https://300watts.me/tags/qcut/Recent content in Qcut on 300.WattsHugoenmorristai01@gmail.com (Morris)morristai01@gmail.com (Morris)This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.Sat, 05 Aug 2017 11:46:56 +0800https://300watts.me/posts/pandas-cut%E8%88%87qcut%E5%87%BD%E6%95%B8/Sat, 05 Aug 2017 11:46:56 +0800morristai01@gmail.com (Morris)https://300watts.me/posts/pandas-cut%E8%88%87qcut%E5%87%BD%E6%95%B8/<p>如果我們今天有一些連續性的數值，可以使用<code>cut</code>及<code>qcut</code>進行離散化。 <code>cut</code>函数是利用數值區間將數值分類，<code>qcut</code>則是用分位數。 換句話說，<code>cut</code>用在長度相等的類別，<code>qcut</code>用在大小相等的類別。</p> <blockquote> <h2 id="cut函數" class="headerLink"> <a href="#cut%e5%87%bd%e6%95%b8" class="header-mark"></a>cut函數</h2> </blockquote><p>假設我們有一些人的年齡<br> <strong>ages</strong> = <code>[20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32, 101]</code><br> 我們如果想要離散化這些數列，分成“18到25”、“25到35”、“35到60”以及“60以上”，可以使用<code>cut</code>函數 <div class="highlight"><div class="chroma"> <table class="lntable"><tr><td class="lntd"> <pre tabindex="0" class="chroma"><code><span class="lnt">1 </span><span class="lnt">2 </span><span class="lnt">3 </span><span class="lnt">4 </span><span class="lnt">5 </span><span class="lnt">6 </span><span class="lnt">7 </span></code></pre></td> <td class="lntd"> <pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="cl"><span class="n">bins</span> <span class="o">=</span> <span class="p">[</span><span class="mi">18</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">35</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</span> </span></span><span class="line"><span class="cl"><span class="n">cats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">ages</span><span class="p">,</span> <span class="n">bins</span><span class="p">)</span> </span></span><span class="line"><span class="cl"><span class="n">cats</span> </span></span><span class="line"><span class="cl"><span class="n">output</span><span class="p">:</span> </span></span><span class="line"><span class="cl"><span class="p">[(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">25</span><span class="p">],</span> <span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">25</span><span class="p">],</span> <span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">25</span><span class="p">],</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">35</span><span class="p">],</span> <span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">25</span><span class="p">],</span> <span class="o">...</span><span class="p">,</span> <span class="p">(</span><span class="mi">60</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="p">(</span><span class="mi">35</span><span class="p">,</span> <span class="mi">60</span><span class="p">],</span> <span class="p">(</span><span class="mi">35</span><span class="p">,</span> <span class="mi">60</span><span class="p">],</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">35</span><span class="p">],</span> <span class="n">NaN</span><span class="p">]</span> </span></span><span class="line"><span class="cl"><span class="n">Length</span><span class="p">:</span> <span class="mi">13</span> </span></span><span class="line"><span class="cl"><span class="n">Categories</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="n">int64</span><span class="p">]):</span> <span class="p">[(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">25</span><span class="p">]</span> <span class="o">&lt;</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">35</span><span class="p">]</span> <span class="o">&lt;</span> <span class="p">(</span><span class="mi">35</span><span class="p">,</span> <span class="mi">60</span><span class="p">]</span> <span class="o">&lt;</span> <span class="p">(</span><span class="mi">60</span><span class="p">,</span> <span class="mi">100</span><span class="p">]]</span></span></span></code></pre></td></tr></table> </div> </div> 第一個list是指每個年齡分別在哪個範圍內，如果超出了就變成<code>NaN</code>缺失值，cats有兩個屬性： <div class="highlight"><div class="chroma"> <table class="lntable"><tr><td class="lntd"> <pre tabindex="0" class="chroma"><code><span class="lnt">1 </span><span class="lnt">2 </span><span class="lnt">3 </span></code></pre></td> <td class="lntd"> <pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="cl"><span class="n">cats</span><span class="o">.</span><span class="n">labels</span> </span></span><span class="line"><span class="cl"><span class="n">output</span><span class="p">:</span> </span></span><span class="line"><span class="cl"><span class="n">array</span><span class="p">([</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</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="mi">3</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="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int8</span><span class="p">)</span></span></span></code></pre></td></tr></table> </div> </div> 我們也可以賦予範圍標籤，比如： <div class="highlight"><div class="chroma"> <table class="lntable"><tr><td class="lntd"> <pre tabindex="0" class="chroma"><code><span class="lnt">1 </span><span class="lnt">2 </span><span class="lnt">3 </span><span class="lnt">4 </span><span class="lnt">5 </span><span class="lnt">6 </span></code></pre></td> <td class="lntd"> <pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="cl"><span class="n">group_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Youth&#39;</span><span class="p">,</span> <span class="s1">&#39;YoungAdult&#39;</span><span class="p">,</span> <span class="s1">&#39;MiddleAged&#39;</span><span class="p">,</span> <span class="s1">&#39;Senior&#39;</span><span class="p">]</span> </span></span><span class="line"><span class="cl"><span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">ages</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">group_names</span><span class="p">)</span> </span></span><span class="line"><span class="cl"><span class="n">output</span><span class="p">:</span> </span></span><span class="line"><span class="cl"><span class="p">[</span><span class="n">Youth</span><span class="p">,</span> <span class="n">Youth</span><span class="p">,</span> <span