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REPLOT (relationship)
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<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<h1 id="seaborn-lib">Seaborn Lib</h1>
<h2 id="basic-imports-for-seaborn">Basic Imports For Seaborn</h2>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-1">1</a></span>
<span class="normal"><a href="#__codelineno-0-2">2</a></span>
<span class="normal"><a href="#__codelineno-0-3">3</a></span>
<span class="normal"><a href="#__codelineno-0-4">4</a></span>
<span class="normal"><a href="#__codelineno-0-5">5</a></span>
<span class="normal"><a href="#__codelineno-0-6">6</a></span>
<span class="normal"><a href="#__codelineno-0-7">7</a></span>
<span class="normal"><a href="#__codelineno-0-8">8</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1"></a><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<a id="__codelineno-0-2" name="__codelineno-0-2"></a><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<a id="__codelineno-0-3" name="__codelineno-0-3"></a><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<a id="__codelineno-0-4" name="__codelineno-0-4"></a><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<a id="__codelineno-0-5" name="__codelineno-0-5"></a>
<a id="__codelineno-0-6" name="__codelineno-0-6"></a><span class="c1"># set aesthetic parameters in one step</span>
<a id="__codelineno-0-7" name="__codelineno-0-7"></a><span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s1">&#39;darkgrid&#39;</span><span class="p">)</span>
<a id="__codelineno-0-8" name="__codelineno-0-8"></a><span class="c1">#STYLE: {None, darkgrid, whitegrid, dark, white, ticks}</span>
</code></pre></div></td></tr></table></div>
<h2 id="replot-relationship">REPLOT (relationship)</h2>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-1-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-1-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-1-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-1-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-1-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-1-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-1-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-1-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-1-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-1-10">10</a></span>
<span class="normal"><a href="#__codelineno-1-11">11</a></span>
<span class="normal"><a href="#__codelineno-1-12">12</a></span>
<span class="normal"><a href="#__codelineno-1-13">13</a></span>
<span class="normal"><a href="#__codelineno-1-14">14</a></span>
<span class="normal"><a href="#__codelineno-1-15">15</a></span>
<span class="normal"><a href="#__codelineno-1-16">16</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">replot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;name_in_data&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;name_in_data&#39;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s1">&#39;point_color&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;point_size&#39;</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="s1">&#39;point_shape&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
<a id="__codelineno-1-2" name="__codelineno-1-2"></a><span class="c1"># HUE, SIZE and STYLE: {name in data} -- used to differentiate points, a sort-of 3rd dimension</span>
<a id="__codelineno-1-3" name="__codelineno-1-3"></a><span class="c1"># hue behaves differently if the data is categorical or numerical, numerical uses a color gradient</span>
<a id="__codelineno-1-4" name="__codelineno-1-4"></a><span class="c1"># SORT: {False, True} -- avoid sorting data in function of x</span>
<a id="__codelineno-1-5" name="__codelineno-1-5"></a><span class="c1"># CI: {None, sd} -- avoid computing confidence intervals or plot standard deviation</span>
<a id="__codelineno-1-6" name="__codelineno-1-6"></a><span class="c1"># (aggregate multiple measurements at each x value by plotting the mean and the 95% confidence interval around the mean)</span>
<a id="__codelineno-1-7" name="__codelineno-1-7"></a><span class="c1"># ESTIMATOR: {None} -- turn off aggregation of multiple observations</span>
<a id="__codelineno-1-8" name="__codelineno-1-8"></a><span class="c1"># MARKERS: {True, False} -- evidenziate observations with dots</span>
<a id="__codelineno-1-9" name="__codelineno-1-9"></a><span class="c1"># DASHES: {True, False} -- evidenziate observations with dashes</span>
