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  <div class="app flex">
    <!-- Sidebar Navigation -->
    <aside class="sidebar" id="sidebar">
      <h1 class="sidebar__title">📊 Data Visualization</h1>
      <nav>
        <h3 class="sidebar__section">Foundations</h3>
        <ul class="nav__list" id="navList">
          <li><a href="#intro" class="nav__link">🎯 Why Visualize Data?</a></li>
          <li><a href="#perception" class="nav__link">👁️ Visual Perception</a></li>
          <li><a href="#grammar" class="nav__link">📐 Grammar of Graphics</a></li>
          <li><a href="#choosing-charts" class="nav__link">🎨 Choosing the Right Chart</a></li>
        </ul>
        <h3 class="sidebar__section">Matplotlib Essentials</h3>
        <ul class="nav__list">
          <li><a href="#matplotlib-anatomy" class="nav__link">🔬 Figure Anatomy</a></li>
          <li><a href="#basic-plots" class="nav__link">📈 Basic Plots</a></li>
          <li><a href="#subplots" class="nav__link">🔲 Subplots & Layouts</a></li>
          <li><a href="#styling" class="nav__link">🎨 Styling & Themes</a></li>
        </ul>
        <h3 class="sidebar__section">Seaborn Statistical Viz</h3>
        <ul class="nav__list">
          <li><a href="#seaborn-intro" class="nav__link">🌊 Seaborn Overview</a></li>
          <li><a href="#distributions" class="nav__link">📊 Distribution Plots</a></li>
          <li><a href="#relationships" class="nav__link">🔗 Relationship Plots</a></li>
          <li><a href="#categorical" class="nav__link">📦 Categorical Plots</a></li>
          <li><a href="#heatmaps" class="nav__link">🔥 Heatmaps & Clusters</a></li>
        </ul>
        <h3 class="sidebar__section">Interactive Visualization</h3>
        <ul class="nav__list">
          <li><a href="#plotly" class="nav__link">🚀 Plotly Express</a></li>
          <li><a href="#animations" class="nav__link">🎬 Animations</a></li>
          <li><a href="#dashboards" class="nav__link">📱 Dashboards (Streamlit)</a></li>
        </ul>
        <h3 class="sidebar__section">Advanced Topics</h3>
        <ul class="nav__list">
          <li><a href="#geospatial" class="nav__link">🗺️ Geospatial Viz</a></li>
          <li><a href="#3d-plots" class="nav__link">🎲 3D Visualization</a></li>
          <li><a href="#storytelling" class="nav__link">📖 Data Storytelling</a></li>
        </ul>
      </nav>
    </aside>

    <!-- Main Content -->
    <main class="content" id="content">
      <!-- ============================ 1. INTRO ============================ -->
      <section id="intro" class="topic-section">
        <h2>🎯 Why Visualize Data?</h2>
        <p>Data visualization transforms abstract numbers into visual stories. The human brain processes images 60,000×
          faster than text. Visualization helps us <strong>explore</strong>, <strong>analyze</strong>, and
          <strong>communicate</strong> data effectively.
        </p>

        <div class="info-card">
          <strong>Anscombe's Quartet:</strong> Four datasets with nearly identical statistical properties (mean,
          variance, correlation) that look completely different when plotted. This demonstrates why visualization is
          essential - statistics alone can be misleading!
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-anscombe" width="800" height="400"></canvas>
        </div>

        <h3>Three Purposes of Visualization</h3>
        <div class="info-card">
          <strong>1. Exploratory:</strong> Discover patterns, anomalies, and insights in your data<br>
          <strong>2. Explanatory:</strong> Communicate findings to stakeholders clearly<br>
          <strong>3. Confirmatory:</strong> Verify hypotheses and validate models
        </div>

        <div class="callout callout--insight">💡 "The greatest value of a picture is when it forces us to notice what we
          never expected to see." — John Tukey</div>
        <div class="callout callout--tip">✅ Always start with visualization before building ML models.</div>
      </section>

      <!-- ====================== 2. VISUAL PERCEPTION ================== -->
      <section id="perception" class="topic-section">
        <h2>👁️ Visual Perception & Pre-attentive Attributes</h2>
        <p>The human visual system can detect certain visual attributes almost instantly (< 250ms) without conscious
            effort. These are called <strong>pre-attentive attributes</strong>.</p>

        <div class="info-card">
          <strong>Pre-attentive Attributes:</strong>
          <ul>
            <li><strong>Position:</strong> Most accurate for quantitative data (use X/Y axes)</li>
            <li><strong>Length:</strong> Bar charts leverage this effectively</li>
            <li><strong>Color Hue:</strong> Best for categorical distinctions</li>
            <li><strong>Color Intensity:</strong> Good for gradients/magnitude</li>
            <li><strong>Size:</strong> Bubble charts, but humans underestimate area</li>
            <li><strong>Shape:</strong> Useful for categories, but limit to 5-7 shapes</li>
            <li><strong>Orientation:</strong> Lines, angles</li>
          </ul>
        </div>

        <div class="form-group">
          <button id="btn-position" class="btn btn--primary">Position Encoding</button>
          <button id="btn-color" class="btn btn--primary">Color Encoding</button>
          <button id="btn-size" class="btn btn--primary">Size Encoding</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-perception" width="700" height="350"></canvas>
        </div>

        <h3>Cleveland & McGill's Accuracy Ranking</h3>
        <div class="info-card">
          <strong>Most Accurate → Least Accurate:</strong><br>
          1. Position on common scale (bar chart)<br>
          2. Position on non-aligned scale (multiple axes)<br>
          3. Length (bar)<br>
          4. Angle, Slope<br>
          5. Area<br>
          6. Volume, Curvature<br>
          7. Color saturation, Color hue
        </div>

