diff --git a/docs/index.html b/docs/index.html
index 8f181e5..6cf6aa7 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -1,8 +1,306 @@
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-  <head>      
-    <title>Redirect to index.html</title>      
-    <meta http-equiv="refresh" content="0;URL='index.html'" />    
-  </head>    
-  <body> 
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-</html>     
+<!DOCTYPE html>
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+
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+<meta name="generator" content="quarto-1.2.475">
+
+<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
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+span.smallcaps{font-variant: small-caps;}
+div.columns{display: flex; gap: min(4vw, 1.5em);}
+div.column{flex: auto; overflow-x: auto;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+ul.task-list li input[type="checkbox"] {
+  width: 0.8em;
+  margin: 0 0.8em 0.2em -1.6em;
+  vertical-align: middle;
+}
+</style>
+
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+<meta name="quarto:offset" content="./">
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+  "limit": 20,
+  "language": {
+    "search-no-results-text": "No results",
+    "search-matching-documents-text": "matching documents",
+    "search-copy-link-title": "Copy link to search",
+    "search-hide-matches-text": "Hide additional matches",
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+    "search-detached-cancel-button-title": "Cancel",
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+    <div class="container-fluid d-flex justify-content-between">
+      <h1 class="quarto-secondary-nav-title">Noj Documentation</h1>
+      <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
+        <i class="bi bi-chevron-right"></i>
+      </button>
+    </div>
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+<!-- content -->
+<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
+<!-- sidebar -->
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+    <div class="pt-lg-2 mt-2 text-left sidebar-header">
+    <div class="sidebar-title mb-0 py-0">
+      <a href="./">Noj Documentation</a> 
+    </div>
+      </div>
+      <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="sidebar-search">
+        <div id="quarto-search" class="" title="Search"></div>
+        </div>
+      </div>
+    <div class="sidebar-menu-container"> 
+    <ul class="list-unstyled mt-1">
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./index.html" class="sidebar-item-text sidebar-link active"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Preface</span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./intro/visualization/index.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Visualization</span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./intro/image/index.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Turning tensors into images</span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./intro/python/index.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Python</span></a>
+  </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./intro/datasets/index.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Datasets</span></a>
+  </div>
+</li>
+    </ul>
+    </div>
+</nav>
+<!-- margin-sidebar -->
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+        <nav id="TOC" role="doc-toc" class="toc-active">
+    <h2 id="toc-title">Table of contents</h2>
+   
+  <ul>
+  <li><a href="#preface" id="toc-preface" class="nav-link active" data-scroll-target="#preface"><span class="toc-section-number">1</span>  Preface</a></li>
+  </ul>
+</nav>
+    </div>
+<!-- main -->
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title d-none d-lg-block">Noj Documentation</h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+  
+    
+  </div>
+  
+
+</header>
+
+<section id="preface" class="level1" data-number="1">
+<h1 data-number="1"><span class="header-section-number">1</span> Preface</h1>
+
+
+</section>
+
+</main> <!-- /main -->
+<script id="quarto-html-after-body" type="application/javascript">
+window.document.addEventListener("DOMContentLoaded", function (event) {
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+      const id = href.replace(/^#\/?/, "");
+      const note = window.document.getElementById(id);
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+      }
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+    const citeInfo = findCites(ref);
+    if (citeInfo) {
+      tippyHover(citeInfo.el, function() {
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+          var citeDiv = window.document.createElement('div');
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+          citeDiv.classList.add('csl-entry');
+          var biblioDiv = window.document.getElementById('ref-' + cite);
+          if (biblioDiv) {
+            citeDiv.innerHTML = biblioDiv.innerHTML;
+          }
+          popup.appendChild(citeDiv);
+        });
+        return popup.innerHTML;
+      });
+    }
+  }
+});
+</script>
+<nav class="page-navigation">
+  <div class="nav-page nav-page-previous">
+  </div>
+  <div class="nav-page nav-page-next">
+      <a href="./intro/visualization/index.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Visualization</span></span> <i class="bi bi-arrow-right-short"></i>
+      </a>
+  </div>
+</nav>
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\ No newline at end of file
diff --git a/docs/intro/datasets/index.html b/docs/intro/datasets/index.html
index 5e8b678..2f8ab3e 100644
--- a/docs/intro/datasets/index.html
+++ b/docs/intro/datasets/index.html
