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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Matplotlib & Seaborn — Complete Graph Reference</title>
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font-size: 0.85rem;
}
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<!-- ── MPL: PAIRWISE / BASIC ─────────────────── -->
<div class="section-block" id="mpl-basic">
<div class="section-header">
<span class="section-pill pill-mpl">Matplotlib</span>
<h2 class="section-title">Pairwise & Basic Data</h2>
<span class="section-count">7 charts</span>
</div>
<div class="card-grid">
<!-- 1. Line Plot -->
<div class="card mpl" data-lib="mpl" data-cat="basic" data-name="line plot plt.plot">
<div class="card-top">
<span class="card-name mpl-color">plt.plot(x, y)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Connects data points with lines. The most fundamental chart for displaying trends, time series, or functional relationships between two continuous variables.</p>
<div class="card-meta">
<span class="meta-chip use">📈 Time Series</span>
<span class="meta-chip use">📉 Trends</span>
<span class="meta-chip use">🔢 Functions</span>
<span class="meta-chip">Stock prices</span>
<span class="meta-chip">Temperature over time</span>
<span class="meta-chip">Signal waveforms</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="imp">import</span> matplotlib.pyplot <span class="imp">as</span> <span class="var">plt</span>
<span class="imp">import</span> numpy <span class="imp">as</span> <span class="var">np</span>
<span class="var">x</span> = np.<span class="fn">linspace</span>(<span class="num">0</span>, <span class="num">10</span>, <span class="num">100</span>)
<span class="var">y</span> = np.<span class="fn">sin</span>(x)
plt.<span class="fn">plot</span>(x, y, color=<span class="str">'steelblue'</span>, linewidth=<span class="num">2</span>, label=<span class="str">'sin(x)'</span>)
plt.<span class="fn">xlabel</span>(<span class="str">'X'</span>); plt.<span class="fn">ylabel</span>(<span class="str">'Y'</span>)
plt.<span class="fn">title</span>(<span class="str">'Line Plot'</span>); plt.<span class="fn">legend</span>(); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- 2. Scatter -->
<div class="card mpl" data-lib="mpl" data-cat="basic" data-name="scatter plot plt.scatter">
<div class="card-top">
<span class="card-name mpl-color">plt.scatter(x, y)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Plots individual data points as markers. Ideal for revealing correlations, clusters, and outliers between two continuous variables without connecting them.</p>
<div class="card-meta">
<span class="meta-chip use">🔵 Correlation</span>
<span class="meta-chip use">🔍 Clustering</span>
<span class="meta-chip use">⚠ Outliers</span>
<span class="meta-chip">Height vs. Weight</span>
<span class="meta-chip">ML feature space</span>
<span class="meta-chip">Survey data</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">x</span> = np.<span class="fn">random.randn</span>(<span class="num">100</span>)
<span class="var">y</span> = <span class="num">2</span>*x + np.<span class="fn">random.randn</span>(<span class="num">100</span>)
<span class="var">colors</span> = np.<span class="fn">abs</span>(x) <span class="cm"># color by value</span>
plt.<span class="fn">scatter</span>(x, y, c=colors, cmap=<span class="str">'viridis'</span>,
s=<span class="num">60</span>, alpha=<span class="num">0.7</span>)
plt.<span class="fn">colorbar</span>(label=<span class="str">'Intensity'</span>)
plt.<span class="fn">title</span>(<span class="str">'Scatter Plot'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- 3. Bar Chart -->
<div class="card mpl" data-lib="mpl" data-cat="basic" data-name="bar chart plt.bar">
<div class="card-top">
<span class="card-name mpl-color">plt.bar(x, height)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Rectangular bars of height proportional to values. Perfect for comparing discrete categories or groups side-by-side. Use <code style="color:var(--mpl-primary)">plt.barh()</code> for horizontal bars.</p>
<div class="card-meta">
<span class="meta-chip use">📊 Comparison</span>
<span class="meta-chip use">🗂 Categories</span>
<span class="meta-chip">Sales by region</span>
