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<span id="categorical"></span><h1><span class="yiyi-st" id="yiyi-77">Categorical Data</span></h1>
<blockquote>
<p>原文：<a href="http://pandas.pydata.org/pandas-docs/stable/categorical.html">http://pandas.pydata.org/pandas-docs/stable/categorical.html</a></p>
<p>译者：<a href="https://github.com/wizardforcel">飞龙</a> <a href="http://usyiyi.cn/">UsyiyiCN</a></p>
<p>校对：（虚位以待）</p>
</blockquote>
<div class="versionadded">
<p><span class="yiyi-st" id="yiyi-78"><span class="versionmodified">版本0.15中的新功能。</span></span></p>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-79">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-80">虽然在早期版本中有<cite>pandas.Categorical</cite>，在<cite>系列</cite>和<cite>DataFrame</cite>中使用分类数据的功能是新功能。</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-81">这是对pandas分类数据类型的简介，包括与R的<code class="docutils literal"><span class="pre">factor</span></code>的短暂比较。</span></p>
<p><span class="yiyi-st" id="yiyi-82"><cite>分类</cite>是与统计中的分类变量相对应的熊猫数据类型：一个变量，只能包含有限的，通常固定的可能值（<cite>类别</cite> ； R中的<cite>电平</cite>）。</span><span class="yiyi-st" id="yiyi-83">例如性别，社会阶层，血型，国家关系，观察时间或通过Likert量表评分。</span></p>
<p><span class="yiyi-st" id="yiyi-84">与统计分类变量相反，分类数据可能有顺序（例如“强同意”与“同意”或“第一次观察”与“第二次观察”），但数值运算（加法，除法，...）可能。</span></p>
<p><span class="yiyi-st" id="yiyi-85">分类数据的所有值均位于<cite>类别</cite>或<cite>np.nan</cite>中。</span><span class="yiyi-st" id="yiyi-86">顺序由<cite>类别</cite>的顺序定义，而不是值的词汇顺序。</span><span class="yiyi-st" id="yiyi-87">在内部，数据结构由<cite>类别</cite>数组和<cite>代码</cite>的整数数组组成，它指向<cite>类别</cite>数组中的实际值。</span></p>
<p><span class="yiyi-st" id="yiyi-88">分类数据类型在以下情况下很有用：</span></p>
<ul class="simple">
<li><span class="yiyi-st" id="yiyi-89">一个字符串变量，只包含几个不同的值。</span><span class="yiyi-st" id="yiyi-90">将此类字符串变量转换为分类变量将会节省一些内存，请参见<a class="reference internal" href="#categorical-memory"><span class="std std-ref">here</span></a>。</span></li>
<li><span class="yiyi-st" id="yiyi-91">变量的词法顺序与逻辑顺序（“一个”，“两个”，“三个”）不同。</span><span class="yiyi-st" id="yiyi-92">通过转换为分类并在类别上指定顺序，排序和最小/最大将使用逻辑顺序而不是词法顺序，请参见<a class="reference internal" href="#categorical-sort"><span class="std std-ref">here</span></a>。</span></li>
<li><span class="yiyi-st" id="yiyi-93">作为一个信号给其他python库，这个列应该被当作一个分类变量（例如使用合适的统计方法或图类型）。</span></li>
</ul>
<p><span class="yiyi-st" id="yiyi-94">另请参阅<a class="reference internal" href="api.html#api-categorical"><span class="std std-ref">API docs on categoricals</span></a>。</span></p>
<div class="section" id="object-creation">
<h2><span class="yiyi-st" id="yiyi-95">Object Creation</span></h2>
<p><span class="yiyi-st" id="yiyi-96">分类<cite>系列</cite>或<cite>DataFrame</cite>中的列可以通过以下几种方式创建：</span></p>
<p><span class="yiyi-st" id="yiyi-97">在构建<cite>系列</cite>时指定<code class="docutils literal"><span class="pre">dtype="category"</span></code>：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"category"</span><span class="p">)</span>
<span class="gp">In [2]: </span><span class="n">s</span>
<span class="gr">Out[2]: </span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 a</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [a, b, c]</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-98">将现有的<cite>系列</cite>或列转换为<code class="docutils literal"><span class="pre">category</span></code> dtype：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [3]: </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="s2">"A"</span><span class="p">:[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">]})</span>
<span class="gp">In [4]: </span><span class="n">df</span><span class="p">[</span><span class="s2">"B"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"A"</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'category'</span><span class="p">)</span>
<span class="gp">In [5]: </span><span class="n">df</span>
<span class="gr">Out[5]: </span>
<span class="go"> A B</span>
<span class="go">0 a a</span>
<span class="go">1 b b</span>
<span class="go">2 c c</span>
<span class="go">3 a a</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-99">通过使用一些特殊功能：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [6]: </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">'value'</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">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">20</span><span class="p">)})</span>
<span class="gp">In [7]: </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span> <span class="s2">"{0} - {1}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">9</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="p">]</span>