@@ -157,7 +157,7 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">5</span>&nbs
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../index.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">index.html</span></a>
+  <a href="../../index.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Noj Documentation</span></a>
   </div>
 </li>
         <li class="sidebar-item">
diff --git a/docs/intro/visualization/index.html b/docs/intro/visualization/index.html
index 6ed4251..ff33433 100644
--- a/docs/intro/visualization/index.html
+++ b/docs/intro/visualization/index.html
@@ -158,7 +158,7 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">2</span>&nbs
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../index.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">index.html</span></a>
+  <a href="../../index.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Preface</span></a>
   </div>
 </li>
         <li class="sidebar-item">
@@ -287,7 +287,7 @@ <h3 data-number="2.1.1" class="anchored" data-anchor-id="a-simple-plot"><span cl
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>                     {<span class="at">:MSIZE</span> <span class="dv">200</span>}))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div>
-<script>vegaEmbed(document.currentScript.parentElement, {"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"circle","size":200,"tooltip":true},"width":400,"background":"floralwhite","height":300,"data":{"values":"x,y\n0,0.4206537650583564\n1,0.6724796007167885\n2,0.7050347862061456\n3,0.8930385822551181\n4,1.306600670637002\n5,1.268862423764611\n6,0.831122264403631\n7,0.6807856685712571\n8,1.144596786276848\n9,0.7593255523174587\n10,1.1178754159346975\n11,0.7665958706099416\n12,0.44430773909612997\n13,0.31329714924493024\n14,0.7759573865477782\n15,1.252553019261778\n16,1.4318891963340379\n17,1.264678201658897\n18,1.1543653608205222\n19,1.178266045823578\n","format":{"type":"csv"}}});</script>
+<script>vegaEmbed(document.currentScript.parentElement, {"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"circle","size":200,"tooltip":true},"width":400,"background":"floralwhite","height":300,"data":{"values":"x,y\n0,0.33499630870162345\n1,0.14865499282967176\n2,0.4175140694174997\n3,8.544534747053634E-4\n4,0.21611694980290996\n5,-0.01124169329133462\n6,0.21686893895818826\n7,-0.015952354498198096\n8,-0.16409827162465285\n9,-0.35092257188323295\n10,-0.5890835621814955\n11,-1.0199885792681167\n12,-0.8971587522753082\n13,-0.46339169646556255\n14,-0.5623134576577914\n15,-0.14283599926419188\n16,-0.5580077556027917\n17,-0.20738217969612172\n18,-0.059989600225636375\n19,-0.20147573893522097\n","format":{"type":"csv"}}});</script>
 </div>
 <p>Let us look inside the resulting vega-lite space. We can see the dataset is included as CSV:</p>
 <div class="sourceClojure">
@@ -306,7 +306,7 @@ <h3 data-number="2.1.1" class="anchored" data-anchor-id="a-simple-plot"><span cl
 <span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> <span class="at">:height</span> <span class="dv">300</span>,</span>
 <span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> <span class="at">:data</span></span>
 <span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a> {<span class="at">:values</span></span>
-<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>  <span class="st">"x,y</span><span class="sc">\n</span><span class="st">0,0.4206537650583564</span><span class="sc">\n</span><span class="st">1,0.6724796007167885</span><span class="sc">\n</span><span class="st">2,0.7050347862061456</span><span class="sc">\n</span><span class="st">3,0.8930385822551181</span><span class="sc">\n</span><span class="st">4,1.306600670637002</span><span class="sc">\n</span><span class="st">5,1.268862423764611</span><span class="sc">\n</span><span class="st">6,0.831122264403631</span><span class="sc">\n</span><span class="st">7,0.6807856685712571</span><span class="sc">\n</span><span class="st">8,1.144596786276848</span><span class="sc">\n</span><span class="st">9,0.7593255523174587</span><span class="sc">\n</span><span class="st">10,1.1178754159346975</span><span class="sc">\n</span><span class="st">11,0.7665958706099416</span><span class="sc">\n</span><span class="st">12,0.44430773909612997</span><span class="sc">\n</span><span class="st">13,0.31329714924493024</span><span class="sc">\n</span><span class="st">14,0.7759573865477782</span><span class="sc">\n</span><span class="st">15,1.252553019261778</span><span class="sc">\n</span><span class="st">16,1.4318891963340379</span><span class="sc">\n</span><span class="st">17,1.264678201658897</span><span class="sc">\n</span><span class="st">18,1.1543653608205222</span><span class="sc">\n</span><span class="st">19,1.178266045823578</span><span class="sc">\n</span><span class="st">"</span>,</span>
+<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>  <span class="st">"x,y</span><span class="sc">\n</span><span class="st">0,0.33499630870162345</span><span class="sc">\n</span><span class="st">1,0.14865499282967176</span><span class="sc">\n</span><span class="st">2,0.4175140694174997</span><span class="sc">\n</span><span class="st">3,8.544534747053634E-4</span><span class="sc">\n</span><span class="st">4,0.21611694980290996</span><span class="sc">\n</span><span class="st">5,-0.01124169329133462</span><span class="sc">\n</span><span class="st">6,0.21686893895818826</span><span class="sc">\n</span><span class="st">7,-0.015952354498198096</span><span class="sc">\n</span><span class="st">8,-0.16409827162465285</span><span class="sc">\n</span><span class="st">9,-0.35092257188323295</span><span class="sc">\n</span><span class="st">10,-0.5890835621814955</span><span class="sc">\n</span><span class="st">11,-1.0199885792681167</span><span class="sc">\n</span><span class="st">12,-0.8971587522753082</span><span class="sc">\n</span><span class="st">13,-0.46339169646556255</span><span class="sc">\n</span><span class="st">14,-0.5623134576577914</span><span class="sc">\n</span><span class="st">15,-0.14283599926419188</span><span class="sc">\n</span><span class="st">16,-0.5580077556027917</span><span class="sc">\n</span><span class="st">17,-0.20738217969612172</span><span class="sc">\n</span><span class="st">18,-0.059989600225636375</span><span class="sc">\n</span><span class="st">19,-0.20147573893522097</span><span class="sc">\n</span><span class="st">"</span>,</span>