<span class="meta-chip">Survey results</span>
<span class="meta-chip">Product performance</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">categories</span> = [<span class="str">'Q1'</span>, <span class="str">'Q2'</span>, <span class="str">'Q3'</span>, <span class="str">'Q4'</span>]
<span class="var">values</span> = [<span class="num">42</span>, <span class="num">58</span>, <span class="num">73</span>, <span class="num">91</span>]
plt.<span class="fn">bar</span>(categories, values, color=<span class="str">'#ff6b35'</span>,
edgecolor=<span class="str">'white'</span>, linewidth=<span class="num">0.5</span>)
plt.<span class="fn">title</span>(<span class="str">'Quarterly Sales'</span>); plt.<span class="fn">show</span>()
<span class="cm"># Horizontal: plt.barh(categories, values)</span></pre>
</div>
</div>
<!-- 4. Stem Plot -->
<div class="card mpl" data-lib="mpl" data-cat="basic" data-name="stem plot plt.stem discrete signal">
<div class="card-top">
<span class="card-name mpl-color">plt.stem(x, y)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Draws vertical lines (stems) from baseline to data points topped with markers. Excellent for discrete signal and digital data visualization where individual samples matter.</p>
<div class="card-meta">
<span class="meta-chip use">📡 Signal Processing</span>
<span class="meta-chip use">🔢 Discrete Sequences</span>
<span class="meta-chip">DSP samples</span>
<span class="meta-chip">Impulse responses</span>
<span class="meta-chip">Spectral analysis</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">n</span> = np.<span class="fn">arange</span>(<span class="num">0</span>, <span class="num">20</span>)
<span class="var">y</span> = np.<span class="fn">cos</span>(n * <span class="num">0.4</span>) * np.<span class="fn">exp</span>(-n * <span class="num">0.1</span>)
plt.<span class="fn">stem</span>(n, y, linefmt=<span class="str">'steelblue'</span>,
markerfmt=<span class="str">'o'</span>, basefmt=<span class="str">'gray'</span>)
plt.<span class="fn">title</span>(<span class="str">'Discrete Signal (Stem)'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- 5. Fill Between -->
<div class="card mpl" data-lib="mpl" data-cat="basic" data-name="fill between area chart plt.fill_between">
<div class="card-top">
<span class="card-name mpl-color">plt.fill_between(x, y1, y2)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Fills the area between two curves or between a curve and a baseline. Great for confidence intervals, uncertainty bands, and range highlighting.</p>
<div class="card-meta">
<span class="meta-chip use">📐 Confidence Intervals</span>
<span class="meta-chip use">🌡 Ranges</span>
<span class="meta-chip">Forecast uncertainty</span>
<span class="meta-chip">Min/Max bands</span>
<span class="meta-chip">Area charts</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">x</span> = np.<span class="fn">linspace</span>(<span class="num">0</span>, <span class="num">10</span>, <span class="num">200</span>)
<span class="var">y1</span> = np.<span class="fn">sin</span>(x) + <span class="num">0.5</span> <span class="cm"># upper bound</span>
<span class="var">y2</span> = np.<span class="fn">sin</span>(x) - <span class="num">0.5</span> <span class="cm"># lower bound</span>
plt.<span class="fn">fill_between</span>(x, y1, y2,
alpha=<span class="num">0.3</span>, color=<span class="str">'royalblue'</span>, label=<span class="str">'95% CI'</span>)
plt.<span class="fn">plot</span>(x, np.<span class="fn">sin</span>(x), color=<span class="str">'royalblue'</span>)
plt.<span class="fn">legend</span>(); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- 6. Stack Plot -->
<div class="card mpl" data-lib="mpl" data-cat="basic" data-name="stackplot stacked area plt.stackplot">
<div class="card-top">
<span class="card-name mpl-color">plt.stackplot(x, y)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Stacked area chart where multiple series are stacked on top of each other. Shows both individual contributions and the total over a continuous domain.</p>
<div class="card-meta">
<span class="meta-chip use">📦 Part-to-Whole</span>
<span class="meta-chip use">📅 Time Evolution</span>
<span class="meta-chip">Market share over time</span>
<span class="meta-chip">Resource allocation</span>