<span class="gp">In [8]: </span><span class="n">df</span><span class="p">[</span><span class="s1">'group'</span><span class="p">]</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">df</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">105</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">right</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">)</span>
<span class="gp">In [9]: </span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gr">Out[9]: </span>
<span class="go"> value group</span>
<span class="go">0 65 60 - 69</span>
<span class="go">1 49 40 - 49</span>
<span class="go">2 56 50 - 59</span>
<span class="go">3 43 40 - 49</span>
<span class="go">4 43 40 - 49</span>
<span class="go">5 91 90 - 99</span>
<span class="go">6 32 30 - 39</span>
<span class="go">7 87 80 - 89</span>
<span class="go">8 36 30 - 39</span>
<span class="go">9 8 0 - 9</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-100">有关<a class="reference internal" href="generated/pandas.cut.html#pandas.cut" title="pandas.cut"><code class="xref py py-func docutils literal"><span class="pre">cut()</span></code></a>的信息，请参阅<a class="reference internal" href="reshaping.html#reshaping-tile-cut"><span class="std std-ref">documentation</span></a>。</span></p>
<p><span class="yiyi-st" id="yiyi-101">将<a class="reference internal" href="generated/pandas.Categorical.html#pandas.Categorical" title="pandas.Categorical"><code class="xref py py-class docutils literal"><span class="pre">pandas.Categorical</span></code></a>对象传递到<cite>系列</cite>或将其分配给<cite>DataFrame</cite>。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="n">raw_cat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="gp"> ....:</span>
<span class="gp">In [11]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">raw_cat</span><span class="p">)</span>
<span class="gp">In [12]: </span><span class="n">s</span>
<span class="gr">Out[12]: </span>
<span class="go">0 NaN</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 NaN</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [b, c, d]</span>
<span class="gp">In [13]: </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="s2">"A"</span><span class="p">:[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">]})</span>
<span class="gp">In [14]: </span><span class="n">df</span><span class="p">[</span><span class="s2">"B"</span><span class="p">]</span> <span class="o">=</span> <span class="n">raw_cat</span>
<span class="gp">In [15]: </span><span class="n">df</span>
<span class="gr">Out[15]: </span>
<span class="go"> A B</span>
<span class="go">0 a NaN</span>
<span class="go">1 b b</span>
<span class="go">2 c c</span>
<span class="go">3 a NaN</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-102">您还可以通过将这些参数传递到<code class="docutils literal"><span class="pre">astype()</span></code>来指定不同排序的类别或将结果数据排序：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">])</span>
<span class="gp">In [17]: </span><span class="n">s_cat</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"category"</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="gp">In [18]: </span><span class="n">s_cat</span>
<span class="gr">Out[18]: </span>
<span class="go">0 NaN</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 NaN</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [b, c, d]</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-103">分类数据具有特定的<code class="docutils literal"><span class="pre">category</span></code> <a class="reference internal" href="basics.html#basics-dtypes"><span class="std std-ref">dtype</span></a>：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [19]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
<span class="gr">Out[19]: </span>
<span class="go">A object</span>
<span class="go">B category</span>
<span class="go">dtype: object</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-104">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-105">与R的<cite>因子</cite>函数相反，分类数据不会将输入值转换为字符串，类别将最终与原始值具有相同的数据类型。</span></p>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-106">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-107">与R的<cite>因子</cite>函数相反，当前没有办法在创建时分配/更改标签。</span><span class="yiyi-st" id="yiyi-108">使用<cite>类别</cite>可在创建时间后更改类别。</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-109">要返回原始系列或<cite>numpy</cite>数组，请使用<code class="docutils literal"><span class="pre">Series.astype(original_dtype)</span></code>或<code class="docutils literal"><span class="pre">np.asarray(categorical)</span></code>：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [20]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">])</span>
<span class="gp">In [21]: </span><span class="n">s</span>