 <span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a>  <span class="at">:format</span> {<span class="at">:type</span> <span class="st">"csv"</span>}}}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
@@ -339,7 +339,7 @@ <h3 data-number="2.1.3" class="anchored" data-anchor-id="layers"><span class="he
 <span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>                        <span class="at">:MCOLOR</span> <span class="st">"brown"</span>})]))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div>
-<script>vegaEmbed(document.currentScript.parentElement, {"width":400,"background":"floralwhite","title":{"text":"points and a line"},"layer":[{"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"circle","size":400,"tooltip":true},"width":400,"background":"floralwhite","height":300},{"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"line","size":4,"color":"brown","tooltip":true},"width":400,"background":"floralwhite","height":300}],"height":300,"data":{"values":"x,y\n0,0.4206537650583564\n1,0.6724796007167885\n2,0.7050347862061456\n3,0.8930385822551181\n4,1.306600670637002\n5,1.268862423764611\n6,0.831122264403631\n7,0.6807856685712571\n8,1.144596786276848\n9,0.7593255523174587\n10,1.1178754159346975\n11,0.7665958706099416\n12,0.44430773909612997\n13,0.31329714924493024\n14,0.7759573865477782\n15,1.252553019261778\n16,1.4318891963340379\n17,1.264678201658897\n18,1.1543653608205222\n19,1.178266045823578\n","format":{"type":"csv"}}});</script>
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 </div>
 </section>
 <section id="concatenation" class="level3" data-number="2.1.4">
@@ -361,7 +361,7 @@ <h3 data-number="2.1.4" class="anchored" data-anchor-id="concatenation"><span cl
 <span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>                        <span class="at">:WIDTH</span> <span class="dv">100</span>})]))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-<script>vegaEmbed(document.currentScript.parentElement, {"width":400,"background":"floralwhite","vconcat":[{"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"circle","size":400,"tooltip":true},"width":100,"background":"floralwhite","height":100},{"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"line","size":4,"color":"brown","tooltip":true},"width":100,"background":"floralwhite","height":100}],"height":300,"data":{"values":"x,y\n0,0.4206537650583564\n1,0.6724796007167885\n2,0.7050347862061456\n3,0.8930385822551181\n4,1.306600670637002\n5,1.268862423764611\n6,0.831122264403631\n7,0.6807856685712571\n8,1.144596786276848\n9,0.7593255523174587\n10,1.1178754159346975\n11,0.7665958706099416\n12,0.44430773909612997\n13,0.31329714924493024\n14,0.7759573865477782\n15,1.252553019261778\n16,1.4318891963340379\n17,1.264678201658897\n18,1.1543653608205222\n19,1.178266045823578\n","format":{"type":"csv"}}});</script>
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 </div>
 <div class="sourceClojure">
 <div class="sourceCode" id="cb9"><pre class="sourceCode clojure code-with-copy"><code class="sourceCode clojure"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>(<span class="kw">-&gt;</span> random-walk</span>
@@ -380,7 +380,7 @@ <h3 data-number="2.1.4" class="anchored" data-anchor-id="concatenation"><span cl
 <span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a>                        <span class="at">:WIDTH</span> <span class="dv">100</span>})]))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-<script>vegaEmbed(document.currentScript.parentElement, {"hconcat":[{"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"circle","size":400,"tooltip":true},"width":100,"background":"floralwhite","height":100},{"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"line","size":4,"color":"brown","tooltip":true},"width":100,"background":"floralwhite","height":100}],"width":400,"background":"floralwhite","height":300,"data":{"values":"x,y\n0,0.4206537650583564\n1,0.6724796007167885\n2,0.7050347862061456\n3,0.8930385822551181\n4,1.306600670637002\n5,1.268862423764611\n6,0.831122264403631\n7,0.6807856685712571\n8,1.144596786276848\n9,0.7593255523174587\n10,1.1178754159346975\n11,0.7665958706099416\n12,0.44430773909612997\n13,0.31329714924493024\n14,0.7759573865477782\n15,1.252553019261778\n16,1.4318891963340379\n17,1.264678201658897\n18,1.1543653608205222\n19,1.178266045823578\n","format":{"type":"csv"}}});</script>
+<script>vegaEmbed(document.currentScript.parentElement, {"hconcat":[{"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"circle","size":400,"tooltip":true},"width":100,"background":"floralwhite","height":100},{"encoding":{"y":{"field":"y","type":"quantitative"},"x":{"field":"x","type":"quantitative"}},"mark":{"type":"line","size":4,"color":"brown","tooltip":true},"width":100,"background":"floralwhite","height":100}],"width":400,"background":"floralwhite","height":300,"data":{"values":"x,y\n0,0.33499630870162345\n1,0.14865499282967176\n2,0.4175140694174997\n3,8.544534747053634E-4\n4,0.21611694980290996\n5,-0.01124169329133462\n6,0.21686893895818826\n7,-0.015952354498198096\n8,-0.16409827162465285\n9,-0.35092257188323295\n10,-0.5890835621814955\n11,-1.0199885792681167\n12,-0.8971587522753082\n13,-0.46339169646556255\n14,-0.5623134576577914\n15,-0.14283599926419188\n16,-0.5580077556027917\n17,-0.20738217969612172\n18,-0.059989600225636375\n19,-0.20147573893522097\n","format":{"type":"csv"}}});</script>