        <div class="callout callout--mistake">⚠️ Pie charts use angle (low accuracy). Bar charts are almost always
          better!</div>
        <div class="callout callout--tip">✅ Use position for most important data, color for categories.</div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: The Weber-Fechner Law</h3>
          <p>Why are humans bad at comparing bubble sizes (area) but great at comparing bar chart heights
            (length/position)? Human perception of physical magnitudes follows a logarithmic scale, not a linear one.
          </p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; font-size: 1.1em; color: #e4e6eb;">
            $$ \frac{\Delta I}{I} = k \quad \Rightarrow \quad S = c \ln\left(\frac{I}{I_0}\right) $$
          </div>
          <ul style="margin-bottom: 0;">
            <li><strong>$I$</strong>: Initial stimulus intensity (e.g., initial bubble area)</li>
            <li><strong>$\Delta I$</strong>: Just Noticeable Difference (JND) required to perceive a change</li>
            <li><strong>$k$</strong>: Weber's constant. For length/position $k \approx 0.03$ (very sensitive), but for
              area $k \approx 0.10$ to $0.20$ (very insensitive).</li>
          </ul>
        </div>
      </section>

      <!-- ====================== 3. GRAMMAR OF GRAPHICS ================== -->
      <section id="grammar" class="topic-section">
        <h2>📐 The Grammar of Graphics</h2>
        <p>The Grammar of Graphics (Wilkinson, 1999) is a framework for describing statistical graphics. It's the
          foundation of ggplot2 (R) and influences Seaborn, Altair, and Plotly.</p>

        <div class="info-card">
          <strong>Components of a Graphic:</strong>
          <ul>
            <li><strong>Data:</strong> The dataset being visualized</li>
            <li><strong>Aesthetics (aes):</strong> Mapping data to visual properties (x, y, color, size)</li>
            <li><strong>Geometries (geom):</strong> Visual elements (points, lines, bars, areas)</li>
            <li><strong>Facets:</strong> Subplots by categorical variable</li>
            <li><strong>Statistics:</strong> Transformations (binning, smoothing, aggregation)</li>
            <li><strong>Coordinates:</strong> Cartesian, polar, map projections</li>
            <li><strong>Themes:</strong> Non-data visual elements (fonts, backgrounds)</li>
          </ul>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-grammar" width="800" height="400"></canvas>
        </div>

        <div class="callout callout--insight">💡 Understanding Grammar of Graphics makes you a better visualizer in ANY
          library.</div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: Coordinate Transformations</h3>
          <p>When mapping data to visuals, the coordinate system applies a mathematical transformation matrix. For
            example, converting standard Cartesian coordinates $(x, y)$ to Polar coordinates $(r, \theta)$ to render a
            pie chart or Coxcomb plot:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; overflow-x: auto; color: #e4e6eb;">
            $$ r = \sqrt{x^2 + y^2} $$
            $$ \theta = \text{atan2}(y, x) $$
            $$ \begin{bmatrix} x \\ y \end{bmatrix} = \begin{bmatrix} r \cos(\theta) \\ r \sin(\theta) \end{bmatrix} $$
          </div>
          <p style="margin-bottom: 0;">This is why pie charts are computationally and perceptually different from bar
            charts—they apply a non-linear polar transformation to the linear data dimensions.</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>app.py - Grammar of Graphics with Plotnine (Python)</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import pandas as pd
from plotnine import *

# Following Grammar of Graphics exactly:
# Data (mpg) -> Aesthetics (x,y,color) -> Geometries (point, smooth)
plot = (
    ggplot(mpg, aes(x='displ', y='hwy', color='class'))
    + geom_point(size=3, alpha=0.7)
    + geom_smooth(method='lm', se=False) # Add regression line
    + theme_minimal()                    # Add theme
    + labs(title='Engine Displacement vs Highway MPG')
)
print(plot)</code></pre>
        </div>
      </section>

      <!-- ====================== 4. CHOOSING CHARTS ================== -->
      <section id="choosing-charts" class="topic-section">
        <h2>🎨 Choosing the Right Chart</h2>
        <p>The best visualization depends on your <strong>data type</strong> and <strong>question</strong>. Here's a
          decision guide:</p>

        <div class="info-card">
          <strong>Single Variable (Univariate):</strong><br>
          • Continuous: Histogram, KDE, Box plot, Violin plot<br>
          • Categorical: Bar chart, Count plot<br><br>

          <strong>Two Variables (Bivariate):</strong><br>
          • Both Continuous: Scatter plot, Line chart, Hexbin, 2D histogram<br>
          • Continuous + Categorical: Box plot, Violin, Strip, Swarm<br>
          • Both Categorical: Heatmap, Grouped bar chart<br><br>

          <strong>Multiple Variables (Multivariate):</strong><br>
          • Pair plot (scatterplot matrix)<br>
          • Parallel coordinates<br>
          • Heatmap correlation matrix<br>
          • Faceted plots (small multiples)
        </div>

        <div class="form-group">
          <button id="btn-comparison" class="btn btn--primary">Comparison</button>
          <button id="btn-composition" class="btn btn--primary">Composition</button>
          <button id="btn-distribution" class="btn btn--primary">Distribution</button>
          <button id="btn-relationship" class="btn btn--primary">Relationship</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-choosing" width="800" height="400"></canvas>
        </div>

        <h3>Common Chart Mistakes</h3>
        <div class="callout callout--mistake">⚠️ <strong>Pie charts for many categories</strong> - Use bar chart instead
        </div>
        <div class="callout callout--mistake">⚠️ <strong>3D effects on 2D data</strong> - Distorts perception</div>
        <div class="callout callout--mistake">⚠️ <strong>Truncated Y-axis</strong> - Exaggerates differences</div>
        <div class="callout callout--mistake">⚠️ <strong>Rainbow color scales</strong> - Not perceptually uniform</div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: Information Entropy in Visuals</h3>
          <p>How much data can a chart "handle" before it becomes cluttered? We can use Shannon Entropy ($H$) to
            quantify the visual information density. If a chart has $n$ visual marks (dots, lines) with probabilities
            $p_i$ of drawing attention:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; color: #e4e6eb;">
            $$ H(X) = - \sum_{i=1}^{n} p_i \log_2(p_i) $$
          </div>
          <p style="margin-bottom: 0;"><strong>Takeaway:</strong> If you add too many dimensions (color, size, shape
            simultaneously) on a single plot, the entropy $H$ exceeds human working memory limits ($\approx 2.5$ bits),
            leading to chart fatigue. This is mathematically why "less is more" in dashboard design.</p>
        </div>
      </section>