<span class="gr">Out[21]: </span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 a</span>
<span class="go">dtype: object</span>
<span class="gp">In [22]: </span><span class="n">s2</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'category'</span><span class="p">)</span>
<span class="gp">In [23]: </span><span class="n">s2</span>
<span class="gr">Out[23]: </span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 a</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [a, b, c]</span>
<span class="gp">In [24]: </span><span class="n">s3</span> <span class="o">=</span> <span class="n">s2</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'string'</span><span class="p">)</span>
<span class="gp">In [25]: </span><span class="n">s3</span>
<span class="gr">Out[25]: </span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 a</span>
<span class="go">dtype: object</span>
<span class="gp">In [26]: </span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">s2</span><span class="p">)</span>
<span class="gr">Out[26]: </span><span class="n">array</span><span class="p">([</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-110">如果您已经具有<cite>代码</cite>和<cite>类别</cite>，则可以使用<a class="reference internal" href="generated/pandas.Categorical.from_codes.html#pandas.Categorical.from_codes" title="pandas.Categorical.from_codes"><code class="xref py py-func docutils literal"><span class="pre">from_codes()</span></code></a>构造函数在正常构造函数模式下保存factorize步骤：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [27]: </span><span class="n">splitter</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">choice</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">5</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">])</span>
<span class="gp">In [28]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="o">.</span><span class="n">from_codes</span><span class="p">(</span><span class="n">splitter</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"train"</span><span class="p">,</span> <span class="s2">"test"</span><span class="p">]))</span>
</pre></div>
</div>
</div>
<div class="section" id="description">
<h2><span class="yiyi-st" id="yiyi-111">Description</span></h2>
<p><span class="yiyi-st" id="yiyi-112">对分类数据使用<code class="docutils literal"><span class="pre">.describe()</span></code>将产生与<code class="docutils literal"><span class="pre">string</span></code>类型的<cite>Series</cite>或<cite>DataFrame</cite>类似的输出。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [29]: </span><span class="n">cat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"b"</span><span class="p">,</span> <span class="s2">"a"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">])</span>
<span class="gp">In [30]: </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="s2">"cat"</span><span class="p">:</span><span class="n">cat</span><span class="p">,</span> <span class="s2">"s"</span><span class="p">:[</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]})</span>
<span class="gp">In [31]: </span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
<span class="gr">Out[31]: </span>
<span class="go"> cat s</span>
<span class="go">count 3 3</span>
<span class="go">unique 2 2</span>
<span class="go">top c c</span>
<span class="go">freq 2 2</span>
<span class="gp">In [32]: </span><span class="n">df</span><span class="p">[</span><span class="s2">"cat"</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
<span class="gr">Out[32]: </span>
<span class="go">count 3</span>
<span class="go">unique 2</span>
<span class="go">top c</span>
<span class="go">freq 2</span>
<span class="go">Name: cat, dtype: object</span>
</pre></div>
</div>
</div>
<div class="section" id="working-with-categories">
<h2><span class="yiyi-st" id="yiyi-113">Working with categories</span></h2>
<p><span class="yiyi-st" id="yiyi-114">分类数据具有<cite>类别</cite>和<cite>有序</cite>属性，其中列出了其可能的值以及顺序是否重要。</span><span class="yiyi-st" id="yiyi-115">这些属性显示为<code class="docutils literal"><span class="pre">s.cat.categories</span></code>和<code class="docutils literal"><span class="pre">s.cat.ordered</span></code>。</span><span class="yiyi-st" id="yiyi-116">如果不手动指定类别和顺序，则从传递的值推断它们。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [33]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"category"</span><span class="p">)</span>
<span class="gp">In [34]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span>
<span class="gr">Out[34]: </span><span class="n">Index</span><span class="p">([</span><span class="s1">u'a'</span><span class="p">,</span> <span class="s1">u'b'</span><span class="p">,</span> <span class="s1">u'c'</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'object'</span><span class="p">)</span>