 </div>
 </section>
 <section id="linear-regression" class="level3" data-number="2.1.5">
@@ -663,7 +663,7 @@ <h3 data-number="2.1.7" class="anchored" data-anchor-id="combining-a-few-things-
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
       <a href="../../index.html" class="pagination-link">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">index.html</span></span>
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Preface</span></span>
       </a>          
   </div>
   <div class="nav-page nav-page-next">
diff --git a/docs/search.json b/docs/search.json
index 4a8b1fb..414bd11 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1,17 +1,10 @@
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-    "objectID": "intro/datasets/index.html",
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-    "title": "5  Datasets",
-    "section": "",
-    "text": "(ns intro.datasets\n  (:require [scicloj.noj.v1.datasets :as datasets]))\n\n\ndatasets/iris\n\n_unnamed [150 5]:\n\n\n\n\n\n\n\n\n\n\n:sepal-length\n:sepal-width\n:petal-length\n:petal-width\n:species\n\n\n\n\n5.1\n3.5\n1.4\n0.2\nsetosa\n\n\n4.9\n3.0\n1.4\n0.2\nsetosa\n\n\n4.7\n3.2\n1.3\n0.2\nsetosa\n\n\n4.6\n3.1\n1.5\n0.2\nsetosa\n\n\n5.0\n3.6\n1.4\n0.2\nsetosa\n\n\n5.4\n3.9\n1.7\n0.4\nsetosa\n\n\n4.6\n3.4\n1.4\n0.3\nsetosa\n\n\n5.0\n3.4\n1.5\n0.2\nsetosa\n\n\n4.4\n2.9\n1.4\n0.2\nsetosa\n\n\n4.9\n3.1\n1.5\n0.1\nsetosa\n\n\n…\n…\n…\n…\n…\n\n\n6.9\n3.1\n5.4\n2.1\nvirginica\n\n\n6.7\n3.1\n5.6\n2.4\nvirginica\n\n\n6.9\n3.1\n5.1\n2.3\nvirginica\n\n\n5.8\n2.7\n5.1\n1.9\nvirginica\n\n\n6.8\n3.2\n5.9\n2.3\nvirginica\n\n\n6.7\n3.3\n5.7\n2.5\nvirginica\n\n\n6.7\n3.0\n5.2\n2.3\nvirginica\n\n\n6.3\n2.5\n5.0\n1.9\nvirginica\n\n\n6.5\n3.0\n5.2\n2.0\nvirginica\n\n\n6.2\n3.4\n5.4\n2.3\nvirginica\n\n\n5.9\n3.0\n5.1\n1.8\nvirginica\n\n\n\n\ndatasets/mtcars\n\n_unnamed [32 12]:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:name\n:mpg\n:cyl\n:disp\n:hp\n:drat\n:wt\n:qsec\n:vs\n:am\n:gear\n:carb\n\n\n\n\nMazda RX4\n21.0\n6\n160.0\n110\n3.90\n2.620\n16.46\n0\n1\n4\n4\n\n\nMazda RX4 Wag\n21.0\n6\n160.0\n110\n3.90\n2.875\n17.02\n0\n1\n4\n4\n\n\nDatsun 710\n22.8\n4\n108.0\n93\n3.85\n2.320\n18.61\n1\n1\n4\n1\n\n\nHornet 4 Drive\n21.4\n6\n258.0\n110\n3.08\n3.215\n19.44\n1\n0\n3\n1\n\n\nHornet Sportabout\n18.7\n8\n360.0\n175\n3.15\n3.440\n17.02\n0\n0\n3\n2\n\n\nValiant\n18.1\n6\n225.0\n105\n2.76\n3.460\n20.22\n1\n0\n3\n1\n\n\nDuster 360\n14.3\n8\n360.0\n245\n3.21\n3.570\n15.84\n0\n0\n3\n4\n\n\nMerc 240D\n24.4\n4\n146.7\n62\n3.69\n3.190\n20.00\n1\n0\n4\n2\n\n\nMerc 230\n22.8\n4\n140.8\n95\n3.92\n3.150\n22.90\n1\n0\n4\n2\n\n\nMerc 280\n19.2\n6\n167.6\n123\n3.92\n3.440\n18.30\n1\n0\n4\n4\n\n\n…\n…\n…\n…\n…\n…\n…\n…\n…\n…\n…\n…\n\n\nDodge Challenger\n15.5\n8\n318.0\n150\n2.76\n3.520\n16.87\n0\n0\n3\n2\n\n\nAMC Javelin\n15.2\n8\n304.0\n150\n3.15\n3.435\n17.30\n0\n0\n3\n2\n\n\nCamaro Z28\n13.3\n8\n350.0\n245\n3.73\n3.840\n15.41\n0\n0\n3\n4\n\n\nPontiac Firebird\n19.2\n8\n400.0\n175\n3.08\n3.845\n17.05\n0\n0\n3\n2\n\n\nFiat X1-9\n27.3\n4\n79.0\n66\n4.08\n1.935\n18.90\n1\n1\n4\n1\n\n\nPorsche 914-2\n26.0\n4\n120.3\n91\n4.43\n2.140\n16.70\n0\n1\n5\n2\n\n\nLotus Europa\n30.4\n4\n95.1\n113\n3.77\n1.513\n16.90\n1\n1\n5\n2\n\n\nFord Pantera L\n15.8\n8\n351.0\n264\n4.22\n3.170\n14.50\n0\n1\n5\n4\n\n\nFerrari Dino\n19.7\n6\n145.0\n175\n3.62\n2.770\n15.50\n0\n1\n5\n6\n\n\nMaserati Bora\n15.0\n8\n301.0\n335\n3.54\n3.570\n14.60\n0\n1\n5\n8\n\n\nVolvo 142E\n21.4\n4\n121.0\n109\n4.11\n2.780\n18.60\n1\n1\n4\n2\n\n\n\n\ndatasets/diamonds\n\n_unnamed [53940 10]:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:carat\n:cut\n:color\n:clarity\n:depth\n:table\n:price\n:x\n:y\n:z\n\n\n\n\n0.23\nIdeal\nE\nSI2\n61.5\n55.0\n326\n3.95\n3.98\n2.43\n\n\n0.21\nPremium\nE\nSI1\n59.8\n61.0\n326\n3.89\n3.84\n2.31\n\n\n0.23\nGood\nE\nVS1\n56.9\n65.0\n327\n4.05\n4.07\n2.31\n\n\n0.29\nPremium\nI\nVS2\n62.4\n58.0\n334\n4.20\n4.23\n2.63\n\n\n0.31\nGood\nJ\nSI2\n63.3\n58.0\n335\n4.34\n4.35\n2.75\n\n\n0.24\nVery Good\nJ\nVVS2\n62.8\n57.0\n336\n3.94\n3.96\n2.48\n\n\n0.24\nVery Good\nI\nVVS1\n62.3\n57.0\n336\n3.95\n3.98\n2.47\n\n\n0.26\nVery Good\nH\nSI1\n61.9\n55.0\n337\n4.07\n4.11\n2.53\n\n\n0.22\nFair\nE\nVS2\n65.1\n61.0\n337\n3.87\n3.78\n2.49\n\n\n0.23\nVery Good\nH\nVS1\n59.4\n61.0\n338\n4.00\n4.05\n2.39\n\n\n…\n…\n…\n…\n…\n…\n…\n…\n…\n…\n\n\n0.71\nIdeal\nG\nVS1\n61.4\n56.0\n2756\n5.76\n5.73\n3.53\n\n\n0.71\nPremium\nE\nSI1\n60.5\n55.0\n2756\n5.79\n5.74\n3.49\n\n\n0.71\nPremium\nF\nSI1\n59.8\n62.0\n2756\n5.74\n5.73\n3.43\n\n\n0.70\nVery Good\nE\nVS2\n60.5\n59.0\n2757\n5.71\n5.76\n3.47\n\n\n0.70\nVery Good\nE\nVS2\n61.2\n59.0\n2757\n5.69\n5.72\n3.49\n\n\n0.72\nPremium\nD\nSI1\n62.7\n59.0\n2757\n5.69\n5.73\n3.58\n\n\n0.72\nIdeal\nD\nSI1\n60.8\n57.0\n2757\n5.75\n5.76\n3.50\n\n\n0.72\nGood\nD\nSI1\n63.1\n55.0\n2757\n5.69\n5.75\n3.61\n\n\n0.70\nVery Good\nD\nSI1\n62.8\n60.0\n2757\n5.66\n5.68\n3.56\n\n\n0.86\nPremium\nH\nSI2\n61.0\n58.0\n2757\n6.15\n6.12\n3.74\n\n\n0.75\nIdeal\nD\nSI2\n62.2\n55.0\n2757\n5.83\n5.87\n3.64\n\n\n\n\n\n\n\nsource: notebooks/intro/datasets.clj"
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     "title": "2  Visualization",
     "section": "2.1 Visualizing datases with Hanami",