<a id="__codelineno-1-10" name="__codelineno-1-10"></a><span class="c1"># COL, ROW: {name in data} -- categorical variables that will determine the grid of plots</span>
<a id="__codelineno-1-11" name="__codelineno-1-11"></a><span class="c1"># COL_WRAP: {int} -- &quot;Wrap&quot; the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.</span>
<a id="__codelineno-1-12" name="__codelineno-1-12"></a><span class="c1"># SCATTERPLOT</span>
<a id="__codelineno-1-13" name="__codelineno-1-13"></a><span class="c1"># depicts the joint distribution of two variables using a cloud of points</span>
<a id="__codelineno-1-14" name="__codelineno-1-14"></a><span class="c1"># kind can be omitted since scatterplot is the default for replot</span>
<a id="__codelineno-1-15" name="__codelineno-1-15"></a><span class="n">sns</span><span class="o">.</span><span class="n">replot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;scatter&#39;</span><span class="p">)</span> <span class="c1"># calls scatterplot()</span>
<a id="__codelineno-1-16" name="__codelineno-1-16"></a><span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">()</span> <span class="c1"># underlying axis-level function of replot()</span>
</code></pre></div></td></tr></table></div>
<h3 id="lineplot">LINEPLOT</h3>
<p>Using semantics in lineplot will determine the aggregation of data.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-2-1">1</a></span>
<span class="normal"><a href="#__codelineno-2-2">2</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">replot</span><span class="p">(</span><span class="n">ci</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="nb">bool</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;line&#39;</span><span class="p">)</span>
<a id="__codelineno-2-2" name="__codelineno-2-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">lineplot</span><span class="p">()</span> <span class="c1"># underlying axis-level function of replot()</span>
</code></pre></div></td></tr></table></div>
<h2 id="catplot-categorical">CATPLOT (categorical)</h2>
<p>Categorical: divided into discrete groups.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-3-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-3-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-3-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-3-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-3-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-3-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-3-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-3-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-3-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-3-10">10</a></span>
<span class="normal"><a href="#__codelineno-3-11">11</a></span>
<span class="normal"><a href="#__codelineno-3-12">12</a></span>
<span class="normal"><a href="#__codelineno-3-13">13</a></span>
<span class="normal"><a href="#__codelineno-3-14">14</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;name_in_data&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;name_in_data&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
<a id="__codelineno-3-2" name="__codelineno-3-2"></a><span class="c1"># HUE: {name in data} -- used to differenziate points, a sort-of 3rd dimension</span>
<a id="__codelineno-3-3" name="__codelineno-3-3"></a><span class="c1"># COL, ROW: {name in data} -- categorical variables that will determine the grid of plots</span>
<a id="__codelineno-3-4" name="__codelineno-3-4"></a><span class="c1"># COL_WRAP: {int} -- &quot;Wrap&quot; the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.</span>
<a id="__codelineno-3-5" name="__codelineno-3-5"></a><span class="c1"># ORDER, HUE_ORDER: {list of strings} -- order of categorical levels of the plot</span>
<a id="__codelineno-3-6" name="__codelineno-3-6"></a><span class="c1"># ROW_ORDER, COL_ORDER: {list of strings} -- order to organize the rows and/or columns of the grid in</span>
<a id="__codelineno-3-7" name="__codelineno-3-7"></a><span class="c1"># ORIENT: {&#39;v&#39;, &#39;h&#39;} -- Orientation of the plot (can also swap x&amp;y assignment)</span>
<a id="__codelineno-3-8" name="__codelineno-3-8"></a><span class="c1"># COLOR: {matplotlib color} -- Color for all of the elements, or seed for a gradient palette</span>
<a id="__codelineno-3-9" name="__codelineno-3-9"></a><span class="c1"># CATEGORICAL SCATTERPLOT - STRIPPLOT</span>
<a id="__codelineno-3-10" name="__codelineno-3-10"></a><span class="c1"># adjust the positions of points on the categorical axis with a small amount of random “jitter”</span>