<span class="gp">In [35]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">ordered</span>
<span class="gr">Out[35]: </span><span class="bp">False</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-117">还可以按特定顺序传入类别：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [36]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">]))</span>
<span class="gp">In [37]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span>
<span class="gr">Out[37]: </span><span class="n">Index</span><span class="p">([</span><span class="s1">u'c'</span><span class="p">,</span> <span class="s1">u'b'</span><span class="p">,</span> <span class="s1">u'a'</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'object'</span><span class="p">)</span>
<span class="gp">In [38]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">ordered</span>
<span class="gr">Out[38]: </span><span class="bp">False</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-118">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-119">新的分类数据不会自动排序。</span><span class="yiyi-st" id="yiyi-120">您必须明确传递<code class="docutils literal"><span class="pre">ordered=True</span></code>以指示有序的<code class="docutils literal"><span class="pre">Categorical</span></code>。</span></p>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-121">注意</span></p>
<p><span class="yiyi-st" id="yiyi-122"><code class="docutils literal"><span class="pre">Series.unique()</span></code>的结果并不总是与<code class="docutils literal"><span class="pre">Series.cat.categories</span></code>相同，因为<code class="docutils literal"><span class="pre">Series.unique()</span></code>的保证，即它按出现顺序返回类别，并且它仅包括实际存在的值。</span></p>
<div class="last highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [39]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="s1">'babc'</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'category'</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">'abcd'</span><span class="p">))</span>
<span class="gp">In [40]: </span><span class="n">s</span>
<span class="gr">Out[40]: </span>
<span class="go">0 b</span>
<span class="go">1 a</span>
<span class="go">2 b</span>
<span class="go">3 c</span>
<span class="go">dtype: category</span>
<span class="go">Categories (4, object): [a, b, c, d]</span>
<span class="c"># categories</span>
<span class="gp">In [41]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span>
<span class="gr">Out[41]: </span><span class="n">Index</span><span class="p">([</span><span class="s1">u'a'</span><span class="p">,</span> <span class="s1">u'b'</span><span class="p">,</span> <span class="s1">u'c'</span><span class="p">,</span> <span class="s1">u'd'</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'object'</span><span class="p">)</span>
<span class="c"># uniques</span>
<span class="gp">In [42]: </span><span class="n">s</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="gr">Out[42]: </span>
<span class="go">[b, a, c]</span>
<span class="go">Categories (3, object): [b, a, c]</span>
</pre></div>
</div>
</div>
<div class="section" id="renaming-categories">
<h3><span class="yiyi-st" id="yiyi-123">Renaming categories</span></h3>
<p><span class="yiyi-st" id="yiyi-124">通过向<code class="docutils literal"><span class="pre">Series.cat.categories</span></code>属性分配新值或使用<code class="xref py py-func docutils literal"><span class="pre">Categorical.rename_categories()</span></code>方法重命名类别：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [43]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"category"</span><span class="p">)</span>
<span class="gp">In [44]: </span><span class="n">s</span>
<span class="gr">Out[44]: </span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 a</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [a, b, c]</span>
<span class="gp">In [45]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Group </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">g</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span><span class="p">]</span>
<span class="gp">In [46]: </span><span class="n">s</span>
<span class="gr">Out[46]: </span>
<span class="go">0 Group a</span>
<span class="go">1 Group b</span>
<span class="go">2 Group c</span>
<span class="go">3 Group a</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [Group a, Group b, Group c]</span>
<span class="gp">In [47]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">rename_categories</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">3</span><span class="p">])</span>
<span class="gr">Out[47]: </span>
<span class="go">0 1</span>
<span class="go">1 2</span>
<span class="go">2 3</span>