<span class="meta-chip">Revenue breakdown</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">x</span> = [<span class="num">2020</span>, <span class="num">2021</span>, <span class="num">2022</span>, <span class="num">2023</span>]
<span class="var">y1</span> = [<span class="num">30</span>, <span class="num">35</span>, <span class="num">40</span>, <span class="num">50</span>] <span class="cm"># Product A</span>
<span class="var">y2</span> = [<span class="num">20</span>, <span class="num">25</span>, <span class="num">30</span>, <span class="num">28</span>] <span class="cm"># Product B</span>
<span class="var">y3</span> = [<span class="num">10</span>, <span class="num">15</span>, <span class="num">12</span>, <span class="num">18</span>] <span class="cm"># Product C</span>
plt.<span class="fn">stackplot</span>(x, y1, y2, y3,
labels=[<span class="str">'A'</span>,<span class="str">'B'</span>,<span class="str">'C'</span>], alpha=<span class="num">0.8</span>)
plt.<span class="fn">legend</span>(loc=<span class="str">'upper left'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- 7. Stairs -->
<div class="card mpl" data-lib="mpl" data-cat="basic" data-name="stairs step plot plt.stairs">
<div class="card-top">
<span class="card-name mpl-color">plt.stairs(values)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Draws a step function connecting constant value segments. Useful for histograms without bars, cumulative distributions, and event-driven processes where values change in discrete steps.</p>
<div class="card-meta">
<span class="meta-chip use">📶 Step Functions</span>
<span class="meta-chip use">🔢 Piecewise Constant</span>
<span class="meta-chip">Histogram outline</span>
<span class="meta-chip">Queue length over time</span>
<span class="meta-chip">State machine history</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">vals</span> = [<span class="num">1</span>, <span class="num">3</span>, <span class="num">2</span>, <span class="num">5</span>, <span class="num">4</span>, <span class="num">6</span>]
<span class="var">edges</span> = np.<span class="fn">arange</span>(<span class="num">7</span>) <span class="cm"># bin edges</span>
plt.<span class="fn">stairs</span>(vals, edges, fill=<span class="kw">True</span>,
color=<span class="str">'teal'</span>, alpha=<span class="num">0.5</span>)
plt.<span class="fn">title</span>(<span class="str">'Staircase Plot'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
</div>
</div>
<!-- ── MPL: STATISTICAL DISTRIBUTIONS ──────────── -->
<div class="section-block" id="mpl-stats">
<div class="section-header">
<span class="section-pill pill-mpl">Matplotlib</span>
<h2 class="section-title">Statistical Distributions</h2>
<span class="section-count">9 charts</span>
</div>
<div class="card-grid">
<!-- Histogram -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="histogram plt.hist distribution">
<div class="card-top">
<span class="card-name mpl-color">plt.hist(x)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Divides data into bins and plots frequency counts as bars. The go-to tool for visualizing a variable's distribution, identifying skewness, peaks, and spread.</p>
<div class="card-meta">
<span class="meta-chip use">📊 Distribution Shape</span>
<span class="meta-chip use">🔍 Skewness</span>
<span class="meta-chip">Test scores</span>
<span class="meta-chip">Pixel intensities</span>
<span class="meta-chip">Measurement errors</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">data</span> = np.<span class="fn">random.normal</span>(<span class="num">0</span>, <span class="num">1</span>, <span class="num">1000</span>)
plt.<span class="fn">hist</span>(data, bins=<span class="num">30</span>, color=<span class="str">'steelblue'</span>,
edgecolor=<span class="str">'white'</span>, density=<span class="kw">True</span>)
plt.<span class="fn">xlabel</span>(<span class="str">'Value'</span>); plt.<span class="fn">ylabel</span>(<span class="str">'Density'</span>)
plt.<span class="fn">title</span>(<span class="str">'Histogram'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- Box Plot -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="box plot plt.boxplot quartile">
<div class="card-top">