<a id="__codelineno-3-11" name="__codelineno-3-11"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;strip&#39;</span><span class="p">,</span> <span class="n">jitter</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<a id="__codelineno-3-12" name="__codelineno-3-12"></a><span class="n">sns</span><span class="o">.</span><span class="n">stripplot</span><span class="p">()</span>
<a id="__codelineno-3-13" name="__codelineno-3-13"></a><span class="c1"># SIZE: {float} -- Diameter of the markers, in points</span>
<a id="__codelineno-3-14" name="__codelineno-3-14"></a><span class="c1"># JITTER: {False, float} -- magnitude of points jitter (distance from axis)</span>
</code></pre></div></td></tr></table></div>
<h3 id="categorical-scatterplot-swarmplot">CATEGORICAL SCATTERPLOT - SWARMPLOT</h3>
<p>Adjusts the points along the categorical axis preventing overlap.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-4-1">1</a></span>
<span class="normal"><a href="#__codelineno-4-2">2</a></span>
<span class="normal"><a href="#__codelineno-4-3">3</a></span>
<span class="normal"><a href="#__codelineno-4-4">4</a></span>
<span class="normal"><a href="#__codelineno-4-5">5</a></span>
<span class="normal"><a href="#__codelineno-4-6">6</a></span>
<span class="normal"><a href="#__codelineno-4-7">7</a></span>
<span class="normal"><a href="#__codelineno-4-8">8</a></span>
<span class="normal"><a href="#__codelineno-4-9">9</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;swarm&#39;</span><span class="p">)</span>
<a id="__codelineno-4-2" name="__codelineno-4-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">swarmplot</span><span class="p">()</span>
<a id="__codelineno-4-3" name="__codelineno-4-3"></a><span class="c1"># SIZE: {float} -- Diameter of the markers, in points</span>
<a id="__codelineno-4-4" name="__codelineno-4-4"></a><span class="c1"># CATEGORICAL DISTRIBUTION - BOXPLOT</span>
<a id="__codelineno-4-5" name="__codelineno-4-5"></a><span class="c1"># shows the three quartile values of the distribution along with extreme values</span>
<a id="__codelineno-4-6" name="__codelineno-4-6"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;box&#39;</span><span class="p">)</span>
<a id="__codelineno-4-7" name="__codelineno-4-7"></a><span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">()</span>
<a id="__codelineno-4-8" name="__codelineno-4-8"></a><span class="c1"># HUE: {name in data} -- box for each level of the semantic moved along the categorical axis so they don’t overlap</span>
<a id="__codelineno-4-9" name="__codelineno-4-9"></a><span class="c1"># DODGE: {bool} -- whether elements should be shifted along the categorical axis if hue is used</span>
</code></pre></div></td></tr></table></div>
<h3 id="categorical-distribution-violinplot">CATEGORICAL DISTRIBUTION - VIOLINPLOT</h3>
<p>Combines a boxplot with the kernel density estimation procedure.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-5-1">1</a></span>
<span class="normal"><a href="#__codelineno-5-2">2</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-5-1" name="__codelineno-5-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;violin&#39;</span><span class="p">)</span>
<a id="__codelineno-5-2" name="__codelineno-5-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">violonplot</span><span class="p">()</span>
</code></pre></div></td></tr></table></div>
<h3 id="categorical-distribution-boxenplot">CATEGORICAL DISTRIBUTION - BOXENPLOT</h3>
<p>Plot similar to boxplot but optimized for showing more information about the shape of the distribution.<br />
It is best suited for larger datasets.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-6-1">1</a></span>
<span class="normal"><a href="#__codelineno-6-2">2</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-6-1" name="__codelineno-6-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;boxen&#39;</span><span class="p">)</span>
<a id="__codelineno-6-2" name="__codelineno-6-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">boxenplot</span><span class="p">()</span>
</code></pre></div></td></tr></table></div>
<h3 id="categorical-estimate-pointplot">CATEGORICAL ESTIMATE - POINTPLOT</h3>
