AI-Aquatica

📊 Usage – Data Visualization (AI-Aquatica)

This guide presents how to use the visualization module to create basic and advanced plots for water quality and environmental datasets.

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1. 📦 Importing

from ai_aquatica.visualization import (
    plot_line,
    plot_bar,
    plot_pie,
    plot_scatter,
    plot_heatmap,
    plot_pca,
    plot_tsne,
    plot_interactive_bubble
)

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2. 📈 Sample dataset

import pandas as pd
import numpy as np

data = pd.DataFrame({
    'feature1': np.random.randn(100),
    'feature2': np.random.randn(100),
    'category': np.random.choice(['A', 'B', 'C'], 100),
    'size': np.random.randint(1, 100, 100)
})

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3. 📉 Basic visualizations

Line plot

plot_line(data, 'feature1', 'feature2')

Bar plot

plot_bar(data, 'category', 'size')

Pie chart

plot_pie(data, 'category')

Scatter plot

plot_scatter(data, 'feature1', 'feature2')

Heatmap of correlations

plot_heatmap(data[['feature1', 'feature2']])

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4. 📊 Advanced visualizations

PCA (Principal Component Analysis)

plot_pca(data[['feature1', 'feature2']])

t-SNE (Dimensionality Reduction)

plot_tsne(data[['feature1', 'feature2']])

Interactive Bubble Chart

plot_interactive_bubble(data, 'feature1', 'feature2', 'size', 'category')

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🧠 Notes

• Requires plotly for interactive bubble chart.
• PCA and t-SNE operate on numeric, scaled features.
• Always visualize your data before and after cleaning or modeling.

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