<span class="card-name mpl-color">plt.boxplot(X)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Summarizes distribution using five statistics: minimum, Q1, median, Q3, maximum. Box whiskers show spread; dots mark outliers. Perfect for comparing distributions across groups.</p>
<div class="card-meta">
<span class="meta-chip use">📦 Five-number Summary</span>
<span class="meta-chip use">🔄 Group Comparison</span>
<span class="meta-chip">Salary by department</span>
<span class="meta-chip">A/B test results</span>
<span class="meta-chip">Quality control</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">data</span> = [np.<span class="fn">random.normal</span>(i, <span class="num">1</span>, <span class="num">100</span>)
<span class="kw">for</span> i <span class="kw">in</span> [<span class="num">0</span>, <span class="num">2</span>, <span class="num">4</span>]]
fig, ax = plt.<span class="fn">subplots</span>()
ax.<span class="fn">boxplot</span>(data, labels=[<span class="str">'A'</span>,<span class="str">'B'</span>,<span class="str">'C'</span>],
patch_artist=<span class="kw">True</span>,
boxprops=<span class="fn">dict</span>(facecolor=<span class="str">'lightblue'</span>))
plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- Error Bar -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="errorbar plt.errorbar confidence measurement error">
<div class="card-top">
<span class="card-name mpl-color">plt.errorbar(x, y, yerr, xerr)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Plots points with error bars indicating uncertainty or variability. Essential for scientific data where measurement precision or confidence intervals must be communicated.</p>
<div class="card-meta">
<span class="meta-chip use">⚗ Scientific Data</span>
<span class="meta-chip use">📏 Uncertainty</span>
<span class="meta-chip">Experimental results</span>
<span class="meta-chip">Survey margins</span>
<span class="meta-chip">Model predictions</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">x</span> = np.<span class="fn">arange</span>(<span class="num">1</span>, <span class="num">6</span>)
<span class="var">y</span> = [<span class="num">2.3</span>, <span class="num">3.1</span>, <span class="num">4.8</span>, <span class="num">3.5</span>, <span class="num">5.2</span>]
<span class="var">yerr</span> = [<span class="num">0.4</span>, <span class="num">0.3</span>, <span class="num">0.6</span>, <span class="num">0.5</span>, <span class="num">0.2</span>]
plt.<span class="fn">errorbar</span>(x, y, yerr=yerr, fmt=<span class="str">'o-'</span>,
capsize=<span class="num">5</span>, color=<span class="str">'royalblue'</span>,
ecolor=<span class="str">'gray'</span>, elinewidth=<span class="num">2</span>)
plt.<span class="fn">title</span>(<span class="str">'Error Bar Plot'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- Violin -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="violin plot plt.violinplot kde distribution">
<div class="card-top">
<span class="card-name mpl-color">plt.violinplot(D)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Combines KDE density estimation with a box plot. The symmetric violin shape shows the full probability distribution, revealing bimodality and detailed distributional structure.</p>
<div class="card-meta">
<span class="meta-chip use">🎻 Full Distribution</span>
<span class="meta-chip use">🔀 Bimodal Data</span>
<span class="meta-chip">Gene expression</span>
<span class="meta-chip">Income distribution</span>
<span class="meta-chip">Response times</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">data</span> = [np.<span class="fn">random.normal</span>(m, <span class="num">0.8</span>, <span class="num">200</span>)
<span class="kw">for</span> m <span class="kw">in</span> [<span class="num">0</span>, <span class="num">2</span>, <span class="num">4</span>, <span class="num">6</span>]]
fig, ax = plt.<span class="fn">subplots</span>()
<span class="var">vp</span> = ax.<span class="fn">violinplot</span>(data, showmeans=<span class="kw">True</span>)
<span class="kw">for</span> body <span class="kw">in</span> vp[<span class="str">'bodies'</span>]:
body.<span class="fn">set_alpha</span>(<span class="num">0.7</span>)
plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- Event Plot -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="event plot plt.eventplot spike raster">
<div class="card-top">
<span class="card-name mpl-color">plt.eventplot(D)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Displays collections of events as parallel vertical lines (like a raster plot). Standard in neuroscience for visualizing spike trains and in physics for particle events.</p>
<div class="card-meta">
<span class="meta-chip use">🧠 Neuroscience</span>
<span class="meta-chip use">⚡ Spike Trains</span>
<span class="meta-chip">Neuron firing</span>
<span class="meta-chip">Click timestamps</span>
<span class="meta-chip">Event logs</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="cm"># Simulate 4 neurons spiking</span>
<span class="var">spikes</span> = [np.<span class="fn">sort</span>(np.<span class="fn">random.uniform</span>(<span class="num">0</span>, <span class="num">1</span>, <span class="num">50</span>))
<span class="kw">for</span> _ <span class="kw">in</span> <span class="fn">range</span>(<span class="num">4</span>)]
plt.<span class="fn">eventplot</span>(spikes, orientation=<span class="str">'horizontal'</span>,
colors=[<span class="str">'r'</span>,<span class="str">'g'</span>,<span class="str">'b'</span>,<span class="str">'orange'</span>],
linewidths=<span class="num">1.5</span>)
plt.<span class="fn">title</span>(<span class="str">'Raster Plot'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- 2D Histogram -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="hist2d 2d histogram density plt.hist2d bivariate">
<div class="card-top">
<span class="card-name mpl-color">plt.hist2d(x, y)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">A 2D histogram that divides the x-y plane into rectangular bins and color-codes the count in each. Better than scatter for very large datasets with overplotting issues.</p>
<div class="card-meta">
<span class="meta-chip use">🗺 Joint Distribution</span>
<span class="meta-chip use">🔢 Large Datasets</span>
<span class="meta-chip">User click heatmaps</span>
<span class="meta-chip">Astrophysics data</span>
<span class="meta-chip">GPS density</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">x</span> = np.<span class="fn">random.normal</span>(<span class="num">0</span>, <span class="num">1</span>, <span class="num">10000</span>)
<span class="var">y</span> = np.<span class="fn">random.normal</span>(<span class="num">0</span>, <span class="num">1</span>, <span class="num">10000</span>)
plt.<span class="fn">hist2d</span>(x, y, bins=<span class="num">50</span>, cmap=<span class="str">'plasma'</span>)
plt.<span class="fn">colorbar</span>(label=<span class="str">'Count'</span>)
plt.<span class="fn">title</span>(<span class="str">'2D Histogram'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- Hexbin -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="hexbin plt.hexbin hex bin density large data">
<div class="card-top">
<span class="card-name mpl-color">plt.hexbin(x, y, C)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Aggregates scatter data into hexagonal bins, color-coding each by count or custom aggregation. Hexagons tile without gaps and are perceptually less biased than squares for spatial data.</p>
<div class="card-meta">
<span class="meta-chip use">🔷 Spatial Density</span>
<span class="meta-chip use">📍 Geospatial</span>
<span class="meta-chip">City density maps</span>
<span class="meta-chip">Transaction hotspots</span>
<span class="meta-chip">Million-point scatter</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">x</span> = np.<span class="fn">random.randn</span>(<span class="num">50000</span>)
<span class="var">y</span> = np.<span class="fn">random.randn</span>(<span class="num">50000</span>)
<span class="var">hb</span> = plt.<span class="fn">hexbin</span>(x, y, gridsize=<span class="num">30</span>,
cmap=<span class="str">'inferno'</span>)
plt.<span class="fn">colorbar</span>(hb, label=<span class="str">'Count'</span>)
plt.<span class="fn">title</span>(<span class="str">'Hexbin Plot'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- Pie Chart -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="pie chart plt.pie proportion percentage">
<div class="card-top">