      <!-- ====================== 5. MATPLOTLIB ANATOMY ================== -->
      <section id="matplotlib-anatomy" class="topic-section">
        <h2>🔬 Matplotlib Figure Anatomy</h2>
        <p>Understanding Matplotlib's object hierarchy is key to creating professional visualizations.</p>

        <div class="info-card">
          <strong>Hierarchical Structure:</strong><br>
          <code>Figure → Axes → Axis → Tick → Label</code><br><br>

          • <strong>Figure:</strong> The overall window/canvas<br>
          • <strong>Axes:</strong> The actual plot area (NOT the X/Y axis!)<br>
          • <strong>Axis:</strong> The X or Y axis with ticks and labels<br>
          • <strong>Artist:</strong> Everything visible (lines, text, patches)
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-anatomy" width="800" height="500"></canvas>
        </div>

        <h3>Two Interfaces</h3>
        <div class="info-card">
          <strong>1. pyplot (MATLAB-style):</strong> Quick, implicit state<br>
          <code>plt.plot(x, y)</code><br>
          <code>plt.xlabel('Time')</code><br>
          <code>plt.show()</code><br><br>

          <strong>2. Object-Oriented (OO):</strong> Explicit, recommended for complex plots<br>
          <code>fig, ax = plt.subplots()</code><br>
          <code>ax.plot(x, y)</code><br>
          <code>ax.set_xlabel('Time')</code>
        </div>

        <div class="callout callout--tip">✅ Always use OO interface for publication-quality plots.</div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: Affine Transformations</h3>
          <p>How does Matplotlib convert your data coordinates (e.g., $x \in [0, 1000]$) into physical pixels on your
            screen? It uses a continuous pipeline of <strong>Affine Transformation Matrices</strong>:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; overflow-x: auto; color: #e4e6eb;">
            $$ \begin{bmatrix} x_{\text{display}} \\ y_{\text{display}} \\ 1 \end{bmatrix} = \begin{bmatrix} s_x & 0 &
            t_x \\ 0 & s_y & t_y \\ 0 & 0 & 1 \end{bmatrix} \begin{bmatrix} x_{\text{data}} \\ y_{\text{data}} \\ 1
            \end{bmatrix} $$
          </div>
          <p style="margin-bottom: 0;">This matrix $T$ scales ($s_x, s_y$) and translates ($t_x, t_y$) data points. The
            transformation pipeline is: Data $\rightarrow$ Axes (relative 0-1) $\rightarrow$ Figure (inches)
            $\rightarrow$ Display (pixels based on DPI).</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>plot.py - Matplotlib Object-Oriented Setup</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import matplotlib.pyplot as plt

# 1. Create the Figure (The Canvas) and Axes (The Artist)
fig, ax = plt.subplots(figsize=(10, 6), dpi=100)

# 2. Draw on the Axes
ax.plot([1, 2, 3], [4, 5, 2], marker='o', label='Data A')

# 3. Configure the Axes (Anatomy elements)
ax.set_title("My First OOP Plot", fontsize=16, fontweight='bold')
ax.set_xlabel("X-Axis (Units)", fontsize=12)
ax.set_ylabel("Y-Axis (Units)", fontsize=12)

# Set limits and ticks
ax.set_xlim(0, 4)
ax.set_ylim(0, 6)
ax.grid(True, linestyle='--', alpha=0.7)

# 4. Add accessories
ax.legend(loc='upper right')

# 5. Render or Save
plt.tight_layout() # Prevent clipping
plt.show()
# fig.savefig('my_plot.png', dpi=300)</code></pre>
        </div>
      </section>

      <!-- ====================== 6. BASIC PLOTS ================== -->
      <section id="basic-plots" class="topic-section">
        <h2>📈 Basic Matplotlib Plots</h2>
        <p>Master the fundamental plot types that form the foundation of data visualization.</p>

        <div class="form-group">
          <button id="btn-line" class="btn btn--primary">Line Plot</button>
          <button id="btn-scatter" class="btn btn--primary">Scatter Plot</button>
          <button id="btn-bar" class="btn btn--primary">Bar Chart</button>
          <button id="btn-hist" class="btn btn--primary">Histogram</button>
          <button id="btn-pie" class="btn btn--primary">Pie Chart</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-basic" width="800" height="400"></canvas>
        </div>

        <h3>Code Examples</h3>
        <div class="info-card">
          <strong>Line Plot:</strong><br>
          <code>ax.plot(x, y, color='blue', linestyle='--', marker='o', label='Series A')</code><br><br>

          <strong>Scatter Plot:</strong><br>
          <code>ax.scatter(x, y, c=colors, s=sizes, alpha=0.7, cmap='viridis')</code><br><br>

          <strong>Bar Chart:</strong><br>
          <code>ax.bar(categories, values, color='steelblue', edgecolor='black')</code><br><br>

          <strong>Histogram:</strong><br>
          <code>ax.hist(data, bins=30, edgecolor='white', density=True)</code>
        </div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: The Freedman-Diaconis Rule</h3>
          <p>When you call a histogram without specifying bins, how does the library choose the optimal bin width?
            Advanced statistical libraries use the Freedman-Diaconis rule, which minimizes the integral of the squared
            difference between the histogram and the true underlying probability density:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; font-size: 1.1em; color: #e4e6eb;">
            $$ \text{Bin Width } (h) = 2 \frac{\text{IQR}(x)}{\sqrt[3]{n}} $$
          </div>
          <p style="margin-bottom: 0;">Where $\text{IQR}$ is the Interquartile Range and $n$ is the number of
            observations. Unlike simpler rules (e.g., Sturges' rule), this mathematical method is extremely robust to
            heavy-tailed distributions and outliers.</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>basic_plots.py - Common Matplotlib Patterns</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import matplotlib.pyplot as plt
import numpy as np

fig, axs = plt.subplots(1, 2, figsize=(15, 5))

