From a126c77ee7c37f6e5b79acf5ba72986dec9575cd Mon Sep 17 00:00:00 2001
From: daslu <daniel.slutsky@gmail.com>
Date: Tue, 31 Oct 2023 23:56:44 +0200
Subject: [PATCH] rerendered docs

---
 docs/index.html                     | 395 +---------------------------
 docs/intro/datasets/index.html      |  19 +-
 docs/intro/visualization/index.html |  17 +-
 docs/search.json                    |  20 +-
 4 files changed, 41 insertions(+), 410 deletions(-)
diff --git a/docs/index.html b/docs/index.html
index ef33c84..8f181e5 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -1,387 +1,8 @@
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-        <li class="sidebar-item">
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-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
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-        <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>
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-  <div class="sidebar-item-container"> 
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-        <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>
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-
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+<html xmlns="http://www.w3.org/1999/xhtml">    
+  <head>      
+    <title>Redirect to index.html</title>      
+    <meta http-equiv="refresh" content="0;URL='index.html'" />    
+  </head>    
+  <body> 
+  </body>  
+</html>     
diff --git a/docs/intro/datasets/index.html b/docs/intro/datasets/index.html
index 393f683..5e8b678 100644
--- a/docs/intro/datasets/index.html
+++ b/docs/intro/datasets/index.html
@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Noj Documentation - 4&nbsp; Datasets</title>
+<title>Noj Documentation - 5&nbsp; Datasets</title>
 <style>
 code{white-space: pre-wrap;}
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@@ -93,7 +93,7 @@
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 <script src="../../site_libs/quarto-search/quarto-search.js"></script>
 <meta name="quarto:offset" content="../../">
-<link href="../../intro/image/index.html" rel="prev">
+<link href="../../intro/python/index.html" rel="prev">
 <script src="../../site_libs/quarto-html/quarto.js"></script>
 <script src="../../site_libs/quarto-html/popper.min.js"></script>
 <script src="../../site_libs/quarto-html/tippy.umd.min.js"></script>
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     <div class="container-fluid d-flex justify-content-between">
-      <h1 class="quarto-secondary-nav-title"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Datasets</span></h1>
+      <h1 class="quarto-secondary-nav-title"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Datasets</span></h1>
       <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
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@@ -172,7 +172,12 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">4</span>&nbs
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../intro/datasets/index.html" class="sidebar-item-text sidebar-link active"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Datasets</span></a>
+  <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 active"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Datasets</span></a>
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@@ -187,7 +192,7 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">4</span>&nbs
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title d-none d-lg-block"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Datasets</span></h1>
+<h1 class="title d-none d-lg-block"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Datasets</span></h1>
 </div>
 
 
@@ -1198,8 +1203,8 @@ <h1 class="title d-none d-lg-block"><span class="chapter-number">4</span>&nbsp;
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="../../intro/image/index.html" class="pagination-link">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Turning tensors into images</span></span>
+      <a href="../../intro/python/index.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Python</span></span>
       </a>          
   </div>
   <div class="nav-page nav-page-next">
diff --git a/docs/intro/visualization/index.html b/docs/intro/visualization/index.html
index a85a545..6ed4251 100644
--- a/docs/intro/visualization/index.html
+++ b/docs/intro/visualization/index.html
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 </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">4</span>&nbsp; <span class="chapter-title">Datasets</span></a>
+  <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>
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@@ -282,7 +287,7 @@ <h3 data-number="2.1.1" class="anchored" data-anchor-id="a-simple-plot"><span cl
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 <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,-0.39615391984318404\n1,-0.5446812271166611\n2,-0.9832657108893103\n3,-0.5671649841192353\n4,-0.09009523598518931\n5,0.07415912187784146\n6,-0.10170081461740843\n7,0.10020830258460467\n8,0.1640025237962498\n9,-0.28933053134006614\n10,-0.32120294054073717\n11,-0.7589079499865103\n12,-0.461950404436437\n13,-0.6060248939186419\n14,-0.545977980694729\n15,-0.8584547085001384\n16,-0.792211350491126\n17,-0.9381995494131425\n18,-0.906282018393721\n19,-0.4575963334804557\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\n0,0.4206537650583564\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>
 </div>
 <p>Let us look inside the resulting vega-lite space. We can see the dataset is included as CSV:</p>
 <div class="sourceClojure">