<p>Show point estimates and confidence intervals using scatter plot glyphs.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-7-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-7-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-7-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-7-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-7-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-7-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-7-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-7-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-7-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-7-10">10</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-7-1" name="__codelineno-7-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;point&#39;</span><span class="p">)</span>
<a id="__codelineno-7-2" name="__codelineno-7-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">pointplot</span><span class="p">()</span>
<a id="__codelineno-7-3" name="__codelineno-7-3"></a><span class="c1"># CI: {float, sd} -- size of confidence intervals to draw around estimated values, sd -&gt; standard deviation</span>
<a id="__codelineno-7-4" name="__codelineno-7-4"></a><span class="c1"># MARKERS: {string, list of strings} -- markers to use for each of the hue levels</span>
<a id="__codelineno-7-5" name="__codelineno-7-5"></a><span class="c1"># LINESTYLES: {string, list of strings} -- line styles to use for each of the hue levels</span>
<a id="__codelineno-7-6" name="__codelineno-7-6"></a><span class="c1"># DODGE: {bool, float} -- amount to separate the points for each hue level along the categorical axis</span>
<a id="__codelineno-7-7" name="__codelineno-7-7"></a><span class="c1"># JOIN: {bool} -- if True, lines will be drawn between point estimates at the same hue level</span>
<a id="__codelineno-7-8" name="__codelineno-7-8"></a><span class="c1"># SCALE: {float} -- scale factor for the plot elements</span>
<a id="__codelineno-7-9" name="__codelineno-7-9"></a><span class="c1"># ERRWIDTH: {float} -- thickness of error bar lines (and caps)</span>
<a id="__codelineno-7-10" name="__codelineno-7-10"></a><span class="c1"># CAPSIZE: {float} -- width of the &quot;caps&quot; on error bars</span>
</code></pre></div></td></tr></table></div>
<h3 id="categorical-estimate-barplot">CATEGORICAL ESTIMATE - BARPLOT</h3>
<p>Show point estimates and confidence intervals as rectangular bars.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-8-1">1</a></span>
<span class="normal"><a href="#__codelineno-8-2">2</a></span>
<span class="normal"><a href="#__codelineno-8-3">3</a></span>
<span class="normal"><a href="#__codelineno-8-4">4</a></span>
<span class="normal"><a href="#__codelineno-8-5">5</a></span>
<span class="normal"><a href="#__codelineno-8-6">6</a></span>
<span class="normal"><a href="#__codelineno-8-7">7</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-8-1" name="__codelineno-8-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;bar&#39;</span><span class="p">)</span>
<a id="__codelineno-8-2" name="__codelineno-8-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">()</span>
<a id="__codelineno-8-3" name="__codelineno-8-3"></a><span class="c1"># CI: {float, sd} -- size of confidence intervals to draw around estimated values, sd -&gt; standard deviation</span>
<a id="__codelineno-8-4" name="__codelineno-8-4"></a><span class="c1"># ERRCOLOR: {matplotlib color} -- color for the lines that represent the confidence interval</span>
<a id="__codelineno-8-5" name="__codelineno-8-5"></a><span class="c1"># ERRWIDTH: {float} -- thickness of error bar lines (and caps)</span>
<a id="__codelineno-8-6" name="__codelineno-8-6"></a><span class="c1"># CAPSIZE: {float} -- width of the &quot;caps&quot; on error bars</span>
<a id="__codelineno-8-7" name="__codelineno-8-7"></a><span class="c1"># DODGE: {bool} -- whether elements should be shifted along the categorical axis if hue is used</span>
</code></pre></div></td></tr></table></div>
<h3 id="categorical-estimate-countplot">CATEGORICAL ESTIMATE - COUNTPLOT</h3>
<p>Show the counts of observations in each categorical bin using bars.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-9-1">1</a></span>
<span class="normal"><a href="#__codelineno-9-2">2</a></span>
<span class="normal"><a href="#__codelineno-9-3">3</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-9-1" name="__codelineno-9-1"></a><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;count&#39;</span><span class="p">)</span>
<a id="__codelineno-9-2" name="__codelineno-9-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">()</span>
<a id="__codelineno-9-3" name="__codelineno-9-3"></a><span class="c1"># DODGE: {bool} -- whether elements should be shifted along the categorical axis if hue is used</span>
