Data Visualization with Python

4.

Statistical Visualization with Seaborn

4.1.

Introduction to Seaborn

4.1.1.

Seaborn Philosophy

4.1.2.

Integration with Matplotlib

4.1.3.

Dataset Integration

4.1.4.

Function Categories

4.1.4.1.

Figure-Level Functions

4.1.4.2.

Axes-Level Functions

4.2.

Visualizing Statistical Relationships

4.2.1.

Relational Plots

4.2.1.1.

Scatter Plot Relationships

4.2.1.1.1.

Basic Scatter Plots

4.2.1.1.2.

Categorical Grouping

4.2.1.1.3.

Size and Style Mapping

4.2.1.2.

Line Plot Relationships

4.2.1.2.1.

Time Series Visualization

4.2.1.2.2.

Confidence Intervals

4.2.1.2.3.

Multiple Line Groups

4.2.1.3.

Faceting with relplot

4.2.1.3.1.

Column and Row Facets

4.2.1.3.2.

Wrap Parameters

4.2.1.3.3.

4.3.

Categorical Data Visualization

4.3.1.

Categorical Scatter Plots

4.3.1.1.

4.3.1.1.1.

Jittering Techniques

4.3.1.1.2.

Grouping Categories

4.3.1.2.

4.3.1.2.1.

Avoiding Overlap

4.3.1.2.2.

Large Dataset Handling

4.3.2.

Categorical Distribution Plots

4.3.2.1.

4.3.2.1.1.

Quartile Representation

4.3.2.1.2.

Outlier Detection

4.3.2.1.3.

Grouped Box Plots

4.3.2.2.

4.3.2.2.1.

Kernel Density Estimation

4.3.2.2.2.

4.3.2.2.3.

Inner Plot Types

4.3.2.3.

Box-and-Whisker Variations

4.3.2.3.1.

4.3.2.3.2.

Letter Value Plots

4.3.3.

Categorical Estimate Plots

4.3.3.1.

4.3.3.1.1.

Mean Estimation

4.3.3.1.2.

Error Bar Representation

4.3.3.1.3.

Horizontal Orientation

4.3.3.2.

4.3.3.2.1.

Confidence Intervals

4.3.3.2.2.

Connection Lines

4.3.3.2.3.

Dodge Parameters

4.3.3.3.

4.3.3.3.1.

Frequency Visualization

4.3.3.3.2.

Order Parameters

4.4.

Distribution Visualization

4.4.1.

Univariate Distributions

4.4.1.1.

4.4.1.1.1.

4.4.1.1.2.

Density Normalization

4.4.1.1.3.

Multiple Distributions

4.4.1.2.

Kernel Density Estimation

4.4.1.2.1.

Bandwidth Selection

4.4.1.2.2.

4.4.1.2.3.

Shade and Fill Options

4.4.1.3.

Empirical Cumulative Distribution

4.4.1.3.1.

Complementary ECDF

4.4.1.3.2.

Comparison Techniques

4.4.1.4.

4.4.1.4.1.

Marginal Distributions

4.4.1.4.2.

Height Parameters

4.4.2.

Bivariate Distributions

4.4.2.1.

4.4.2.1.1.

Scatter with Marginals

4.4.2.1.2.

4.4.2.1.3.

4.4.2.1.4.

Regression Lines

4.4.2.2.

4.4.2.2.1.

Multiple Variable Relationships

4.4.2.2.2.

Diagonal Plot Types

4.4.2.2.3.

Grouping Variables

4.5.

Matrix Visualization

4.5.1.

4.5.1.1.

Correlation Matrices

4.5.1.2.

Pivot Table Visualization

4.5.1.3.

Annotation Options

4.5.1.4.

Color Map Selection

4.5.1.5.

Masking Techniques

4.5.2.

4.5.2.1.

Hierarchical Clustering

4.5.2.2.

Dendrogram Interpretation

4.5.2.3.

Distance Metrics

4.5.2.4.

Linkage Methods

4.6.

Grid-Based Plotting

4.6.1.

4.6.1.1.

Multi-Panel Layouts

4.6.1.2.

Mapping Functions

4.6.1.3.

4.6.1.4.

Legend Handling

4.6.2.

4.6.2.1.

Customizable Pair Plots

4.6.2.2.

Different Plot Types per Position

4.6.2.3.

Diagonal Customization

4.7.

Seaborn Styling and Themes

4.7.1.

Built-in Themes

4.7.1.1.

4.7.1.2.

4.7.1.3.

4.7.1.4.

4.7.1.5.

4.7.2.

Context Scaling

4.7.2.1.

4.7.2.2.

4.7.2.3.

4.7.2.4.

4.7.3.

4.7.3.1.

Qualitative Palettes

4.7.3.2.

Sequential Palettes

4.7.3.3.

Diverging Palettes

4.7.3.4.

Custom Palette Creation

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5. Interactive Visualization with Plotly