# 1. Scatter Plot (Color & Size mapping)
x = np.random.randn(100)
y = x + np.random.randn(100)*0.5
sizes = np.random.uniform(10, 200, 100)
colors = x

sc = axs[0].scatter(x, y, s=sizes, c=colors, cmap='viridis', alpha=0.7)
axs[0].set_title('Scatter Plot')
fig.colorbar(sc, ax=axs[0], label='Color Value')

# 2. Bar Chart (with Error Bars)
categories = ['Group A', 'Group B', 'Group C']
values = [10, 22, 15]
errors = [1.5, 3.0, 2.0]

axs[1].bar(categories, values, yerr=errors, capsize=5, color='coral', alpha=0.8)
axs[1].set_title('Bar Chart with Error Bars')
for i, v in enumerate(values):
    axs[1].text(i, v + 0.5, str(v), ha='center')

plt.tight_layout()
plt.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 7. SUBPLOTS ================== -->
      <section id="subplots" class="topic-section">
        <h2>🔲 Subplots & Multi-panel Layouts</h2>
        <p>Combine multiple visualizations into a single figure for comprehensive analysis.</p>

        <div class="form-group">
          <button id="btn-grid-2x2" class="btn btn--primary">2×2 Grid</button>
          <button id="btn-grid-uneven" class="btn btn--primary">Uneven Grid</button>
          <button id="btn-gridspec" class="btn btn--primary">GridSpec</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-subplots" width="800" height="500"></canvas>
        </div>

        <div class="info-card">
          <strong>Methods:</strong><br>
          <code>fig, axes = plt.subplots(2, 2, figsize=(12, 10))</code><br>
          <code>fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)</code><br>
          <code>gs = fig.add_gridspec(3, 3); ax = fig.add_subplot(gs[0, :])</code>
        </div>

        <div class="callout callout--tip">✅ Use plt.tight_layout() or fig.set_constrained_layout(True) to prevent
          overlaps.</div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>subplots.py - Complex Layouts with GridSpec</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

fig = plt.figure(figsize=(10, 8))
gs = gridspec.GridSpec(3, 3, figure=fig)

# 1. Main large plot (spans 2x2 grid)
ax_main = fig.add_subplot(gs[0:2, 0:2])
ax_main.set_title('Main View')

# 2. Side plots (Top right, Bottom right)
ax_side1 = fig.add_subplot(gs[0, 2])
ax_side2 = fig.add_subplot(gs[1, 2])

# 3. Bottom wide plot (spans 1x3 grid)
ax_bottom = fig.add_subplot(gs[2, :])
ax_bottom.set_title('Timeline View')

plt.tight_layout()
plt.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 8. STYLING ================== -->
      <section id="styling" class="topic-section">
        <h2>🎨 Styling & Professional Themes</h2>
        <p>Transform basic plots into publication-quality visualizations.</p>

        <div class="form-group">
          <button id="btn-style-default" class="btn btn--primary">Default</button>
          <button id="btn-style-seaborn" class="btn btn--primary">Seaborn</button>
          <button id="btn-style-ggplot" class="btn btn--primary">ggplot</button>
          <button id="btn-style-dark" class="btn btn--primary">Dark Background</button>
          <button id="btn-style-538" class="btn btn--primary">FiveThirtyEight</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-styling" width="800" height="400"></canvas>
        </div>

        <div class="info-card">
          <strong>Available Styles:</strong><br>
          <code>plt.style.available</code> → Lists all built-in styles<br>
          <code>plt.style.use('seaborn-v0_8-whitegrid')</code><br>
          <code>with plt.style.context('dark_background'):</code>
        </div>

        <h3>Color Palettes</h3>
        <div class="info-card">
          <strong>Perceptually Uniform:</strong> viridis, plasma, inferno, magma, cividis<br>
          <strong>Sequential:</strong> Blues, Greens, Oranges (for magnitude)<br>
          <strong>Diverging:</strong> coolwarm, RdBu (for +/- deviations)<br>
          <strong>Categorical:</strong> tab10, Set2, Paired (discrete groups)
        </div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: Perceptually Uniform Colors (CIELAB)</h3>
          <p>Why do we use "viridis" instead of "rainbow" colormaps? A color map is a mathematical function mapping data
            $f(x) \rightarrow (R, G, B)$. However, standard RGB math doesn't match human perception (Euclidean distance
            in RGB $\neq$ perceived color distance).</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; font-size: 1.1em; color: #e4e6eb;">
            $$ \Delta E^* = \sqrt{(\Delta L^*)^2 + (\Delta a^*)^2 + (\Delta b^*)^2} $$
          </div>
          <p style="margin-bottom: 0;">Advanced colormaps like <em>viridis</em> are calculated in the <strong>CIELAB
              ($L^*a^*b^*$) color space</strong>. In this space, the mathematical distance formula $\Delta E^*$
            perfectly matches how the retina and brain perceive brightness and hue differences, ensuring data is never
            visually distorted.</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>styling.py - Applying Professional Aesthetics</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import matplotlib.pyplot as plt
import seaborn as sns

# 1. Apply a global Seaborn theme
sns.set_theme(style="whitegrid", palette="muted")

# 2. Customize fonts globally
plt.rcParams.update({
    'font.family': 'sans-serif',
    'font.sans-serif': ['Helvetica', 'Arial'],
    'axes.titleweight': 'bold',
    'axes.titlesize': 16,
    'axes.labelsize': 12,
    'lines.linewidth': 2
})

# 3. Plotting with the new theme
fig, ax = plt.subplots(figsize=(8, 5))
ax.plot([1, 2, 3], [4, 5, 2], label='Data')
ax.legend()