</code></pre></div></td></tr></table></div>
<h2 id="univariate-distributions">UNIVARIATE DISTRIBUTIONS</h2>
<h3 id="distplot">DISTPLOT</h3>
<p>Flexibly plot a univariate distribution of observations</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-10-1">1</a></span>
<span class="normal"><a href="#__codelineno-10-2">2</a></span>
<span class="normal"><a href="#__codelineno-10-3">3</a></span>
<span class="normal"><a href="#__codelineno-10-4">4</a></span>
<span class="normal"><a href="#__codelineno-10-5">5</a></span>
<span class="normal"><a href="#__codelineno-10-6">6</a></span>
<span class="normal"><a href="#__codelineno-10-7">7</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-10-1" name="__codelineno-10-1"></a><span class="c1"># A: {series, 1d-array, list}</span>
<a id="__codelineno-10-2" name="__codelineno-10-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
<a id="__codelineno-10-3" name="__codelineno-10-3"></a><span class="c1"># BINS: {None, arg for matplotlib hist()} -- specification of hist bins, or None to use Freedman-Diaconis rule</span>
<a id="__codelineno-10-4" name="__codelineno-10-4"></a><span class="c1"># HIST: {bool} - whether to plot a (normed) histogram</span>
<a id="__codelineno-10-5" name="__codelineno-10-5"></a><span class="c1"># KDE: {bool} - whether to plot a gaussian kernel density estimate</span>
<a id="__codelineno-10-6" name="__codelineno-10-6"></a><span class="c1"># HIST_KWD, KDE_KWD, RUG_KWD: {dict} -- keyword arguments for underlying plotting functions</span>
<a id="__codelineno-10-7" name="__codelineno-10-7"></a><span class="c1"># COLOR: {matplotlib color} -- color to plot everything but the fitted curve in</span>
</code></pre></div></td></tr></table></div>
<h3 id="rugplot">RUGPLOT</h3>
<p>Plot datapoints in an array as sticks on an axis.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-11-1">1</a></span>
<span class="normal"><a href="#__codelineno-11-2">2</a></span>
<span class="normal"><a href="#__codelineno-11-3">3</a></span>
<span class="normal"><a href="#__codelineno-11-4">4</a></span>
<span class="normal"><a href="#__codelineno-11-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-11-1" name="__codelineno-11-1"></a><span class="c1"># A: {vector} -- 1D array of observations</span>
<a id="__codelineno-11-2" name="__codelineno-11-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">rugplot</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">data</span><span class="p">)</span> <span class="c1"># -&gt; axes obj with plot on it</span>
<a id="__codelineno-11-3" name="__codelineno-11-3"></a><span class="c1"># HEIGHT: {scalar} -- height of ticks as proportion of the axis</span>
<a id="__codelineno-11-4" name="__codelineno-11-4"></a><span class="c1"># AXIS: {&#39;x&#39;, &#39;y&#39;} -- axis to draw rugplot on</span>
<a id="__codelineno-11-5" name="__codelineno-11-5"></a><span class="c1"># AX: {matplotlib axes} -- axes to draw plot into, otherwise grabs current axes</span>
</code></pre></div></td></tr></table></div>
<h3 id="kdeplot">KDEPLOT</h3>
<p>Fit and plot a univariate or bivariate kernel density estimate.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-12-1">1</a></span>
<span class="normal"><a href="#__codelineno-12-2">2</a></span>
<span class="normal"><a href="#__codelineno-12-3">3</a></span>
<span class="normal"><a href="#__codelineno-12-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-12-1" name="__codelineno-12-1"></a><span class="c1"># DATA: {1D array-like} -- input data</span>
<a id="__codelineno-12-2" name="__codelineno-12-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
<a id="__codelineno-12-3" name="__codelineno-12-3"></a><span class="c1"># DATA2 {1D array-like} -- second input data. if present, a bivariate KDE will be estimated.</span>
<a id="__codelineno-12-4" name="__codelineno-12-4"></a><span class="c1"># SHADE: {bool} -- if True, shade-in the area under KDE curve (or draw with filled contours is bivariate)</span>
</code></pre></div></td></tr></table></div>
<h2 id="bivariate-distribution">BIVARIATE DISTRIBUTION</h2>
<h3 id="jointplot">JOINTPLOT</h3>
<p>Draw a plot of two variables with bivariate and univariate graphs.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-13-1">1</a></span>
<span class="normal"><a href="#__codelineno-13-2">2</a></span>
<span class="normal"><a href="#__codelineno-13-3">3</a></span>
<span class="normal"><a href="#__codelineno-13-4">4</a></span>
<span class="normal"><a href="#__codelineno-13-5">5</a></span>
<span class="normal"><a href="#__codelineno-13-6">6</a></span>
