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.nojekyll

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changelog.html

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<script src="site_libs/quarto-html/tippy.umd.min.js"></script>
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<link href="site_libs/quarto-html/quarto-syntax-highlighting-629432c7d29b615b3888e3cdfd62b7a9.css" rel="stylesheet" id="quarto-text-highlighting-styles">
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<script src="site_libs/bootstrap/bootstrap.min.js"></script>
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<link href="site_libs/bootstrap/bootstrap-3095cb3133064099da2b6c1e837d828f.min.css" rel="stylesheet" append-hash="true" id="quarto-bootstrap" data-mode="light">
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development.html

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<script src="site_libs/quarto-html/tippy.umd.min.js"></script>
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<script src="site_libs/bootstrap/bootstrap.min.js"></script>
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<link href="site_libs/bootstrap/bootstrap-3095cb3133064099da2b6c1e837d828f.min.css" rel="stylesheet" append-hash="true" id="quarto-bootstrap" data-mode="light">
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<h2 class="anchored" data-anchor-id="debug-mode">Debug mode</h2>
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<p>“Debug mode” outputs the computed JavaScript plot structure (the one passed to <code>Plot.plot</code>).</p>
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<p>To activate it, add <code>debug=True</code> when using <code>Plot.plot()</code> or when creating your plot generator object:</p>
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<div id="4ffc2122" class="cell" data-execution_count="1">
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<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>op <span class="op">=</span> Obsplot(debug<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>If using the <code>widget</code> renderer, debug output will be displayed in JavaScript console. With the <code>jsdom</code> renderer, it will be displayed directly in the notebook.</p>

gallery_geo.html

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<h2 class="anchored" data-anchor-id="geo-mark">Geo mark</h2>
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<p>The <a href="https://observablehq.com/@observablehq/plot-geo?collection=@observablehq/plot">Geo mark</a> allows to draw geographic features such as points, lines and polygons. These marks data are passed as GeoJSON.</p>
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<p>This allows to create choropleth maps such as the following:</p>
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<div id="c1e1d261" class="cell" data-execution_count="2">
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<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pyobsplot <span class="im">import</span> Obsplot, Plot</span>
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<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> geopandas <span class="im">as</span> gpd</span>
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<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
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</div>
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<p>Of course other marks can be used in conjunction with geo marks. This example represents the density of Walmarts supermarkets and is taken from the <a href="https://observablehq.com/@observablehq/plot-mapping?collection=@observablehq/plot">Mapping notebook</a>.</p>
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<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load US states from TopoJson with geopandas and convert to GeoJson</span></span>
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<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>states <span class="op">=</span> gpd.read_file(<span class="st">"data/us-counties-10m.json"</span>, layer<span class="op">=</span><span class="st">"states"</span>).to_json()</span>
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<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>nation <span class="op">=</span> gpd.read_file(<span class="st">"data/us-counties-10m.json"</span>, layer<span class="op">=</span><span class="st">"nation"</span>).to_json()</span>
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<h2 class="anchored" data-anchor-id="raster-mark">Raster mark</h2>
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<p>The <a href="https://observablehq.com/@observablehq/plot-raster?collection=@observablehq/plot">Raster mark</a> creates an image from spatial data.</p>
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<p>Plot allows to do different type of spatial interpolations, such as <em>nearest</em>, which draws voronoi cells around values:</p>
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<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> polars <span class="im">as</span> pl</span>
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<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>ca55 <span class="op">=</span> pl.read_csv(<span class="st">"data/ca55-south.csv"</span>)</span>
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</div>
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<p>Or the more recent random walk interpolation:</p>
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<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>flare_map(<span class="st">"random-walk"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div class="cell-output cell-output-display">
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<figure class="plot-d6a7b5-figure figure" style="max-width: initial; padding: 0px 5px 5px 5px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);" typstbg="#FFFFFF" typstfg="#000000" typstcaption="#777777"><svg class="plot-d6a7b5" fill="currentColor" font-family="system-ui, sans-serif" font-size="10" text-anchor="middle" width="640" height="500" viewbox="0 0 640 500" xmlns="http://www.w3.org/2000/svg" xlink="http://www.w3.org/1999/xlink" style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);"><style>:where(.plot-d6a7b5) {

gallery_interaction.html

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gallery_marks.html

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<section id="cell-mark" class="level2">
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<p>The following chart, taken from the <a href="https://observablehq.com/@observablehq/plot-cell?collection=@observablehq/plot">Cell Mark notebook</a>, shows the rating of every episode of <em>The Simpsons</em>.</p>
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<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> polars <span class="im">as</span> pl</span>
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<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pyobsplot <span class="im">import</span> Plot, d3, Math, js</span>
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<h2 class="anchored" data-anchor-id="faceting">Faceting</h2>
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<p><em>Faceting</em> allows to easily produce multiple plots by partitioning data. The following facet plot is taken from the <a href="https://observablehq.com/@observablehq/plot-facets?collection=@observablehq/plot">Facets notebook</a>.</p>
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<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>penguins <span class="op">=</span> pl.read_csv(<span class="st">"data/penguins.csv"</span>)</span>
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<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>Plot.plot(</span>
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<h2 class="anchored" data-anchor-id="arrow-mark">Arrow mark</h2>
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<p>Some marks allow for nice representation of complex data. The following plot, taken from the <a href="https://observablehq.com/@observablehq/plot-arrow?collection=@observablehq/plot">Arrow mark notebook</a>, shows the evolution of inequality and population in various U.S. cities.</p>
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<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>metros <span class="op">=</span> pl.read_csv(<span class="st">"data/metros.csv"</span>)</span>
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<h2 class="anchored" data-anchor-id="contour-mark">Contour mark</h2>
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<p>The contour mark allows for nice representations of spatial data. The following example taken from the <a href="https://observablehq.com/@observablehq/plot-contour?collection=@observablehq/plot">Contour mark notebook</a> shows water vapor data from november 2022 (note that data processing and plot code are slightly modified to adapt to <code>pyobsplot</code> specificities).</p>
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<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>vapor <span class="op">=</span> (</span>
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<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> pl.read_csv(<span class="st">"data/vapor.csv"</span>, has_header<span class="op">=</span><span class="va">False</span>, null_values<span class="op">=</span><span class="st">"99999.0"</span>)</span>
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<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> .transpose()</span>

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<link href="site_libs/bootstrap/bootstrap-3095cb3133064099da2b6c1e837d828f.min.css" rel="stylesheet" append-hash="true" id="quarto-bootstrap" data-mode="light">
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<p>The light theme produces plots with a white background and a black foreground color. This is the default theme:</p>
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<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> polars <span class="im">as</span> pl</span>
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<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pyobsplot <span class="im">import</span> Obsplot, Plot, d3, js</span>
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<p>The dark theme produces plots with a black background and a white foreground color.</p>
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<p>The “current” theme uses a transparent background and a <code>currentColor</code> foreground, so it should keep the current color theme:</p>
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<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>Plot.plot(</span>
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<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> {</span>
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<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="st">"y"</span>: {<span class="st">"grid"</span>: <span class="va">True</span>},</span>

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