# 4. Remove top and right spines (cleaner look)
sns.despine(ax=ax)

plt.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 9. SEABORN INTRO ================== -->
      <section id="seaborn-intro" class="topic-section">
        <h2>🌊 Seaborn: Statistical Visualization</h2>
        <p>Seaborn is a high-level library built on Matplotlib that makes statistical graphics beautiful and easy.</p>

        <div class="info-card">
          <strong>Why Seaborn?</strong>
          <ul>
            <li>Beautiful default styles and color palettes</li>
            <li>Works seamlessly with Pandas DataFrames</li>
            <li>Statistical estimation built-in (confidence intervals, regression)</li>
            <li>Faceting for multi-panel figures</li>
            <li>Functions organized by plot purpose</li>
          </ul>
        </div>

        <h3>Seaborn Function Categories</h3>
        <div class="info-card">
          <strong>Figure-level:</strong> Create entire figures (displot, relplot, catplot)<br>
          <strong>Axes-level:</strong> Draw on specific axes (histplot, scatterplot, boxplot)<br><br>

          <strong>By Purpose:</strong><br>
          • <strong>Distribution:</strong> histplot, kdeplot, ecdfplot, rugplot<br>
          • <strong>Relationship:</strong> scatterplot, lineplot, regplot<br>
          • <strong>Categorical:</strong> stripplot, swarmplot, boxplot, violinplot, barplot<br>
          • <strong>Matrix:</strong> heatmap, clustermap
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-seaborn-intro" width="800" height="400"></canvas>
        </div>
      </section>

      <!-- ====================== 10. DISTRIBUTIONS ================== -->
      <section id="distributions" class="topic-section">
        <h2>📊 Distribution Plots</h2>
        <p>Visualize the distribution of a single variable or compare distributions across groups.</p>

        <div class="form-group">
          <button id="btn-histplot" class="btn btn--primary">Histogram</button>
          <button id="btn-kdeplot" class="btn btn--primary">KDE Plot</button>
          <button id="btn-ecdfplot" class="btn btn--primary">ECDF</button>
          <button id="btn-rugplot" class="btn btn--primary">Rug Plot</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-distributions" width="800" height="400"></canvas>
        </div>

        <div class="info-card">
          <strong>Histogram vs KDE:</strong><br>
          • Histogram: Discrete bins, shows raw counts<br>
          • KDE: Smooth curve, estimates probability density<br>
          • Use both together: <code>sns.histplot(data, kde=True)</code>
        </div>

        <div class="callout callout--insight">💡 ECDF (Empirical Cumulative Distribution Function) avoids binning issues
          entirely.</div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: Kernel Density Estimation (KDE)</h3>
          <p>A KDE plot is not just a smoothed line; it's a mathematical sum of continuous probability distributions
            (kernels) placed at every single data point $x_i$:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; font-size: 1.1em; color: #e4e6eb;">
            $$ \hat{f}_h(x) = \frac{1}{n h} \sum_{i=1}^{n} K\left(\frac{x - x_i}{h}\right) $$
          </div>
          <p style="margin-bottom: 0;">Here, $K$ is typically the Standard Normal Gaussian density function, and $h$ is
            the bandwidth parameter. If $h$ is too small, the curve is jagged (overfit); if $h$ is too large, it hides
            important statistical features (underfit).</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>distributions.py - Visualizing Distributions</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import seaborn as sns
import matplotlib.pyplot as plt

penguins = sns.load_dataset("penguins")

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# 1. Histogram + KDE overlay
sns.histplot(
    data=penguins, x="flipper_length_mm", hue="species",
    element="step", stat="density", common_norm=False,
    ax=axes[0]
)
axes[0].set_title("Histogram with Step Fill")

# 2. KDE Plot with Rug Plot
sns.kdeplot(
    data=penguins, x="body_mass_g", hue="species",
    fill=True, common_norm=False, palette="crest",
    alpha=0.5, linewidth=1.5, ax=axes[1]
)
sns.rugplot(
    data=penguins, x="body_mass_g", hue="species",
    height=0.05, ax=axes[1]
)
axes[1].set_title("KDE Density + Rug Plot")

sns.despine()
plt.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 11. RELATIONSHIPS ================== -->
      <section id="relationships" class="topic-section">
        <h2>🔗 Relationship Plots</h2>
        <p>Explore relationships between two or more continuous variables.</p>

        <div class="form-group">
          <button id="btn-scatter-hue" class="btn btn--primary">Scatter + Hue</button>
          <button id="btn-regplot" class="btn btn--primary">Regression Plot</button>
          <button id="btn-residplot" class="btn btn--primary">Residual Plot</button>
          <button id="btn-pairplot" class="btn btn--primary">Pair Plot</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-relationships" width="800" height="500"></canvas>
        </div>

        <div class="info-card">
          <strong>Key Functions:</strong><br>
          <code>sns.scatterplot(data=df, x='x', y='y', hue='category', size='magnitude')</code><br>
          <code>sns.regplot(data=df, x='x', y='y', scatter_kws={'alpha':0.5})</code><br>
          <code>sns.pairplot(df, hue='species', diag_kind='kde')</code>
        </div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: Ordinary Least Squares (OLS)</h3>
          <p>When you use <code>sns.regplot</code>, Seaborn calculates the line of best fit by minimizing the sum of the
            squared residuals ($e_i^2$). The exact matrix algebra closed-form solution for the coefficients
            $\hat{\boldsymbol{\beta}}$ is:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; font-size: 1.1em; color: #e4e6eb;">
            $$ \hat{\boldsymbol{\beta}} = (\mathbf{X}^T \mathbf{X})^{-1} \mathbf{X}^T \mathbf{y} $$
          </div>
          <p style="margin-bottom: 0;">The shaded region around the line represents the 95% confidence interval, meaning
            if we resampled the data 100 times, the true regression line would fall inside this shaded band 95 times
            (usually computed via bootstrapping).</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>relationships.py - Scatter and Regression</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

# 1. Advanced Scatter (4 dimensions: x, y, color, size)
plt.figure(figsize=(8, 6))
sns.scatterplot(
    data=tips, x="total_bill", y="tip",
    hue="time", size="size", sizes=(20, 200),
    palette="deep", alpha=0.8
)
plt.title("4D Scatter Plot (Total Bill vs Tip)")
plt.show()

