built on top of Matplotlib.

Chapter 13: Data Visualization — Seaborn for Statistical Visualization

🌊 Seaborn for Statistical Visualization

Seaborn is a high-level data visualization library built on top of Matplotlib.
It’s specifically designed for creating statistical and aesthetically pleasing visualizations with minimal code.
Seaborn integrates tightly with Pandas DataFrames, making it ideal for exploring and analyzing datasets visually.

⚙️ 1. Installing and Setting Up Seaborn

pip install seaborn

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="whitegrid", palette="muted")

🎨 Seaborn automatically handles color palettes, themes, and statistical aggregation.

📊 2. Distribution Plots — Understanding Data Spread

Distribution plots reveal how data values are distributed, showing frequency and shape.

Example — Histogram with Density Curve

import seaborn as sns
import matplotlib.pyplot as plt

data = [35, 45, 50, 55, 60, 62, 65, 68, 70, 72, 75, 80, 85]

sns.histplot(data, bins=6, kde=True, color="royalblue")

plt.title("Distribution of Values", fontsize=14)
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()

💡 kde=True adds a smooth curve showing the probability density function (PDF).

🟢 3. Scatter Plots — Visualizing Relationships

Scatter plots show relationships between two continuous variables and optionally group data by categories.

data = sns.load_dataset("iris")

sns.scatterplot(data=data, x="sepal_length", y="sepal_width", hue="species", style="species", s=100)

plt.title("Sepal Dimensions by Species", fontsize=14)
plt.xlabel("Sepal Length (cm)")
plt.ylabel("Sepal Width (cm)")
plt.show()

🎯 Use hue for color grouping and style or size for additional dimensions.

🧩 4. Box and Violin Plots — Comparing Distributions

These plots are ideal for comparing spread, median, and outliers across categories.

Example — Boxplot

sns.boxplot(data=data, x="species", y="sepal_length", palette="Set2")
plt.title("Sepal Length by Species (Boxplot)")
plt.show()

Example — Violin Plot

sns.violinplot(data=data, x="species", y="sepal_length", inner="quartile", palette="pastel")
plt.title("Sepal Length by Species (Violin Plot)")
plt.show()

📦 Boxplots emphasize median & quartiles, while 🎻 violin plots show full distribution density.

📈 5. Regression and Trend Analysis

Seaborn’s regression functions help visualize linear or non-linear trends in data.

tips = sns.load_dataset("tips")

sns.regplot(data=tips, x="total_bill", y="tip", scatter_kws={"alpha":0.6}, line_kws={"color":"red"})
plt.title("Relationship between Bill and Tip")
plt.show()

sns.regplot() automatically fits and visualizes a regression line with confidence intervals.

🔗 6. Pair Plots — Exploring Multivariate Relationships

Pair plots visualize all pairwise relationships in a dataset.

sns.pairplot(data=data, hue="species", diag_kind="kde", palette="husl")
plt.suptitle("Pairwise Relationships — Iris Dataset", y=1.02)
plt.show()

Each diagonal plot shows a univariate distribution, while off-diagonal plots show bivariate relationships.

🔥 7. Heatmaps — Correlation and Matrix Data

Heatmaps are great for visualizing matrix-like data or correlation coefficients.

corr = data.corr(numeric_only=True)

sns.heatmap(corr, annot=True, cmap="coolwarm", linewidths=0.5)
plt.title("Correlation Heatmap")
plt.show()

Use heatmaps to quickly detect relationships or multicollinearity between variables.

🧮 8. Categorical Plots and Facet Grids

Seaborn provides versatile functions for categorical data visualization.

sns.catplot(data=tips, x="day", y="total_bill", kind="box", hue="sex", height=5, aspect=1.2)
plt.title("Boxplot by Day and Gender")
plt.show()

You can also split data into subplots using FacetGrid for comparisons:

g = sns.FacetGrid(tips, col="sex", row="time", margin_titles=True)
g.map_dataframe(sns.scatterplot, x="total_bill", y="tip", color="teal")
g.add_legend()
plt.show()

🎨 9. Customization and Themes

Change aesthetics globally with:

sns.set_theme(style="darkgrid", context="talk", palette="deep")

Try sns.color_palette("flare") or sns.set_palette("pastel") for soft, publication-ready colors.

🧭 10. Summary — Common Seaborn Plot Types

🧭 Conclusion

Seaborn brings beauty, simplicity, and statistical intelligence to data visualization.
It reduces boilerplate code and automatically handles themes, grouping, and color mapping — allowing you to focus on insights instead of formatting.

“With Seaborn, your data tells its story — elegantly, statistically, and beautifully.”