Data Visualization with Seaborn

Data Visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. It's crucial for exploratory data analysis (EDA), communicating insights, and making data-driven decisions.

Seaborn is a powerful Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. While Matplotlib provides the foundational framework for plotting in Python, Seaborn builds on top of it, offering a more streamlined approach for complex statistical plots, often requiring less code.

Key features and advantages of Seaborn include:

1. High-level API: It simplifies the creation of many common statistical plot types that would require more intricate code in Matplotlib alone.
2. Statistical Focus: It is specifically designed for visualizing relationships between multiple variables, distributions of variables, and comparisons across different categories.
3. Aesthetics and Themes: Seaborn comes with built-in themes, color palettes, and styling options that make plots visually appealing by default, requiring minimal customization.
4. Integration with Pandas: It works seamlessly with Pandas DataFrames, allowing easy plotting directly from structured data.
5. Diverse Plot Types: Seaborn offers a wide array of plot types, including:
- Relational plots: `scatterplot`, `lineplot` (to visualize relationships between two variables).
- Categorical plots: `boxplot`, `violinplot`, `swarmplot`, `countplot`, `barplot` (to compare numerical variables across different categories).
- Distribution plots: `histplot`, `kdeplot`, `displot`, `rugplot` (to visualize the distribution of a single variable).
- Regression plots: `lmplot`, `regplot` (to visualize linear relationships and their fits).
- Matrix plots: `heatmap` (for visualizing correlation matrices or other grid-based data).
- Multi-plot grids: `FacetGrid`, `PairGrid`, `JointGrid` (for creating complex grids of plots based on different subsets of data or variable combinations).

In essence, Seaborn enhances the capabilities of Matplotlib by providing specialized tools for statistical graphics, making the process of data exploration and communication more efficient and visually engaging.

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

 Set the aesthetic style of the plots
sns.set_theme(style="whitegrid")

 Load a sample dataset (Iris dataset is built into seaborn)
iris = sns.load_dataset("iris")

 --- Example 1: Distribution Plot (Histogram + KDE) ---
plt.figure(figsize=(8, 5))
sns.histplot(data=iris, x="sepal_length", kde=True, bins=20)
plt.title("Distribution of Sepal Length")
plt.xlabel("Sepal Length (cm)")
plt.ylabel("Count")
plt.show()

 --- Example 2: Relational Plot (Scatter Plot) ---
plt.figure(figsize=(10, 6))
sns.scatterplot(data=iris, x="sepal_length", y="sepal_width", hue="species", s=100, alpha=0.8)
plt.title("Sepal Length vs. Sepal Width by Species")
plt.xlabel("Sepal Length (cm)")
plt.ylabel("Sepal Width (cm)")
plt.legend(title="Species")
plt.show()

 --- Example 3: Categorical Plot (Box Plot) ---
plt.figure(figsize=(10, 6))
sns.boxplot(data=iris, x="species", y="petal_length", palette="viridis")
plt.title("Petal Length Distribution by Species")
plt.xlabel("Species")
plt.ylabel("Petal Length (cm)")
plt.show()

 --- Example 4: Pair Plot (Visualize relationships between all numerical columns) ---
 This is a powerful plot to get an overview of the dataset
sns.pairplot(iris, hue="species", diag_kind="kde", markers=["o", "s", "D"])
plt.suptitle("Pair Plot of Iris Dataset by Species", y=1.02)  Adjust title position
plt.show()

 --- Example 5: Heatmap for Correlation Matrix ---
 Calculate the correlation matrix
corr_matrix = iris.drop('species', axis=1).corr()

plt.figure(figsize=(8, 7))
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", fmt=".2f", linewidths=.5)
plt.title("Correlation Matrix of Iris Features")
plt.show()