# 2. Regression Plot with Subplots (Using lmplot)
# lmplot is a figure-level function that creates multiple subplots automatically
sns.lmplot(
    data=tips, x="total_bill", y="tip", col="time", hue="smoker",
    height=5, aspect=1.2, scatter_kws={'alpha':0.5}
)
plt.show()

# 3. Pairplot (Explore all pairwise relationships)
sns.pairplot(
    data=tips, hue="smoker",
    diag_kind="kde", markers=["o", "s"]
)
plt.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 12. CATEGORICAL ================== -->
      <section id="categorical" class="topic-section">
        <h2>📦 Categorical Plots</h2>
        <p>Visualize distributions and comparisons across categorical groups.</p>

        <div class="form-group">
          <button id="btn-stripplot" class="btn btn--primary">Strip Plot</button>
          <button id="btn-swarmplot" class="btn btn--primary">Swarm Plot</button>
          <button id="btn-boxplot" class="btn btn--primary">Box Plot</button>
          <button id="btn-violinplot" class="btn btn--primary">Violin Plot</button>
          <button id="btn-barplot" class="btn btn--primary">Bar Plot</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-categorical" width="800" height="400"></canvas>
        </div>

        <div class="info-card">
          <strong>When to Use:</strong><br>
          • <strong>Strip/Swarm:</strong> Show all data points (small datasets)<br>
          • <strong>Box:</strong> Summary statistics (median, quartiles, outliers)<br>
          • <strong>Violin:</strong> Full distribution shape + summary<br>
          • <strong>Bar:</strong> Mean/count with error bars
        </div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: The IQR Outlier Rule</h3>
          <p>Box plots identify "outliers" (the individual dots beyond the whiskers) purely mathematically, not
            visually. They use John Tukey's Interquartile Range (IQR) method:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; color: #e4e6eb;">
            $$ \text{IQR} = Q_3 - Q_1 $$
            $$ \text{Lower Fence} = Q_1 - 1.5 \times \text{IQR} $$
            $$ \text{Upper Fence} = Q_3 + 1.5 \times \text{IQR} $$
          </div>
          <p style="margin-bottom: 0;">Any point strictly outside $[Lower, Upper]$ is plotted as an outlier. <strong>Fun
              Fact:</strong> In a perfectly normal Gaussian distribution $\mathcal{N}(\mu, \sigma^2)$, exactly 0.70% of
            the data will be incorrectly flagged as outliers by this static math rule!</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>categorical.py - Categories and Factor Variables</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# 1. Violin Plot (Distribution density across categories)
sns.violinplot(
    data=tips, x="day", y="total_bill", hue="sex",
    split=True, inner="quart", palette="muted",
    ax=axes[0]
)
axes[0].set_title("Violin Plot (Split by Sex)")

# 2. Boxplot + Swarmplot Overlay
# Good for showing summary stats PLUS underlying data points
sns.boxplot(
    data=tips, x="day", y="total_bill", color="white",
    width=.5, showfliers=False, ax=axes[1] # hide boxplot outliers to avoid overlap
)
sns.swarmplot(
    data=tips, x="day", y="total_bill", hue="time",
    size=6, alpha=0.7, ax=axes[1]
)
axes[1].set_title("Boxplot + Swarmplot Overlay")

plt.tight_layout()
plt.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 13. HEATMAPS ================== -->
      <section id="heatmaps" class="topic-section">
        <h2>🔥 Heatmaps & Correlation Matrices</h2>
        <p>Visualize matrices of values using color intensity. Essential for EDA correlation analysis.</p>

        <div class="form-group">
          <button id="btn-heatmap-basic" class="btn btn--primary">Basic Heatmap</button>
          <button id="btn-corr-matrix" class="btn btn--primary">Correlation Matrix</button>
          <button id="btn-clustermap" class="btn btn--primary">Clustermap</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-heatmaps" width="800" height="500"></canvas>
        </div>

        <div class="info-card">
          <strong>Best Practices:</strong><br>
          • Always annotate with values: <code>annot=True</code><br>
          • Use diverging colormap for correlation: <code>cmap='coolwarm', center=0</code><br>
          • Mask upper/lower triangle: <code>mask=np.triu(np.ones_like(corr))</code><br>
          • Square cells: <code>square=True</code>
        </div>

        <div class="callout callout--insight">💡 Clustermap automatically clusters similar rows/columns together.</div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: Correlation Coefficients</h3>
          <p>Correlation heatmaps display the strength of linear relationships between variables, typically mapping the
            <strong>Pearson Correlation Coefficient ($r$)</strong> to a discrete color gradient hexbin:
          </p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; font-size: 1.1em; color: #e4e6eb;">
            $$ r = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}} $$
          </div>
          <p style="margin-bottom: 0;">For non-linear but monotonic relationships, you should switch pandas to use
            <strong>Spearman's Rank Correlation ($\rho$)</strong>, which mathematically converts raw values to ranks
            $R(x_i)$ before applying the same formula. Both map perfectly bounds of $[-1, 1]$.
          </p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>heatmaps.py - Correlation Matrix</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Load data and calculate correlation matrix
penguins = sns.load_dataset("penguins")
# Select only numerical columns for correlation
numerical_df = penguins.select_dtypes(include=[np.number]) 
corr = numerical_df.corr()

# Create a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))

plt.figure(figsize=(8, 6))

# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(
    corr, 
    mask=mask, 
    cmap='coolwarm', 
    vmax=1, vmin=-1, 
    center=0,
    square=True, 
    linewidths=.5, 
    annot=True, 
    fmt=".2f",
    cbar_kws={"shrink": .8}
)

plt.title("Penguin Feature Correlation")
plt.tight_layout()
plt.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 14. PLOTLY ================== -->
      <section id="plotly" class="topic-section">
        <h2>🚀 Plotly Express: Interactive Visualization</h2>
        <p>Plotly creates interactive, web-based visualizations with zoom, pan, hover tooltips, and more.</p>

