Data Visualization та візуалізація даних

📚 Libraries

Matplotlib

Types: Line, bar, scatter, histogram plots.

Advantages: Flexible, full control.

Applications: For custom visualizations.

Seaborn

Types: Statistical plots, heatmaps, pair plots.

Advantages: Beautiful defaults, statistical plots.

Applications: For EDA.

Plotly

Types: Interactive plots, 3D, animations.

Advantages: Interactivity, for web.

Applications: For presentations, dashboards.

🔧 Types of Visualizations

Exploratory Plots

Histograms: For distributions.

Scatter: For relationships.

Box plots: For distributions, outliers.

Statistical Plots

Correlation heatmaps: For correlations.

Pair plots: For pairwise relationships.

Violin plots: For distributions.

Time Series Plots

Line plots: For trends.

Seasonal plots: For seasonality.

Decomposition: For components.

📚 Practical Examples

Example 1: EDA with Seaborn

Distributions: Create histograms for numerical features.

Relationships: Create scatter plots and correlation heatmap.

Categories: Create box plots for categorical data.

Example 2: Interactive Dashboard

Plotly: Create interactive plots.

Dash: Create a dashboard using Plotly Dash.

Deployment: Deploy the dashboard for use.

© 2025 Scientific Simulator. All rights reserved.

Data Visualization: data visualization