class="n">Youth</span><span class="p">,</span> <span class="n">YoungAdult</span><span class="p">,</span> <span class="n">Youth</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="n">Senior</span><span class="p">,</span> <span class="n">MiddleAged</span><span class="p">,</span> <span class="n">MiddleAged</span><span class="p">,</span> <span class="n">YoungAdult</span><span class="p">,</span> <span class="n">NaN</span><span class="p">]</span> </span></span><span class="line"><span class="cl"><span class="n">Length</span><span class="p">:</span> <span class="mi">13</span> </span></span><span class="line"><span class="cl"><span class="n">Categories</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="nb">object</span><span class="p">):</span> <span class="p">[</span><span class="n">MiddleAged</span> <span class="o">&lt;</span> <span class="n">Senior</span> <span class="o">&lt;</span> <span class="n">YoungAdult</span> <span class="o">&lt;</span> <span class="n">Youth</span><span class="p">]</span></span></span></code></pre></td></tr></table> </div> </div></p> <blockquote> <h2 id="qcut函數" class="headerLink"> <a href="#qcut%e5%87%bd%e6%95%b8" class="header-mark"></a>qcut函數</h2> </blockquote><p><div class="highlight"><div class="chroma"> <table class="lntable"><tr><td class="lntd"> <pre tabindex="0" class="chroma"><code><span class="lnt"> 1 </span><span class="lnt"> 2 </span><span class="lnt"> 3 </span><span class="lnt"> 4 </span><span class="lnt"> 5 </span><span class="lnt"> 6 </span><span class="lnt"> 7 </span><span class="lnt"> 8 </span><span class="lnt"> 9 </span><span class="lnt">10 </span><span class="lnt">11 </span><span class="lnt">12 </span></code></pre></td> <td class="lntd"> <pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="cl"><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="c1">#高斯分佈</span> </span></span><span class="line"><span class="cl"><span class="n">cats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">qcut</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> <span class="c1">#按四分位數分類，也可以用[0, .25, .5, .75, 1.]</span> </span></span><span class="line"><span class="cl"><span class="n">cats</span> </span></span><span class="line"><span class="cl"><span class="n">output</span><span class="p">:</span> </span></span><span class="line"><span class="cl"><span class="p">[(</span><span class="mf">0.624</span><span class="p">,</span> <span class="mf">3.928</span><span class="p">],</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.691</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0144</span><span class="p">],</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.691</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0144</span><span class="p">],</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.0144</span><span class="p">,</span> <span class="mf">0.624</span><span class="p">],</span> <span class="p">(</span><span class="mf">0.624</span><span class="p">,</span> <span class="mf">3.928</span><span class="p">],</span> <span class="o">...</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.0144</span><span class="p">,</span> <span class="mf">0.624</span><span class="p">],</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.0144</span><span class="p">,</span> <span class="mf">0.624</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">2.949</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.691</span><span class="p">],</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.0144</span><span class="p">,</span> <span class="mf">0.624</span><span class="p">],</span> <span class="p">(</span><span class="mf">0.624</span><span class="p">,</span> <span class="mf">3.928</span><span class="p">]]</span> <span class="n">Length</span><span class="p">:</span> <span class="mi">1000</span> <span class="n">Categories</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="nb">object</span><span class="p">):</span> <span class="p">[[</span><span class="o">-</span><span class="mf">2.949</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.691</span><span class="p">]</span> <span class="o">&lt;</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.691</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0144</span><span class="p">]</span> <span class="o">&lt;</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.0144</span><span class="p">,</span> <span class="mf">0.624</span><span class="p">]</span> <span class="o">&lt;</span> <span class="p">(</span><span class="mf">0.624</span><span class="p">,</span> <span class="mf">3.928</span><span class="p">]]</span> </span></span><span class="line"><span class="cl"><span class="n">pd</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">cats</span><span class="p">)</span> <span class="c1">#計算每個區間的數值個數</span> </span></span><span class="line"><span class="cl"><span class="n">output</span><span class="p">:</span> </span></span><span class="line"><span class="cl"><span class="p">(</span><span class="mf">0.624</span><span class="p">,</span> <span class="mf">3.928</span><span class="p">]</span> <span class="mi">250</span> </span></span><span class="line"><span class="cl"><span class="p">(</span><span class="o">-</span><span class="mf">0.0144</span><span class="p">,</span> <span class="mf">0.624</span><span class="p">]</span> <span class="mi">250</span> </span></span><span class="line"><span class="cl"><span class="p">(</span><span class="o">-</span><span class="mf">0.691</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0144</span><span class="p">]</span> <span class="mi">250</span> </span></span><span class="line"><span class="cl"><span class="p">[</span><span class="o">-</span><span class="mf">2.949</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.691</span><span class="p">]</span> <span class="mi">250</span> </span></span><span class="line"><span class="cl"><span class="n">dtype</span><span class="p">:</span> <span class="n">int64</span></span></span></code></pre></td></tr></table> </div> </div> 會發現<code>qcut</code>把所有數值平均分配了，當然如果不想用四分位的話還可以自行輸入list，只要範圍介在0~1，例如<code>[0, 0.1, 0.5, 0.9, 1.]</code></p>