<span class="card-name mpl-color">plt.pie(x)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Circular chart divided into slices proportional to values. Best for showing part-to-whole relationships with a small number of categories (≤ 6). Use sparingly; bar charts are often clearer.</p>
<div class="card-meta">
<span class="meta-chip use">🥧 Part-to-Whole</span>
<span class="meta-chip use">📊 Proportions</span>
<span class="meta-chip">Market share</span>
<span class="meta-chip">Budget allocation</span>
<span class="meta-chip">Survey responses</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">sizes</span> = [<span class="num">35</span>, <span class="num">25</span>, <span class="num">20</span>, <span class="num">15</span>, <span class="num">5</span>]
<span class="var">labels</span> = [<span class="str">'Python'</span>,<span class="str">'JS'</span>,<span class="str">'Java'</span>,<span class="str">'C++'</span>,<span class="str">'Other'</span>]
<span class="var">explode</span> = [<span class="num">0.05</span>]*<span class="num">5</span>
plt.<span class="fn">pie</span>(sizes, labels=labels, explode=explode,
autopct=<span class="str">'%1.1f%%'</span>, startangle=<span class="num">90</span>)
plt.<span class="fn">title</span>(<span class="str">'Language Popularity'</span>); plt.<span class="fn">show</span>()</pre>
</div>
</div>
<!-- ECDF -->
<div class="card mpl" data-lib="mpl" data-cat="stats" data-name="ecdf empirical cumulative distribution function plt.ecdf">
<div class="card-top">
<span class="card-name mpl-color">plt.ecdf(x)</span>
<span class="card-tag tag-mpl">Matplotlib</span>
</div>
<p class="card-desc">Empirical Cumulative Distribution Function. Shows the proportion of data below any given value without binning assumptions. Useful for comparing two distributions directly.</p>
<div class="card-meta">
<span class="meta-chip use">📈 CDF</span>
<span class="meta-chip use">🔀 Distribution Comparison</span>
<span class="meta-chip">Latency percentiles</span>
<span class="meta-chip">Test score ranks</span>
<span class="meta-chip">Quality thresholds</span>
</div>
<div class="card-code-wrap">
<div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">data</span> = np.<span class="fn">random.normal</span>(<span class="num">0</span>, <span class="num">1</span>, <span class="num">500</span>)
<span class="cm"># Available since matplotlib 3.8+</span>
plt.<span class="fn">ecdf</span>(data, label=<span class="str">'Group A'</span>)
plt.<span class="fn">xlabel</span>(<span class="str">'Value'</span>)
plt.<span class="fn">ylabel</span>(<span class="str">'Cumulative Probability'</span>)
plt.<span class="fn">legend</span>(); plt.<span class="fn">show</span>()</pre>
</div>
</div>
</div>
</div>
<!-- ── MPL: GRIDDED DATA ───────────────────────── -->
<div class="section-block" id="mpl-grid">
<div class="section-header">
<span class="section-pill pill-mpl">Matplotlib</span>
<h2 class="section-title">Gridded Data</h2>
<span class="section-count">7 charts</span>
</div>
<div class="card-grid">
<div class="card mpl" data-lib="mpl" data-cat="grid" data-name="imshow image heatmap plt.imshow matrix">
<div class="card-top"><span class="card-name mpl-color">plt.imshow(Z)</span><span class="card-tag tag-mpl">Matplotlib</span></div>
<p class="card-desc">Displays matrix or image data as a colored pixel grid. The primary function for showing images, correlation matrices, confusion matrices, and any 2D array data.</p>
<div class="card-meta"><span class="meta-chip use">🖼 Images</span><span class="meta-chip use">🔢 Matrices</span><span class="meta-chip">Confusion matrix</span><span class="meta-chip">Correlation matrix</span><span class="meta-chip">Image processing</span></div>
<div class="card-code-wrap"><div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">Z</span> = np.<span class="fn">random.rand</span>(<span class="num">10</span>, <span class="num">10</span>)
plt.<span class="fn">imshow</span>(Z, cmap=<span class="str">'viridis'</span>,
interpolation=<span class="str">'nearest'</span>)
plt.<span class="fn">colorbar</span>(); plt.<span class="fn">title</span>(<span class="str">'imshow'</span>); plt.<span class="fn">show</span>()</pre></div>
</div>
<div class="card mpl" data-lib="mpl" data-cat="grid" data-name="pcolormesh pseudo color mesh plt.pcolormesh grid">
