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Seaborn 筆記
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<p>這篇記錄我在使用 <a href="https://seaborn.pydata.org/">seaborn</a> 做資料分析還有 visualization 時常用的 code.
一般慣例會把 seaborn 更名成 <code>sns</code> for reference.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<h2 id="基本設定">基本設定<a class="anchor-link" href="#基本設定">¶</a></h2>
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<p>這邊值得注意的是要調整的參數要一次全部設定, 用好幾次 <code>set()</code> 的話只有最後一次的 <code>set()</code> 的結果會被保留</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">font</span><span class="o">=</span><span class="s1">'IPAPMincho'</span><span class="p">,</span> <span class="n">font_scale</span><span class="o">=</span><span class="mf">1.8</span><span class="p">)</span>
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<h2 id="Histogram">Histogram<a class="anchor-link" href="#Histogram">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
<span class="n">data</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span>
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<pre>array([-0.53267554, 0.03851161, -0.16072742, -0.70889663, 0.23085979,
-1.61295347, -0.46508874, 0.60112507, 0.42017249, -0.73656917])</pre>
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<p>seaborn 是建立在 matplotlib 之上, 因此 matplotlib 也可以直接拿來跟 seaborn 產生的圖互動</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Defualt style with kde'</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s1">'dark'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Dark style without kde'</span><span class="p">);</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
</pre></div>
</div>
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
"/>
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<h2 id="Scatter-plot">Scatter plot<a class="anchor-link" href="#Scatter-plot">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="s1">'x'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span>
<span class="s1">'y'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)})</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
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vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe table table-striped table-responsive">
<thead>
<tr style="text-align: right;">
<th></th>
<th>x</th>
<th>y</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>-2.863752</td>
<td>-1.066424</td>
</tr>
<tr>
<th>1</th>
<td>-0.779238</td>
<td>0.862169</td>
</tr>
<tr>
<th>2</th>
<td>0.016786</td>
<td>-0.016519</td>
</tr>
<tr>
<th>3</th>
<td>0.948504</td>
<td>0.298314</td>
</tr>
<tr>
<th>4</th>
<td>2.029428</td>
<td>1.211997</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>要使用 seaborn 初始設定就再呼叫一次 <code>set()</code></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>注意點：</p>
<ul>
<li>一般用lmplot畫, 然後設定 <code>fit_reg=False</code> 就可以讓 regression line 消失. 有時候有沒有那條線影響圖很大</li>
<li>一樣先 <code>x</code>, 再 <code>y</code></li>
</ul>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">fit_reg</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]:</span>
<span class="n">sns</span><span class="o">.</span><span class="n">lmplot</span><span class="p">(</span><span class="s1">'x'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span>
<span class="n">fit_reg</span><span class="o">=</span><span class="n">fit_reg</span><span class="p">,</span>
<span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s2">"marker"</span><span class="p">:</span> <span class="s2">"D"</span><span class="p">,</span> <span class="s2">"s"</span><span class="p">:</span> <span class="mi">100</span><span class="p">})</span>
<span class="n">title</span> <span class="o">=</span> <span class="s1">'Show regression line'</span> <span class="k">if</span> <span class="n">fit_reg</span> <span class="k">else</span> <span class="s1">'Without regression line'</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
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<div class="output_png output_subarea">
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
"/>
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<p>想要將兩個 <code>lmplot</code> 並排 render 可以參考這個 <a href="https://stackoverflow.com/a/33091668/3859572">stackoverflow 答案</a>.</p>
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<h2 id="Correlation-matrix-/-Heatmap">Correlation matrix / Heatmap<a class="anchor-link" href="#Correlation-matrix-/-Heatmap">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="s1">'x1'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span>
<span class="s1">'x2'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span>
<span class="s1">'x3'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="p">})</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th></th>
<th>x1</th>
<th>x2</th>
<th>x3</th>
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<tbody>
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<th>0</th>
<td>1.269566</td>
<td>0.349083</td>
<td>-0.000743</td>
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<th>1</th>
<td>-1.634587</td>
<td>0.072568</td>
<td>0.042596</td>
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<tr>
<th>2</th>
<td>-0.581238</td>
<td>-0.337935</td>
<td>-0.412084</td>
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<tr>
<th>3</th>
<td>-0.080881</td>
<td>-1.376481</td>
<td>1.361046</td>
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<th>4</th>
<td>-0.609886</td>
<td>-1.061285</td>
<td>0.265788</td>
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<p>這邊利用 pandas 本身的 <code>corr()</code> 計算 correlation matrix 然後使用 seaborn 做 vis.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">corr</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
<span class="n">corr</span>
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<th>x1</th>
<th>x2</th>
<th>x3</th>
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<th>x1</th>
<td>1.000000</td>
<td>-0.034731</td>
<td>0.032407</td>
</tr>
<tr>
<th>x2</th>
<td>-0.034731</td>
<td>1.000000</td>
<td>-0.192169</td>
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<th>x3</th>
<td>0.032407</td>
<td>-0.192169</td>
<td>1.000000</td>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">font_scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">corr</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">annot_kws</span><span class="o">=</span><span class="p">{</span><span class="s2">"size"</span><span class="p">:</span> <span class="mi">15</span><span class="p">},</span>
<span class="n">xticklabels</span><span class="o">=</span><span class="n">corr</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
<span class="n">yticklabels</span><span class="o">=</span><span class="n">corr</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">);</span>
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<h2 id="References">References<a class="anchor-link" href="#References">¶</a></h2><ul>
<li><a href="http://seaborn.pydata.org/api.html">API References</a></li>
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<script>
$(document).scroll(function() {
var y = $(this).scrollTop();
if ( $(window).width() > 980 ) {
if (y > 600) {
$('#left-navigation').fadeIn(300);
} else {
$('#left-navigation').fadeOut(300);
}
}
});
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function showMailingPopUp() {
require(["mojo/signup-forms/Loader"], function(L) { L.start({"baseUrl":"mc.us18.list-manage.com","uuid":"151cb59f2de814c499c76b77a","lid":"dd1d78cc5e"})})
document.cookie = "MCPopupClosed=; expires=Thu, 01 Jan 1970 00:00:00 UTC";
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$(".open-popup").on('click', function() {
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<script>
var options = {
bottom: '32px', // default: '32px'
right: 'unset', // default: '32px'
left: '32px', // default: 'unset'
time: '0.2s', // default: '0.3s'
mixColor: '#fff', // default: '#fff'
backgroundColor: '#fff', // default: '#fff'
buttonColorDark: '#100f2c', // default: '#100f2c'
buttonColorLight: '#fff', // default: '#fff'
saveInCookies: true, // default: true,
label: '🌓', // default: ''
autoMatchOsTheme: true // default: true
}
const darkmode = new Darkmode(options);
darkmode.showWidget();
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function openTocNav() {
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function closeTocNav() {
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var current_width = document.getElementById("tocNav").style.width;
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} else {
document.getElementById("tocNav").style.width = "100%";
}
}
function closeLeftNavImage(elementId) {
document.getElementById(elementId).style.width = "0%";
}