        <div class="info-card">
          <strong>Why Plotly?</strong>
          <ul>
            <li>Interactive out of the box (zoom, pan, select)</li>
            <li>Hover tooltips with data details</li>
            <li>Export as HTML, PNG, or embed in dashboards</li>
            <li>Works in Jupyter, Streamlit, Dash</li>
            <li>plotly.express is the high-level API (like Seaborn for Matplotlib)</li>
          </ul>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-plotly" width="800" height="400"></canvas>
        </div>

        <div class="info-card">
          <strong>Common Functions:</strong><br>
          <code>px.scatter(df, x='x', y='y', color='category', size='value', hover_data=['name'])</code><br>
          <code>px.line(df, x='date', y='price', color='stock')</code><br>
          <code>px.bar(df, x='category', y='count', color='group', barmode='group')</code><br>
          <code>px.histogram(df, x='value', nbins=50, marginal='box')</code>
        </div>
      </section>

      <!-- ====================== 15. ANIMATIONS ================== -->
      <section id="animations" class="topic-section">
        <h2>🎬 Animated Visualizations</h2>
        <p>Add time dimension to your visualizations with animations.</p>

        <div class="form-group">
          <button id="btn-animate" class="btn btn--primary">▶ Play Animation</button>
          <button id="btn-stop" class="btn btn--primary">⏹ Stop</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-animation" width="800" height="400"></canvas>
        </div>

        <div class="info-card">
          <strong>Plotly Animation:</strong><br>
          <code>px.scatter(df, x='gdp', y='life_exp', animation_frame='year', animation_group='country', size='pop', color='continent')</code><br><br>

          <strong>Matplotlib Animation:</strong><br>
          <code>from matplotlib.animation import FuncAnimation</code><br>
          <code>ani = FuncAnimation(fig, update_func, frames=100, interval=50)</code>
        </div>

        <div class="callout callout--tip">✅ Hans Rosling's Gapminder is the classic example of animated scatter plots!
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>animation_example.py - Gapminder Scatter</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import plotly.express as px

df = px.data.gapminder()

# Plotly makes animations incredibly easy with two arguments:
# 'animation_frame' (the time dimension) and 'animation_group' (the entity)
fig = px.scatter(
    df, 
    x="gdpPercap", y="lifeExp", 
    animation_frame="year", animation_group="country",
    size="pop", color="continent", 
    hover_name="country",
    log_x=True, size_max=55, 
    range_x=[100,100000], range_y=[25,90],
    title="Global Development 1952 - 2007"
)

fig.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 16. DASHBOARDS ================== -->
      <section id="dashboards" class="topic-section">
        <h2>📱 Interactive Dashboards with Streamlit</h2>
        <p>Build interactive web apps for data exploration without web development experience.</p>

        <div class="info-card">
          <strong>Streamlit Basics:</strong><br>
          <code>streamlit run app.py</code><br><br>

          <code>import streamlit as st</code><br>
          <code>st.title("My Dashboard")</code><br>
          <code>st.slider("Select value", 0, 100, 50)</code><br>
          <code>st.selectbox("Choose", ["A", "B", "C"])</code><br>
          <code>st.plotly_chart(fig)</code>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-dashboard" width="800" height="400"></canvas>
        </div>

        <div class="callout callout--insight">💡 Streamlit auto-reruns when input changes - no callbacks needed!</div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>app.py - Minimal Streamlit Dashboard</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import streamlit as st
import pandas as pd
import plotly.express as px

# 1. Page Configuration
st.set_page_config(page_title="Sales Dashboard", layout="wide")
st.title("Interactive Sales Dashboard 📊")

# 2. Sidebar Filters
st.sidebar.header("Filters")
category = st.sidebar.selectbox("Select Category", ["Electronics", "Clothing", "Home"])
min_sales = st.sidebar.slider("Minimum Sales", 0, 1000, 200)

# Mock Data Generation
df = pd.DataFrame({
    'Date': pd.date_range(start='2023-01-01', periods=30),
    'Sales': [x * 10 for x in range(30)],
    'Category': [category] * 30
})
filtered_df = df[df['Sales'] >= min_sales]

# 3. Layout with Columns
col1, col2 = st.columns(2)

# KPI Metric
col1.metric("Total Filtered Sales", f"${filtered_df['Sales'].sum()}")

# 4. Insert Plotly Chart
fig = px.line(filtered_df, x='Date', y='Sales', title=f"{category} Sales Trend")
col2.plotly_chart(fig, use_container_width=True)</code></pre>
        </div>
      </section>

      <!-- ====================== 17. GEOSPATIAL ================== -->
      <section id="geospatial" class="topic-section">
        <h2>🗺️ Geospatial Visualization</h2>
        <p>Visualize geographic data with maps, choropleth, and point plots.</p>

        <div class="form-group">
          <button id="btn-choropleth" class="btn btn--primary">Choropleth Map</button>
          <button id="btn-scatter-geo" class="btn btn--primary">Scatter on Map</button>
          <button id="btn-heatmap-geo" class="btn btn--primary">Density Map</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-geo" width="800" height="500"></canvas>
        </div>

        <div class="info-card">
          <strong>Libraries:</strong><br>
          • <strong>Plotly:</strong> <code>px.choropleth(df, locations='country', color='value')</code><br>
          • <strong>Folium:</strong> Interactive Leaflet maps<br>
          • <strong>Geopandas + Matplotlib:</strong> Static maps with shapefiles<br>
          • <strong>Kepler.gl:</strong> Large-scale geospatial visualization
        </div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: Geospatial Math</h3>
          <p>Visualizing data on a map requires mathematically converting a 3D spherical Earth into 2D screen pixels.
            The <strong>Web Mercator Projection</strong> (used by Google Maps and Plotly) achieves this by preserving
            angles (conformal) but heavily distorting sizes near the poles:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; font-size: 1.1em; color: #e4e6eb;">
            $$ x = R \cdot \lambda \qquad y = R \ln\left[\tan\left(\frac{\pi}{4} + \frac{\varphi}{2}\right)\right] $$
          </div>
          <p style="margin-bottom: 0;">Furthermore, when calculating distances between two GPS coordinates (e.g., to
            color a density heatmap), you cannot use straight Euclidean distance $d = \sqrt{x^2+y^2}$. Advanced
            libraries compute the <strong>Haversine formula</strong> to find the true great-circle distance over the
            sphere.</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>geospatial.py - Plotly Choropleth</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import plotly.express as px