@@ -301,7 +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,-0.39615391984318404</span><span class="sc">\n</span><span class="st">1,-0.5446812271166611</span><span class="sc">\n</span><span class="st">2,-0.9832657108893103</span><span class="sc">\n</span><span class="st">3,-0.5671649841192353</span><span class="sc">\n</span><span class="st">4,-0.09009523598518931</span><span class="sc">\n</span><span class="st">5,0.07415912187784146</span><span class="sc">\n</span><span class="st">6,-0.10170081461740843</span><span class="sc">\n</span><span class="st">7,0.10020830258460467</span><span class="sc">\n</span><span class="st">8,0.1640025237962498</span><span class="sc">\n</span><span class="st">9,-0.28933053134006614</span><span class="sc">\n</span><span class="st">10,-0.32120294054073717</span><span class="sc">\n</span><span class="st">11,-0.7589079499865103</span><span class="sc">\n</span><span class="st">12,-0.461950404436437</span><span class="sc">\n</span><span class="st">13,-0.6060248939186419</span><span class="sc">\n</span><span class="st">14,-0.545977980694729</span><span class="sc">\n</span><span class="st">15,-0.8584547085001384</span><span class="sc">\n</span><span class="st">16,-0.792211350491126</span><span class="sc">\n</span><span class="st">17,-0.9381995494131425</span><span class="sc">\n</span><span class="st">18,-0.906282018393721</span><span class="sc">\n</span><span class="st">19,-0.4575963334804557</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">0,0.4206537650583564</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="cb5-11"><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>
@@ -334,7 +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,-0.39615391984318404\n1,-0.5446812271166611\n2,-0.9832657108893103\n3,-0.5671649841192353\n4,-0.09009523598518931\n5,0.07415912187784146\n6,-0.10170081461740843\n7,0.10020830258460467\n8,0.1640025237962498\n9,-0.28933053134006614\n10,-0.32120294054073717\n11,-0.7589079499865103\n12,-0.461950404436437\n13,-0.6060248939186419\n14,-0.545977980694729\n15,-0.8584547085001384\n16,-0.792211350491126\n17,-0.9381995494131425\n18,-0.906282018393721\n19,-0.4575963334804557\n","format":{"type":"csv"}}});</script>
+<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>
 </div>
 </section>
 <section id="concatenation" class="level3" data-number="2.1.4">
@@ -356,7 +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>
 </div>
 <div>
-<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,-0.39615391984318404\n1,-0.5446812271166611\n2,-0.9832657108893103\n3,-0.5671649841192353\n4,-0.09009523598518931\n5,0.07415912187784146\n6,-0.10170081461740843\n7,0.10020830258460467\n8,0.1640025237962498\n9,-0.28933053134006614\n10,-0.32120294054073717\n11,-0.7589079499865103\n12,-0.461950404436437\n13,-0.6060248939186419\n14,-0.545977980694729\n15,-0.8584547085001384\n16,-0.792211350491126\n17,-0.9381995494131425\n18,-0.906282018393721\n19,-0.4575963334804557\n","format":{"type":"csv"}}});</script>
+<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>
 </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>
@@ -375,7 +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>
 </div>
 <div>
-<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,-0.39615391984318404\n1,-0.5446812271166611\n2,-0.9832657108893103\n3,-0.5671649841192353\n4,-0.09009523598518931\n5,0.07415912187784146\n6,-0.10170081461740843\n7,0.10020830258460467\n8,0.1640025237962498\n9,-0.28933053134006614\n10,-0.32120294054073717\n11,-0.7589079499865103\n12,-0.461950404436437\n13,-0.6060248939186419\n14,-0.545977980694729\n15,-0.8584547085001384\n16,-0.792211350491126\n17,-0.9381995494131425\n18,-0.906282018393721\n19,-0.4575963334804557\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\n0,0.4206537650583564\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>
 </div>
 </section>
 <section id="linear-regression" class="level3" data-number="2.1.5">
diff --git a/docs/search.json b/docs/search.json
index bba6d65..4a8b1fb 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1,17 +1,17 @@
 [
   {
-    "objectID": "index.html",
-    "href": "index.html",
-    "title": "Noj Documentation",
+    "objectID": "intro/datasets/index.html",
+    "href": "intro/datasets/index.html",
+    "title": "5  Datasets",
     "section": "",
-    "text": "(ns index)\n\n\n\n\n\nsource: notebooks/index.clj"
+    "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,-0.39615391984318404\\n1,-0.5446812271166611\\n2,-0.9832657108893103\\n3,-0.5671649841192353\\n4,-0.09009523598518931\\n5,0.07415912187784146\\n6,-0.10170081461740843\\n7,0.10020830258460467\\n8,0.1640025237962498\\n9,-0.28933053134006614\\n10,-0.32120294054073717\\n11,-0.7589079499865103\\n12,-0.461950404436437\\n13,-0.6060248939186419\\n14,-0.545977980694729\\n15,-0.8584547085001384\\n16,-0.792211350491126\\n17,-0.9381995494131425\\n18,-0.906282018393721\\n19,-0.4575963334804557\\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\\n0,0.4206537650583564\\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"
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   {
     "objectID": "intro/image/index.html",
@@ -21,10 +21,10 @@
     "text": "(ns intro.image\n  (:require [tech.v3.tensor :as tensor]\n            [tech.v3.datatype.functional :as fun]\n            [scicloj.noj.v1.vis.image :as vis.image]))\n\n\n(-> (for [i (range 100)]\n      (range 100))\n    tensor/ensure-tensor\n    (fun/* 400)\n    (vis.image/tensor->image :ushort-gray))\n\n\n\n\n\n\nsource: notebooks/intro/image.clj"
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-    "title": "4  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": "intro/python/index.html#using-python-visualizations",
+    "href": "intro/python/index.html#using-python-visualizations",
+    "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"
   }
 ]
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