<span class="go">3 1</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, int64): [1, 2, 3]</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-125">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-126">与R的<cite>因子</cite>相反，分类数据可以具有除字符串之外的其他类型的类别。</span></p>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-127">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-128">请注意，分配新类别是一个内部操作，而大多数<code class="docutils literal"><span class="pre">Series.cat</span></code>下的其他操作默认返回一系列新的类型<cite>类别</cite>。</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-129">类别必须是唯一的或产生<cite>ValueError</cite>：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [48]: </span><span class="k">try</span><span class="p">:</span>
<span class="gp"> ....:</span> <span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="gp"> ....:</span> <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp"> ....:</span> <span class="k">print</span><span class="p">(</span><span class="s2">"ValueError: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span>
<span class="gp"> ....:</span>
<span class="go">ValueError: Categorical categories must be unique</span>
</pre></div>
</div>
</div>
<div class="section" id="appending-new-categories">
<h3><span class="yiyi-st" id="yiyi-130">Appending new categories</span></h3>
<p><span class="yiyi-st" id="yiyi-131">可以使用<code class="xref py py-func docutils literal"><span class="pre">Categorical.add_categories()</span></code>方法来追加类别：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [49]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">add_categories</span><span class="p">([</span><span class="mi">4</span><span class="p">])</span>
<span class="gp">In [50]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span>
<span class="gr">Out[50]: </span><span class="n">Index</span><span class="p">([</span><span class="s1">u'Group a'</span><span class="p">,</span> <span class="s1">u'Group b'</span><span class="p">,</span> <span class="s1">u'Group c'</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'object'</span><span class="p">)</span>
<span class="gp">In [51]: </span><span class="n">s</span>
<span class="gr">Out[51]: </span>
<span class="go">0 Group a</span>
<span class="go">1 Group b</span>
<span class="go">2 Group c</span>
<span class="go">3 Group a</span>
<span class="go">dtype: category</span>
<span class="go">Categories (4, object): [Group a, Group b, Group c, 4]</span>
</pre></div>
</div>
</div>
<div class="section" id="removing-categories">
<h3><span class="yiyi-st" id="yiyi-132">Removing categories</span></h3>
<p><span class="yiyi-st" id="yiyi-133">可以使用<code class="xref py py-func docutils literal"><span class="pre">Categorical.remove_categories()</span></code>方法来删除类别。</span><span class="yiyi-st" id="yiyi-134">删除的值将替换为<code class="docutils literal"><span class="pre">np.nan</span></code>。</span><span class="yiyi-st" id="yiyi-135">：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [52]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">remove_categories</span><span class="p">([</span><span class="mi">4</span><span class="p">])</span>
<span class="gp">In [53]: </span><span class="n">s</span>
<span class="gr">Out[53]: </span>
<span class="go">0 Group a</span>
<span class="go">1 Group b</span>
<span class="go">2 Group c</span>
<span class="go">3 Group a</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [Group a, Group b, Group c]</span>
</pre></div>
</div>
</div>
<div class="section" id="removing-unused-categories">
<h3><span class="yiyi-st" id="yiyi-136">Removing unused categories</span></h3>
<p><span class="yiyi-st" id="yiyi-137">删除未使用的类别也可以：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [54]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">]))</span>
<span class="gp">In [55]: </span><span class="n">s</span>
<span class="gr">Out[55]: </span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 a</span>
<span class="go">dtype: category</span>
<span class="go">Categories (4, object): [a, b, c, d]</span>
<span class="gp">In [56]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">remove_unused_categories</span><span class="p">()</span>
<span class="gr">Out[56]: </span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 a</span>
<span class="go">dtype: category</span>
<span class="go">Categories (2, object): [a, b]</span>
</pre></div>
</div>
</div>
<div class="section" id="setting-categories">
<h3><span class="yiyi-st" id="yiyi-138">Setting categories</span></h3>