-    "text": "2.1 Visualizing datases with Hanami\nNoj offers a few convenience functions to make Hanami plotting work smoothly with Tablecloth and Kindly.\n\n(def random-walk\n  (let [n 20]\n    (-> {:x (range n)\n         :y (->> (repeatedly n #(- (rand) 0.5))\n                 (reductions +))}\n        tc/dataset)))\n\n\n2.1.1 A simple plot\nWe can plot a Tablecloth datasete using a Hanami template:\n\n(-> random-walk\n    (vis/hanami-plot ht/point-chart\n                     {:MSIZE 200}))\n\n\n\n\nLet us look inside the resulting vega-lite space. We can see the dataset is included as CSV:\n\n(-> random-walk\n    (vis/hanami-plot ht/point-chart\n                     {:MSIZE 200})\n    kind/pprint)\n\n\n{:encoding\n {:y {:field \"y\", :type \"quantitative\"},\n  :x {:field \"x\", :type \"quantitative\"}},\n :mark {:type \"circle\", :size 200, :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  \"x,y\\n0,0.4206537650583564\\n1,0.6724796007167885\\n2,0.7050347862061456\\n3,0.8930385822551181\\n4,1.306600670637002\\n5,1.268862423764611\\n6,0.831122264403631\\n7,0.6807856685712571\\n8,1.144596786276848\\n9,0.7593255523174587\\n10,1.1178754159346975\\n11,0.7665958706099416\\n12,0.44430773909612997\\n13,0.31329714924493024\\n14,0.7759573865477782\\n15,1.252553019261778\\n16,1.4318891963340379\\n17,1.264678201658897\\n18,1.1543653608205222\\n19,1.178266045823578\\n\",\n  :format {:type \"csv\"}}}\n\n\n\n2.1.2 Additional Hanami templates\nThe scicloj.noj.v1.vis.hanami.templates namespace add Hanami templates to Hanami’s own collection.\n\n(-> datasets/mtcars\n    (vis/hanami-plot vht/boxplot-chart\n                     {:X :gear\n                      :XTYPE :nominal\n                      :Y :mpg}))\n\n\n\n\n\n\n2.1.3 Layers\n\n(-> random-walk\n    (vis/hanami-layers\n     {:TITLE \"points and a line\"}\n     [(vis/hanami-plot nil\n                       ht/point-chart\n                       {:MSIZE 400})\n      (vis/hanami-plot nil\n                       ht/line-chart\n                       {:MSIZE 4\n                        :MCOLOR \"brown\"})]))\n\n\n\n\n\n\n2.1.4 Concatenation\n\n(-> random-walk\n    (vis/hanami-vconcat\n     {}\n     [(vis/hanami-plot nil\n                       ht/point-chart\n                       {:MSIZE 400\n                        :HEIGHT 100\n                        :WIDTH 100})\n      (vis/hanami-plot nil\n                       ht/line-chart\n                       {:MSIZE 4\n                        :MCOLOR \"brown\"\n                        :HEIGHT 100\n                        :WIDTH 100})]))\n\n\n\n\n\n(-> random-walk\n    (vis/hanami-hconcat\n     {}\n     [(vis/hanami-plot nil\n                       ht/point-chart\n                       {:MSIZE 400\n                        :HEIGHT 100\n                        :WIDTH 100})\n      (vis/hanami-plot nil\n                       ht/line-chart\n                       {:MSIZE 4\n                        :MCOLOR \"brown\"\n                        :HEIGHT 100\n                        :WIDTH 100})]))\n\n\n\n\n\n\n2.1.5 Linear regression\n\n(-> datasets/mtcars\n    (stats/add-predictions :mpg [:wt]\n                           {:model-type :smile.regression/ordinary-least-square})\n    (vis/hanami-layers {}\n                       [(vis/hanami-plot nil\n                                         ht/point-chart\n                                         {:X :wt\n                                          :Y :mpg\n                                          :MSIZE 200\n                                          :HEIGHT 200\n                                          :WIDTH 200})\n                        (vis/hanami-plot nil\n                                         ht/line-chart\n                                         {:X :wt\n                                          :Y :mpg-prediction\n                                          :MSIZE 5\n                                          :MCOLOR \"purple\"\n                                          :YTITLE :mpg})]))\n\n\n\n\n\n\n2.1.6 Histogram\n\n(-> datasets/iris\n    (vis/hanami-histogram :sepal-width\n                          {:nbins 10}))\n\n\n\n\n\n\n2.1.7 Combining a few things together\nThe following is inspired by the example at Plotnine’s main page. Note how we add regression lines here. We take care of layout and colouring on our side, not using Vega-Lite for that.\n\n(let [pallete (->> :accent\n                   color/palette\n                   (mapv color/format-hex))]\n  (-> datasets/mtcars\n      (tc/group-by :gear {:result-type :as-map})\n      (->> (sort-by key)\n           (map-indexed\n            (fn [i [group-name ds]]\n              (-> ds\n                  (stats/add-predictions :mpg [:wt]\n                                         {:model-type :smile.regression/ordinary-least-square})\n                  (tc/select-columns [:gear :wt :mpg :mpg-prediction])\n                  (vis/hanami-layers {:TITLE (str \"grear=\" group-name)}\n                                     [(vis/hanami-plot nil\n                                                       ht/point-chart\n                                                       {:X :wt\n                                                        :Y :mpg\n                                                        :MSIZE 200\n                                                        :MCOLOR (pallete i)\n                                                        :HEIGHT 200\n                                                        :WIDTH 200})\n                                      (vis/hanami-plot nil\n                                                       ht/line-chart\n                                                       {:X :wt\n                                                        :Y :mpg-prediction\n                                                        :MSIZE 5\n                                                        :MCOLOR (pallete i)\n                                                        :YTITLE :mpg})]\n                                     ))))\n           (vis/hanami-vconcat nil {}))))\n\n\n\n\nA similar example with histograms:\n\n(let [pallete (->> :accent\n                   color/palette\n                   (mapv color/format-hex))]\n  (-> datasets/iris\n      (tc/group-by :species {:result-type :as-map})\n      (->> (sort-by key)\n           (map-indexed\n            (fn [i [group-name ds]]\n              (-> ds\n                  (vis/hanami-histogram :sepal-width\n                                        {:nbins 10}))))\n           (vis/hanami-vconcat nil {}))))\n\n\n\n\nScatterplots and regression lines again, this time using Vega-Lite for layout and coloring (using its “facet” option).\n\n(-> datasets/mtcars\n    (tc/group-by [:gear])\n    (stats/add-predictions :mpg [:wt]\n                           {:model-type :smile.regression/ordinary-least-square})\n    (tc/ungroup)\n    (tc/select-columns [:gear :wt :mpg :mpg-prediction])\n    (vis/hanami-layers {}\n                       [(vis/hanami-plot nil\n                                         ht/point-chart\n                                         {:X :wt\n                                          :Y :mpg\n                                          :MSIZE 200\n                                          :COLOR \"gear\"\n                                          :HEIGHT 100\n                                          :WIDTH 200})\n                        (vis/hanami-plot nil\n                                         ht/line-chart\n                                         {:X :wt\n                                          :Y :mpg-prediction\n                                          :MSIZE 5\n                                          :COLOR \"gear\"\n                                          :YTITLE :mpg})])\n    ((fn [spec]\n       {:facet {:row {:field \"gear\"}}\n        :spec (dissoc spec :data)\n        :data (:data spec)}))\n    kind/vega-lite)\n\n\n\n\n\n:bye\n\n\n:bye\n\n\n\n\n\nsource: notebooks/intro/visualization.clj"
+    "text": "2.1 Visualizing datases with Hanami\nNoj offers a few convenience functions to make Hanami plotting work smoothly with Tablecloth and Kindly.\n\n(def random-walk\n  (let [n 20]\n    (-> {:x (range n)\n         :y (->> (repeatedly n #(- (rand) 0.5))\n                 (reductions +))}\n        tc/dataset)))\n\n\n2.1.1 A simple plot\nWe can plot a Tablecloth datasete using a Hanami template:\n\n(-> random-walk\n    (vis/hanami-plot ht/point-chart\n                     {:MSIZE 200}))\n\n\n\n\nLet us look inside the resulting vega-lite space. We can see the dataset is included as CSV:\n\n(-> random-walk\n    (vis/hanami-plot ht/point-chart\n                     {:MSIZE 200})\n    kind/pprint)\n\n\n{:encoding\n {:y {:field \"y\", :type \"quantitative\"},\n  :x {:field \"x\", :type \"quantitative\"}},\n :mark {:type \"circle\", :size 200, :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  \"x,y\\n0,0.33499630870162345\\n1,0.14865499282967176\\n2,0.4175140694174997\\n3,8.544534747053634E-4\\n4,0.21611694980290996\\n5,-0.01124169329133462\\n6,0.21686893895818826\\n7,-0.015952354498198096\\n8,-0.16409827162465285\\n9,-0.35092257188323295\\n10,-0.5890835621814955\\n11,-1.0199885792681167\\n12,-0.8971587522753082\\n13,-0.46339169646556255\\n14,-0.5623134576577914\\n15,-0.14283599926419188\\n16,-0.5580077556027917\\n17,-0.20738217969612172\\n18,-0.059989600225636375\\n19,-0.20147573893522097\\n\",\n  :format {:type \"csv\"}}}\n\n\n\n2.1.2 Additional Hanami templates\nThe scicloj.noj.v1.vis.hanami.templates namespace add Hanami templates to Hanami’s own collection.\n\n(-> datasets/mtcars\n    (vis/hanami-plot vht/boxplot-chart\n                     {:X :gear\n                      :XTYPE :nominal\n                      :Y :mpg}))\n\n\n\n\n\n\n2.1.3 Layers\n\n(-> random-walk\n    (vis/hanami-layers\n     {:TITLE \"points and a line\"}\n     [(vis/hanami-plot nil\n                       ht/point-chart\n                       {:MSIZE 400})\n      (vis/hanami-plot nil\n                       ht/line-chart\n                       {:MSIZE 4\n                        :MCOLOR \"brown\"})]))\n\n\n\n\n\n\n2.1.4 Concatenation\n\n(-> random-walk\n    (vis/hanami-vconcat\n     {}\n     [(vis/hanami-plot nil\n                       ht/point-chart\n                       {:MSIZE 400\n                        :HEIGHT 100\n                        :WIDTH 100})\n      (vis/hanami-plot nil\n                       ht/line-chart\n                       {:MSIZE 4\n                        :MCOLOR \"brown\"\n                        :HEIGHT 100\n                        :WIDTH 100})]))\n\n\n\n\n\n(-> random-walk\n    (vis/hanami-hconcat\n     {}\n     [(vis/hanami-plot nil\n                       ht/point-chart\n                       {:MSIZE 400\n                        :HEIGHT 100\n                        :WIDTH 100})\n      (vis/hanami-plot nil\n                       ht/line-chart\n                       {:MSIZE 4\n                        :MCOLOR \"brown\"\n                        :HEIGHT 100\n                        :WIDTH 100})]))\n\n\n\n\n\n\n2.1.5 Linear regression\n\n(-> datasets/mtcars\n    (stats/add-predictions :mpg [:wt]\n                           {:model-type :smile.regression/ordinary-least-square})\n    (vis/hanami-layers {}\n                       [(vis/hanami-plot nil\n                                         ht/point-chart\n                                         {:X :wt\n                                          :Y :mpg\n                                          :MSIZE 200\n                                          :HEIGHT 200\n                                          :WIDTH 200})\n                        (vis/hanami-plot nil\n                                         ht/line-chart\n                                         {:X :wt\n                                          :Y :mpg-prediction\n                                          :MSIZE 5\n                                          :MCOLOR \"purple\"\n                                          :YTITLE :mpg})]))\n\n\n\n\n\n\n2.1.6 Histogram\n\n(-> datasets/iris\n    (vis/hanami-histogram :sepal-width\n                          {:nbins 10}))\n\n\n\n\n\n\n2.1.7 Combining a few things together\nThe following is inspired by the example at Plotnine’s main page. Note how we add regression lines here. We take care of layout and colouring on our side, not using Vega-Lite for that.\n\n(let [pallete (->> :accent\n                   color/palette\n                   (mapv color/format-hex))]\n  (-> datasets/mtcars\n      (tc/group-by :gear {:result-type :as-map})\n      (->> (sort-by key)\n           (map-indexed\n            (fn [i [group-name ds]]\n              (-> ds\n                  (stats/add-predictions :mpg [:wt]\n                                         {:model-type :smile.regression/ordinary-least-square})\n                  (tc/select-columns [:gear :wt :mpg :mpg-prediction])\n                  (vis/hanami-layers {:TITLE (str \"grear=\" group-name)}\n                                     [(vis/hanami-plot nil\n                                                       ht/point-chart\n                                                       {:X :wt\n                                                        :Y :mpg\n                                                        :MSIZE 200\n                                                        :MCOLOR (pallete i)\n                                                        :HEIGHT 200\n                                                        :WIDTH 200})\n                                      (vis/hanami-plot nil\n                                                       ht/line-chart\n                                                       {:X :wt\n                                                        :Y :mpg-prediction\n                                                        :MSIZE 5\n                                                        :MCOLOR (pallete i)\n                                                        :YTITLE :mpg})]\n                                     ))))\n           (vis/hanami-vconcat nil {}))))\n\n\n\n\nA similar example with histograms:\n\n(let [pallete (->> :accent\n                   color/palette\n                   (mapv color/format-hex))]\n  (-> datasets/iris\n      (tc/group-by :species {:result-type :as-map})\n      (->> (sort-by key)\n           (map-indexed\n            (fn [i [group-name ds]]\n              (-> ds\n                  (vis/hanami-histogram :sepal-width\n                                        {:nbins 10}))))\n           (vis/hanami-vconcat nil {}))))\n\n\n\n\nScatterplots and regression lines again, this time using Vega-Lite for layout and coloring (using its “facet” option).\n\n(-> datasets/mtcars\n    (tc/group-by [:gear])\n    (stats/add-predictions :mpg [:wt]\n                           {:model-type :smile.regression/ordinary-least-square})\n    (tc/ungroup)\n    (tc/select-columns [:gear :wt :mpg :mpg-prediction])\n    (vis/hanami-layers {}\n                       [(vis/hanami-plot nil\n                                         ht/point-chart\n                                         {:X :wt\n                                          :Y :mpg\n                                          :MSIZE 200\n                                          :COLOR \"gear\"\n                                          :HEIGHT 100\n                                          :WIDTH 200})\n                        (vis/hanami-plot nil\n                                         ht/line-chart\n                                         {:X :wt\n                                          :Y :mpg-prediction\n                                          :MSIZE 5\n                                          :COLOR \"gear\"\n                                          :YTITLE :mpg})])\n    ((fn [spec]\n       {:facet {:row {:field \"gear\"}}\n        :spec (dissoc spec :data)\n        :data (:data spec)}))\n    kind/vega-lite)\n\n\n\n\n\n:bye\n\n\n:bye\n\n\n\n\n\nsource: notebooks/intro/visualization.clj"
   },
   {
     "objectID": "intro/image/index.html",
@@ -26,5 +19,19 @@
     "title": "4  Python",
     "section": "4.1 Using Python visualizations",
     "text": "4.1 Using Python visualizations\n\n(require-python '[numpy :as np]\n                '[numpy.random :as np.random]\n                'matplotlib.pyplot\n                '[seaborn :as sns]\n                'json\n                '[arviz :as az])\n\n\n:ok\n\n\n(def sine-data\n  (-> {:x (range 0 (* 3 np/pi) 0.1)}\n      tc/dataset\n      (tc/add-column :y #(fun/sin (:x %)))))\n\n\n(vis.python/with-pyplot\n  ;; http://gigasquidsoftware.com/blog/2020/01/18/parens-for-pyplot/\n  (matplotlib.pyplot/plot\n   (:x sine-data)\n   (:y sine-data)))\n\n\n\n\n\n(vis.python/pyplot\n #(matplotlib.pyplot/plot\n   (:x sine-data)\n   (:y sine-data)))\n\n\n\n\nhttps://seaborn.pydata.org/tutorial/introduction\n\n(let [tips (sns/load_dataset \"tips\")]\n  (sns/set_theme)\n  (vis.python/pyplot\n   #(sns/relplot :data tips\n                 :x \"total_bill\"\n                 :y \"tip\"\n                 :col \"time\"\n                 :hue \"smoker\"\n                 :style \"smoker\"\n                 :size \"size\")))\n\n\n\n\n\n(let [size [10 50]\n      data {:normal (apply np.random/randn size)\n            :gumbel (np.random/gumbel :size size)\n            :student_t (np.random/standard_t :df 6\n                                             :size size)\n            :exponential (np.random/exponential :size size)}]\n  (vis.python/pyplot\n   #(az/plot_forest data)))\n\n\n\n\n\n:bye\n\n\n:bye\n\n\n\n\n\nsource: notebooks/intro/python.clj"