<div class="card-top"><span class="card-name mpl-color">plt.pcolormesh(X, Y, Z)</span><span class="card-tag tag-mpl">Matplotlib</span></div>
<p class="card-desc">Creates a pseudocolor plot of a 2D array on a non-uniform grid. Like imshow but works on arbitrary (possibly non-rectangular) coordinate grids. Preferred over pcolor for performance.</p>
<div class="card-meta"><span class="meta-chip use">🌍 Geo Grids</span><span class="meta-chip use">🌡 Field Maps</span><span class="meta-chip">Temperature fields</span><span class="meta-chip">Simulation output</span><span class="meta-chip">Radar data</span></div>
<div class="card-code-wrap"><div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">X</span>, <span class="var">Y</span> = np.<span class="fn">meshgrid</span>(np.<span class="fn">linspace</span>(-<span class="num">3</span>,<span class="num">3</span>,<span class="num">50</span>),
np.<span class="fn">linspace</span>(-<span class="num">3</span>,<span class="num">3</span>,<span class="num">50</span>))
<span class="var">Z</span> = np.<span class="fn">sin</span>(X) * np.<span class="fn">cos</span>(Y)
plt.<span class="fn">pcolormesh</span>(X, Y, Z, cmap=<span class="str">'RdBu_r'</span>,
shading=<span class="str">'auto'</span>)
plt.<span class="fn">colorbar</span>(); plt.<span class="fn">show</span>()</pre></div>
</div>
<div class="card mpl" data-lib="mpl" data-cat="grid" data-name="contour contour lines plt.contour topographic">
<div class="card-top"><span class="card-name mpl-color">plt.contour(X, Y, Z)</span><span class="card-tag tag-mpl">Matplotlib</span></div>
<p class="card-desc">Draws iso-value contour lines on a 2D scalar field. Classic for topographic maps, pressure isobars in meteorology, and level sets in optimization landscapes.</p>
<div class="card-meta"><span class="meta-chip use">🗺 Topography</span><span class="meta-chip use">📈 Level Sets</span><span class="meta-chip">Terrain maps</span><span class="meta-chip">Weather isobars</span><span class="meta-chip">Optimization surface</span></div>
<div class="card-code-wrap"><div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">X</span>, <span class="var">Y</span> = np.<span class="fn">meshgrid</span>(np.<span class="fn">linspace</span>(-<span class="num">2</span>,<span class="num">2</span>,<span class="num">100</span>),
np.<span class="fn">linspace</span>(-<span class="num">2</span>,<span class="num">2</span>,<span class="num">100</span>))
<span class="var">Z</span> = X**<span class="num">2</span> + Y**<span class="num">2</span>
<span class="var">cs</span> = plt.<span class="fn">contour</span>(X, Y, Z, levels=<span class="num">10</span>)
plt.<span class="fn">clabel</span>(cs, inline=<span class="kw">True</span>); plt.<span class="fn">show</span>()</pre></div>
</div>
<div class="card mpl" data-lib="mpl" data-cat="grid" data-name="contourf filled contour plt.contourf">
<div class="card-top"><span class="card-name mpl-color">plt.contourf(X, Y, Z)</span><span class="card-tag tag-mpl">Matplotlib</span></div>
<p class="card-desc">Filled contour plot — same as contour but with solid color fills between levels. Ideal for showing continuous field strength visually; widely used in climate and fluid simulations.</p>
<div class="card-meta"><span class="meta-chip use">🌈 Field Strength</span><span class="meta-chip use">🌊 Fluid Flow</span><span class="meta-chip">Pressure maps</span><span class="meta-chip">Heat diffusion</span><span class="meta-chip">ML decision regions</span></div>
<div class="card-code-wrap"><div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">Z</span> = np.<span class="fn">sin</span>(X) + np.<span class="fn">cos</span>(Y)
plt.<span class="fn">contourf</span>(X, Y, Z, levels=<span class="num">20</span>,
cmap=<span class="str">'coolwarm'</span>)
plt.<span class="fn">colorbar</span>(label=<span class="str">'Amplitude'</span>)
plt.<span class="fn">title</span>(<span class="str">'Filled Contour'</span>); plt.<span class="fn">show</span>()</pre></div>
</div>
<div class="card mpl" data-lib="mpl" data-cat="grid" data-name="quiver vector field arrow plt.quiver">
<div class="card-top"><span class="card-name mpl-color">plt.quiver(X, Y, U, V)</span><span class="card-tag tag-mpl">Matplotlib</span></div>