<p><span class="yiyi-st" id="yiyi-139">如果您希望在一个步骤中删除并添加新类别（具有一些速度优势），或只是将类别设置为预定义的比例，请使用<code class="xref py py-func docutils literal"><span class="pre">Categorical.set_categories()</span></code>。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [57]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"one"</span><span class="p">,</span><span class="s2">"two"</span><span class="p">,</span><span class="s2">"four"</span><span class="p">,</span> <span class="s2">"-"</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"category"</span><span class="p">)</span>
<span class="gp">In [58]: </span><span class="n">s</span>
<span class="gr">Out[58]: </span>
<span class="go">0 one</span>
<span class="go">1 two</span>
<span class="go">2 four</span>
<span class="go">3 -</span>
<span class="go">dtype: category</span>
<span class="go">Categories (4, object): [-, four, one, two]</span>
<span class="gp">In [59]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">set_categories</span><span class="p">([</span><span class="s2">"one"</span><span class="p">,</span><span class="s2">"two"</span><span class="p">,</span><span class="s2">"three"</span><span class="p">,</span><span class="s2">"four"</span><span class="p">])</span>
<span class="gp">In [60]: </span><span class="n">s</span>
<span class="gr">Out[60]: </span>
<span class="go">0 one</span>
<span class="go">1 two</span>
<span class="go">2 four</span>
<span class="go">3 NaN</span>
<span class="go">dtype: category</span>
<span class="go">Categories (4, object): [one, two, three, four]</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-140">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-141">请注意，<code class="xref py py-func docutils literal"><span class="pre">Categorical.set_categories()</span></code>无法知道某个类别是否被有意省略，或者因为类型差异（例如numpys S1 dtype和python字符串）而拼写错误或（在Python3下）。</span><span class="yiyi-st" id="yiyi-142"></span></p>
</div>
</div>
</div>
<div class="section" id="sorting-and-order">
<h2><span class="yiyi-st" id="yiyi-143">Sorting and Order</span></h2>
<div class="admonition warning" id="categorical-sort">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-144">警告</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-145">默认的构造在v0.16.0中从先前隐式的<code class="docutils literal"><span class="pre">ordered=True</span></code>改变为<code class="docutils literal"><span class="pre">ordered=False</span></code></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-146">如果对类别数据排序（<code class="docutils literal"><span class="pre">s.cat.ordered</span> <span class="pre">==</span> <span class="pre">True</span></code>），具有含义并且某些操作是可能的。</span><span class="yiyi-st" id="yiyi-147">如果分类是无序的，<code class="docutils literal"><span class="pre">.min()/.max()</span></code>会引发一个<cite>TypeError</cite>。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [61]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">False</span><span class="p">))</span>
<span class="gp">In [62]: </span><span class="n">s</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [63]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'category'</span><span class="p">,</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [64]: </span><span class="n">s</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [65]: </span><span class="n">s</span>
<span class="gr">Out[65]: </span>
<span class="go">0 a</span>
<span class="go">3 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [a < b < c]</span>
<span class="gp">In [66]: </span><span class="n">s</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">s</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="gr">Out[66]: </span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">)</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-148">您可以使用<code class="docutils literal"><span class="pre">as_ordered()</span></code>或使用<code class="docutils literal"><span class="pre">as_unordered()</span></code>无序排序设置要排序的分类数据。</span><span class="yiyi-st" id="yiyi-149">这些将默认返回<em>新</em>对象。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [67]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">as_ordered</span><span class="p">()</span>
<span class="gr">Out[67]: </span>
<span class="go">0 a</span>
<span class="go">3 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [a < b < c]</span>
<span class="gp">In [68]: </span><span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">as_unordered</span><span class="p">()</span>
<span class="gr">Out[68]: </span>
<span class="go">0 a</span>
<span class="go">3 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [a, b, c]</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-150">排序将使用类别定义的顺序，而不是数据类型上存在的任何词法顺序。</span><span class="yiyi-st" id="yiyi-151">这对于字符串和数字数据是正确的：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [69]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</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">3</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"category"</span><span class="p">)</span>