+  },
+  {
+    "objectID": "intro/datasets/index.html",
+    "href": "intro/datasets/index.html",
+    "title": "5  Datasets",
+    "section": "",
+    "text": "(ns intro.datasets\n  (:require [scicloj.noj.v1.datasets :as datasets]))\n\n\ndatasets/iris\n\n_unnamed [150 5]:\n\n\n\n\n\n\n\n\n\n\n:sepal-length\n:sepal-width\n:petal-length\n:petal-width\n:species\n\n\n\n\n5.1\n3.5\n1.4\n0.2\nsetosa\n\n\n4.9\n3.0\n1.4\n0.2\nsetosa\n\n\n4.7\n3.2\n1.3\n0.2\nsetosa\n\n\n4.6\n3.1\n1.5\n0.2\nsetosa\n\n\n5.0\n3.6\n1.4\n0.2\nsetosa\n\n\n5.4\n3.9\n1.7\n0.4\nsetosa\n\n\n4.6\n3.4\n1.4\n0.3\nsetosa\n\n\n5.0\n3.4\n1.5\n0.2\nsetosa\n\n\n4.4\n2.9\n1.4\n0.2\nsetosa\n\n\n4.9\n3.1\n1.5\n0.1\nsetosa\n\n\n…\n…\n…\n…\n…\n\n\n6.9\n3.1\n5.4\n2.1\nvirginica\n\n\n6.7\n3.1\n5.6\n2.4\nvirginica\n\n\n6.9\n3.1\n5.1\n2.3\nvirginica\n\n\n5.8\n2.7\n5.1\n1.9\nvirginica\n\n\n6.8\n3.2\n5.9\n2.3\nvirginica\n\n\n6.7\n3.3\n5.7\n2.5\nvirginica\n\n\n6.7\n3.0\n5.2\n2.3\nvirginica\n\n\n6.3\n2.5\n5.0\n1.9\nvirginica\n\n\n6.5\n3.0\n5.2\n2.0\nvirginica\n\n\n6.2\n3.4\n5.4\n2.3\nvirginica\n\n\n5.9\n3.0\n5.1\n1.8\nvirginica\n\n\n\n\ndatasets/mtcars\n\n_unnamed [32 12]:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:name\n:mpg\n:cyl\n:disp\n:hp\n:drat\n:wt\n:qsec\n:vs\n:am\n:gear\n:carb\n\n\n\n\nMazda RX4\n21.0\n6\n160.0\n110\n3.90\n2.620\n16.46\n0\n1\n4\n4\n\n\nMazda RX4 Wag\n21.0\n6\n160.0\n110\n3.90\n2.875\n17.02\n0\n1\n4\n4\n\n\nDatsun 710\n22.8\n4\n108.0\n93\n3.85\n2.320\n18.61\n1\n1\n4\n1\n\n\nHornet 4 Drive\n21.4\n6\n258.0\n110\n3.08\n3.215\n19.44\n1\n0\n3\n1\n\n\nHornet Sportabout\n18.7\n8\n360.0\n175\n3.15\n3.440\n17.02\n0\n0\n3\n2\n\n\nValiant\n18.1\n6\n225.0\n105\n2.76\n3.460\n20.22\n1\n0\n3\n1\n\n\nDuster 360\n14.3\n8\n360.0\n245\n3.21\n3.570\n15.84\n0\n0\n3\n4\n\n\nMerc 240D\n24.4\n4\n146.7\n62\n3.69\n3.190\n20.00\n1\n0\n4\n2\n\n\nMerc 230\n22.8\n4\n140.8\n95\n3.92\n3.150\n22.90\n1\n0\n4\n2\n\n\nMerc 280\n19.2\n6\n167.6\n123\n3.92\n3.440\n18.30\n1\n0\n4\n4\n\n\n…\n…\n…\n…\n…\n…\n…\n…\n…\n…\n…\n…\n\n\nDodge Challenger\n15.5\n8\n318.0\n150\n2.76\n3.520\n16.87\n0\n0\n3\n2\n\n\nAMC Javelin\n15.2\n8\n304.0\n150\n3.15\n3.435\n17.30\n0\n0\n3\n2\n\n\nCamaro Z28\n13.3\n8\n350.0\n245\n3.73\n3.840\n15.41\n0\n0\n3\n4\n\n\nPontiac Firebird\n19.2\n8\n400.0\n175\n3.08\n3.845\n17.05\n0\n0\n3\n2\n\n\nFiat X1-9\n27.3\n4\n79.0\n66\n4.08\n1.935\n18.90\n1\n1\n4\n1\n\n\nPorsche 914-2\n26.0\n4\n120.3\n91\n4.43\n2.140\n16.70\n0\n1\n5\n2\n\n\nLotus Europa\n30.4\n4\n95.1\n113\n3.77\n1.513\n16.90\n1\n1\n5\n2\n\n\nFord Pantera L\n15.8\n8\n351.0\n264\n4.22\n3.170\n14.50\n0\n1\n5\n4\n\n\nFerrari Dino\n19.7\n6\n145.0\n175\n3.62\n2.770\n15.50\n0\n1\n5\n6\n\n\nMaserati Bora\n15.0\n8\n301.0\n335\n3.54\n3.570\n14.60\n0\n1\n5\n8\n\n\nVolvo 142E\n21.4\n4\n121.0\n109\n4.11\n2.780\n18.60\n1\n1\n4\n2\n\n\n\n\ndatasets/diamonds\n\n_unnamed [53940 10]:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:carat\n:cut\n:color\n:clarity\n:depth\n:table\n:price\n:x\n:y\n:z\n\n\n\n\n0.23\nIdeal\nE\nSI2\n61.5\n55.0\n326\n3.95\n3.98\n2.43\n\n\n0.21\nPremium\nE\nSI1\n59.8\n61.0\n326\n3.89\n3.84\n2.31\n\n\n0.23\nGood\nE\nVS1\n56.9\n65.0\n327\n4.05\n4.07\n2.31\n\n\n0.29\nPremium\nI\nVS2\n62.4\n58.0\n334\n4.20\n4.23\n2.63\n\n\n0.31\nGood\nJ\nSI2\n63.3\n58.0\n335\n4.34\n4.35\n2.75\n\n\n0.24\nVery Good\nJ\nVVS2\n62.8\n57.0\n336\n3.94\n3.96\n2.48\n\n\n0.24\nVery Good\nI\nVVS1\n62.3\n57.0\n336\n3.95\n3.98\n2.47\n\n\n0.26\nVery Good\nH\nSI1\n61.9\n55.0\n337\n4.07\n4.11\n2.53\n\n\n0.22\nFair\nE\nVS2\n65.1\n61.0\n337\n3.87\n3.78\n2.49\n\n\n0.23\nVery Good\nH\nVS1\n59.4\n61.0\n338\n4.00\n4.05\n2.39\n\n\n…\n…\n…\n…\n…\n…\n…\n…\n…\n…\n\n\n0.71\nIdeal\nG\nVS1\n61.4\n56.0\n2756\n5.76\n5.73\n3.53\n\n\n0.71\nPremium\nE\nSI1\n60.5\n55.0\n2756\n5.79\n5.74\n3.49\n\n\n0.71\nPremium\nF\nSI1\n59.8\n62.0\n2756\n5.74\n5.73\n3.43\n\n\n0.70\nVery Good\nE\nVS2\n60.5\n59.0\n2757\n5.71\n5.76\n3.47\n\n\n0.70\nVery Good\nE\nVS2\n61.2\n59.0\n2757\n5.69\n5.72\n3.49\n\n\n0.72\nPremium\nD\nSI1\n62.7\n59.0\n2757\n5.69\n5.73\n3.58\n\n\n0.72\nIdeal\nD\nSI1\n60.8\n57.0\n2757\n5.75\n5.76\n3.50\n\n\n0.72\nGood\nD\nSI1\n63.1\n55.0\n2757\n5.69\n5.75\n3.61\n\n\n0.70\nVery Good\nD\nSI1\n62.8\n60.0\n2757\n5.66\n5.68\n3.56\n\n\n0.86\nPremium\nH\nSI2\n61.0\n58.0\n2757\n6.15\n6.12\n3.74\n\n\n0.75\nIdeal\nD\nSI2\n62.2\n55.0\n2757\n5.83\n5.87\n3.64\n\n\n\n\n\n\n\nsource: notebooks/intro/datasets.clj"
+  },
+  {
+    "objectID": "index.html",
+    "href": "index.html",
+    "title": "Noj Documentation",
+    "section": "",
+    "text": "1 Preface"
   }
 ]
\ No newline at end of file
diff --git a/notebooks/index.clj b/notebooks/index.clj
deleted file mode 100644
index 00726c1..0000000
--- a/notebooks/index.clj
+++ /dev/null
@@ -1 +0,0 @@
-(ns index)
diff --git a/src/user.clj b/src/user.clj
index 51b9ee8..1240dfb 100644
--- a/src/user.clj
+++ b/src/user.clj
@@ -29,9 +29,10 @@
 
   (clay/update-book!
    {:title "Noj Documentation"
+    :toc true
     :base-source-path "notebooks"
     :base-target-path "clean-book"
-    :chapter-source-paths ["index.clj"
+    :chapter-source-paths ["index.md"
                            "intro/visualization.clj"
                            "intro/image.clj"
                            "intro/python.clj"