<p class="card-desc">Draws arrows on a grid to represent a 2D vector field. Direction and optionally magnitude are encoded. Used in fluid dynamics, electromagnetics, and gradient visualization.</p>
<div class="card-meta"><span class="meta-chip use">➡ Vector Fields</span><span class="meta-chip use">🌊 Fluid Dynamics</span><span class="meta-chip">Wind vectors</span><span class="meta-chip">Gradient descent</span><span class="meta-chip">EM fields</span></div>
<div class="card-code-wrap"><div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">X</span>, <span class="var">Y</span> = np.<span class="fn">meshgrid</span>(np.<span class="fn">arange</span>(-<span class="num">2</span>,<span class="num">3</span>),
np.<span class="fn">arange</span>(-<span class="num">2</span>,<span class="num">3</span>))
<span class="var">U</span> = -Y; <span class="var">V</span> = X <span class="cm"># circular field</span>
plt.<span class="fn">quiver</span>(X, Y, U, V, color=<span class="str">'teal'</span>)
plt.<span class="fn">title</span>(<span class="str">'Quiver Plot'</span>); plt.<span class="fn">show</span>()</pre></div>
</div>
<div class="card mpl" data-lib="mpl" data-cat="grid" data-name="streamplot streamline plt.streamplot flow">
<div class="card-top"><span class="card-name mpl-color">plt.streamplot(X, Y, U, V)</span><span class="card-tag tag-mpl">Matplotlib</span></div>
<p class="card-desc">Draws continuous streamlines following a 2D vector field. Unlike quiver, streamlines show flow paths rather than local arrows, making global flow patterns more legible.</p>
<div class="card-meta"><span class="meta-chip use">🌊 Flow Patterns</span><span class="meta-chip use">💨 Wind/Fluid</span><span class="meta-chip">Ocean currents</span><span class="meta-chip">Aerodynamics</span><span class="meta-chip">Magnetic field lines</span></div>
<div class="card-code-wrap"><div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">Y</span>, <span class="var">X</span> = np.<span class="fn">mgrid</span>[-<span class="num">3</span>:<span class="num">3</span>:<span class="num">100j</span>, -<span class="num">3</span>:<span class="num">3</span>:<span class="num">100j</span>]
<span class="var">U</span> = -<span class="num">1</span> - X**<span class="num">2</span> + Y
<span class="var">V</span> = <span class="num">1</span> + X - Y**<span class="num">2</span>
<span class="var">speed</span> = np.<span class="fn">sqrt</span>(U*U + V*V)
plt.<span class="fn">streamplot</span>(X, Y, U, V,
color=speed, cmap=<span class="str">'autumn'</span>); plt.<span class="fn">show</span>()</pre></div>
</div>
<div class="card mpl" data-lib="mpl" data-cat="grid" data-name="barbs wind barbs plt.barbs meteorology">
<div class="card-top"><span class="card-name mpl-color">plt.barbs(X, Y, U, V)</span><span class="card-tag tag-mpl">Matplotlib</span></div>
<p class="card-desc">Meteorological wind barb symbols that encode both wind direction and speed using a flag-and-feather notation. Standard in weather maps and atmospheric science.</p>
<div class="card-meta"><span class="meta-chip use">🌤 Meteorology</span><span class="meta-chip use">💨 Wind Speed/Direction</span><span class="meta-chip">Weather maps</span><span class="meta-chip">Aviation charts</span><span class="meta-chip">Climate models</span></div>
<div class="card-code-wrap"><div class="code-bar"><span class="code-dots"><span class="dot dot-r"></span><span class="dot dot-y"></span><span class="dot dot-g"></span></span><span>python</span></div>
<pre class="code-block"><span class="var">X</span>, <span class="var">Y</span> = np.<span class="fn">meshgrid</span>(np.<span class="fn">arange</span>(<span class="num">0</span>,<span class="num">5</span>),
np.<span class="fn">arange</span>(<span class="num">0</span>,<span class="num">5</span>))
<span class="var">U</span> = np.<span class="fn">random.uniform</span>(-<span class="num">20</span>,<span class="num">20</span>,X.shape)
<span class="var">V</span> = np.<span class="fn">random.uniform</span>(-<span class="num">20</span>,<span class="num">20</span>,Y.shape)
plt.<span class="fn">barbs</span>(X, Y, U, V)
plt.<span class="fn">title</span>(<span class="str">'Wind Barbs'</span>); plt.<span class="fn">show</span>()</pre></div>
</div>
</div>
</div>