<span class="normal"><a href="#__codelineno-13-7">7</a></span>
<span class="normal"><a href="#__codelineno-13-8">8</a></span>
<span class="normal"><a href="#__codelineno-13-9">9</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-13-1" name="__codelineno-13-1"></a><span class="c1"># X, Y: {string, vector} -- data or names of variables in data</span>
<a id="__codelineno-13-2" name="__codelineno-13-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">jointplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
<a id="__codelineno-13-3" name="__codelineno-13-3"></a><span class="c1"># DATA:{pandas DataFrame} -- DataFrame when x and y are variable names</span>
<a id="__codelineno-13-4" name="__codelineno-13-4"></a><span class="c1"># KIND: {&#39;scatter&#39;, &#39;reg&#39;, &#39;resid&#39;, &#39;kde&#39;, &#39;hex&#39;} -- kind of plot to draw</span>
<a id="__codelineno-13-5" name="__codelineno-13-5"></a><span class="c1"># COLOR: {matplotlib color} -- color used for plot elements</span>
<a id="__codelineno-13-6" name="__codelineno-13-6"></a><span class="c1"># HEIGHT: {numeric} -- size of figure (it will be square)</span>
<a id="__codelineno-13-7" name="__codelineno-13-7"></a><span class="c1"># RATIO: {numeric} -- ratio of joint axes height to marginal axes height</span>
<a id="__codelineno-13-8" name="__codelineno-13-8"></a><span class="c1"># SPACE: {numeric} -- space between the joint and marginal axes</span>
<a id="__codelineno-13-9" name="__codelineno-13-9"></a><span class="c1"># JOINT_KWD, MARGINAL_KWD, ANNOT_KWD: {dict} -- additional keyword arguments for the plot components</span>
</code></pre></div></td></tr></table></div>
<h2 id="pair-wise-relationisps-in-dataset">PAIR-WISE RELATIONISPS IN DATASET</h2>
<h3 id="pairplot">PAIRPLOT</h3>
<p>Plot pairwise relationships in a dataset.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-14-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-14-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-14-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-14-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-14-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-14-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-14-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-14-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-14-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-14-10">10</a></span>
<span class="normal"><a href="#__codelineno-14-11">11</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-14-1" name="__codelineno-14-1"></a><span class="c1"># DATA: {pandas DataFrame} -- tidy (long-form) dataframe where each column is a variable and each row is an observation</span>
<a id="__codelineno-14-2" name="__codelineno-14-2"></a><span class="n">sns</span><span class="o">.</span><span class="n">pairplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span>
<a id="__codelineno-14-3" name="__codelineno-14-3"></a><span class="c1"># HUE: {string (variable name)} -- variable in data to map plot aspects to different colors</span>
<a id="__codelineno-14-4" name="__codelineno-14-4"></a><span class="c1"># HUE_ORDER: {list of strings} -- order for the levels of the hue variable in the palette</span>
<a id="__codelineno-14-5" name="__codelineno-14-5"></a><span class="c1"># VARS: {list of variable names} -- variables within data to use, otherwise every column with numeric datatype</span>
<a id="__codelineno-14-6" name="__codelineno-14-6"></a><span class="c1"># X_VARS, Y_VARS: {list of variable names} -- variables within data to use separately for rows and columns of figure</span>
<a id="__codelineno-14-7" name="__codelineno-14-7"></a><span class="c1"># KIND: {&#39;scatter&#39;, &#39;reg&#39;} -- kind of plot for the non-identity relationships</span>
<a id="__codelineno-14-8" name="__codelineno-14-8"></a><span class="c1"># DIAG_KIND: {&#39;auto&#39;, &#39;hist&#39;, &#39;kde&#39;} -- Kind of plot for the diagonal subplots. default depends hue</span>
<a id="__codelineno-14-9" name="__codelineno-14-9"></a><span class="c1"># MARKERS: {matplotlib marker or list}</span>
<a id="__codelineno-14-10" name="__codelineno-14-10"></a><span class="c1"># HEIGHT:{scalar} -- height (in inches) of each facet</span>
<a id="__codelineno-14-11" name="__codelineno-14-11"></a><span class="c1"># ASPECT: {scalar} -- aspect * height gives the width (in inches) of each facet</span>
</code></pre></div></td></tr></table></div>
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