# Plotly includes built-in geospatial data
df = px.data.gapminder().query("year==2007")

# Create a choropleth map
# 'locations' takes ISO-3 country codes by default 
fig = px.choropleth(
    df, 
    locations="iso_alpha",   # Geopolitical boundaries
    color="lifeExp",         # Data to map to color
    hover_name="country",    # Tooltip label
    color_continuous_scale=px.colors.sequential.Plasma,
    title="Global Life Expectancy (2007)"
)

# Customize the map projection type
fig.update_geos(
    projection_type="orthographic", # "natural earth", "mercator", etc.
    showcoastlines=True, 
    coastlinecolor="DarkBlue"
)

fig.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 18. 3D PLOTS ================== -->
      <section id="3d-plots" class="topic-section">
        <h2>🎲 3D Visualization</h2>
        <p>Visualize three-dimensional relationships with surface plots, scatter plots, and more.</p>

        <div class="form-group">
          <button id="btn-3d-scatter" class="btn btn--primary">3D Scatter</button>
          <button id="btn-3d-surface" class="btn btn--primary">Surface Plot</button>
          <button id="btn-3d-wireframe" class="btn btn--primary">Wireframe</button>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-3d" width="800" height="500"></canvas>
        </div>

        <div class="callout callout--mistake">⚠️ 3D plots can obscure data. Often, multiple 2D views are more effective.
        </div>
        <div class="callout callout--tip">✅ Use Plotly for interactive 3D (rotate, zoom) instead of static Matplotlib
          3D.</div>

        <div class="info-card" style="margin-top: 20px; border-left-color: #9900ff;">
          <h3 style="margin-top: 0; color: #9900ff;">🧠 Under the Hood: 3D Perspective Projection Matrix</h3>
          <p>To render 3D data $(x, y, z)$ on a 2D screen browser, libraries like Plotly.js apply a <strong>Perspective
              Projection Matrix</strong>. This creates the optical illusion of depth by scaling $x$ and $y$ inversely
            with distance $z$:</p>
          <div
            style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: center; margin: 15px 0; overflow-x: auto; color: #e4e6eb;">
            $$ \begin{bmatrix} x' \\ y' \\ z' \\ w \end{bmatrix} = \begin{bmatrix} \frac{1}{\text{aspect} \cdot
            \tan(\frac{fov}{2})} & 0 & 0 & 0 \\ 0 & \frac{1}{\tan(\frac{fov}{2})} & 0 & 0 \\ 0 & 0 & \frac{f+n}{f-n} &
            \frac{-2fn}{f-n} \\ 0 & 0 & 1 & 0 \end{bmatrix} \begin{bmatrix} x \\ y \\ z \\ 1 \end{bmatrix} $$
          </div>
          <p style="margin-bottom: 0;">Once multiplied out, the final screen coordinates are $(x'/w, y'/w)$. When you
            rapidly drag to rotate a 3D Plotly graph, your browser's WebGL engine is recalculating this exact matrix
            millions of times per second to update the viewpoint mapping in real-time!</p>
        </div>

        <div class="code-block" style="margin-top: 20px;">
          <div class="code-header">
            <span>3d_plots.py - Interactive 3D Scatter</span>
            <button class="copy-btn" onclick="copyCode(this)">Copy</button>
          </div>
          <pre><code>import plotly.express as px

# Load Iris dataset
df = px.data.iris()

# Create interactive 3D scatter plot
fig = px.scatter_3d(
    df, 
    x='sepal_length', 
    y='sepal_width', 
    z='petal_width',
    color='species',
    size='petal_length', 
    size_max=18,
    symbol='species',
    opacity=0.7,
    title="Iris 3D Feature Space"
)

# Tight layout for 3D plot
fig.update_layout(margin=dict(l=0, r=0, b=0, t=40))
fig.show()</code></pre>
        </div>
      </section>

      <!-- ====================== 19. STORYTELLING ================== -->
      <section id="storytelling" class="topic-section">
        <h2>📖 Data Storytelling</h2>
        <p>Transform visualizations into compelling narratives that drive action.</p>

        <div class="info-card">
          <strong>The Data Storytelling Framework:</strong>
          <ol>
            <li><strong>Context:</strong> Why does this matter? Who is the audience?</li>
            <li><strong>Data:</strong> What insights did you discover?</li>
            <li><strong>Narrative:</strong> What's the storyline (beginning, middle, end)?</li>
            <li><strong>Visual:</strong> Which chart best supports the story?</li>
            <li><strong>Call to Action:</strong> What should the audience do?</li>
          </ol>
        </div>

        <div class="canvas-wrapper">
          <canvas id="canvas-storytelling" width="800" height="400"></canvas>
        </div>

        <h3>Design Principles</h3>
        <div class="info-card">
          <strong>Remove Clutter:</strong> Eliminate chartjunk, gridlines, borders<br>
          <strong>Focus Attention:</strong> Use color strategically (grey + accent)<br>
          <strong>Think Like a Designer:</strong> Alignment, white space, hierarchy<br>
          <strong>Tell a Story:</strong> Title = conclusion, not description<br>
          <strong>Bad:</strong> "Sales by Region"<br>
          <strong>Good:</strong> "West Region Sales Dropped 23% in Q4"
        </div>

        <div class="callout callout--insight">💡 "If you can't explain it simply, you don't understand it well enough."
          — Einstein</div>
        <div class="callout callout--tip">✅ Read "Storytelling with Data" by Cole Nussbaumer Knaflic</div>
      </section>

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