<span class="gp">In [70]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">set_categories</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [71]: </span><span class="n">s</span>
<span class="gr">Out[71]: </span>
<span class="go">0 1</span>
<span class="go">1 2</span>
<span class="go">2 3</span>
<span class="go">3 1</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, int64): [2 < 3 < 1]</span>
<span class="gp">In [72]: </span><span class="n">s</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [73]: </span><span class="n">s</span>
<span class="gr">Out[73]: </span>
<span class="go">1 2</span>
<span class="go">2 3</span>
<span class="go">0 1</span>
<span class="go">3 1</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, int64): [2 < 3 < 1]</span>
<span class="gp">In [74]: </span><span class="n">s</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">s</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="gr">Out[74]: </span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="reordering">
<h3><span class="yiyi-st" id="yiyi-152">Reordering</span></h3>
<p><span class="yiyi-st" id="yiyi-153">可以通过<code class="xref py py-func docutils literal"><span class="pre">Categorical.reorder_categories()</span></code>和<code class="xref py py-func docutils literal"><span class="pre">Categorical.set_categories()</span></code>方法重新排序类别。</span><span class="yiyi-st" id="yiyi-154">对于<code class="xref py py-func docutils literal"><span class="pre">Categorical.reorder_categories()</span></code>，所有旧类别都必须包含在新类别中，不允许使用新类别。</span><span class="yiyi-st" id="yiyi-155">这将必然使排序顺序与类别顺序相同。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [75]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</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">3</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"category"</span><span class="p">)</span>
<span class="gp">In [76]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">reorder_categories</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [77]: </span><span class="n">s</span>
<span class="gr">Out[77]: </span>
<span class="go">0 1</span>
<span class="go">1 2</span>
<span class="go">2 3</span>
<span class="go">3 1</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, int64): [2 < 3 < 1]</span>
<span class="gp">In [78]: </span><span class="n">s</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [79]: </span><span class="n">s</span>
<span class="gr">Out[79]: </span>
<span class="go">1 2</span>
<span class="go">2 3</span>
<span class="go">0 1</span>
<span class="go">3 1</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, int64): [2 < 3 < 1]</span>
<span class="gp">In [80]: </span><span class="n">s</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">s</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="gr">Out[80]: </span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-156">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-157">请注意分配新类别和重新排序类别之间的差异：首先重命名类别，因此在<cite>系列</cite>中的各个值，但如果第一个位置最后排序，重命名的值仍将最后排序。</span><span class="yiyi-st" id="yiyi-158">重新排序意味着值的排序方式不同，但不是<cite>系列</cite>中的单个值发生更改。</span></p>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-159">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-160">如果<cite>分类</cite>未排序，<code class="docutils literal"><span class="pre">Series.min()</span></code>和<code class="docutils literal"><span class="pre">Series.max()</span></code>会引发<code class="docutils literal"><span class="pre">TypeError</span></code>。</span><span class="yiyi-st" id="yiyi-161">像<code class="docutils literal"><span class="pre">+</span></code>，<code class="docutils literal"><span class="pre">-</span></code>，<code class="docutils literal"><span class="pre">*</span></code>，<code class="docutils literal"><span class="pre">/</span></code>和基于它们的操作（例如<code class="docutils literal"><span class="pre">Series.median()</span></code>，如果数组的长度是偶数，则需要计算两个值之间的平均值）不起作用，并产生<code class="docutils literal"><span class="pre">TypeError</span></code>。</span></p>
</div>
</div>
<div class="section" id="multi-column-sorting">
<h3><span class="yiyi-st" id="yiyi-162">Multi Column Sorting</span></h3>
<p><span class="yiyi-st" id="yiyi-163">分类类型列将以与其他列类似的方式参与多列排序。</span><span class="yiyi-st" id="yiyi-164">分类的排序由该列的<code class="docutils literal"><span class="pre">categories</span></code>确定。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [81]: </span><span class="n">dfs</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">'A'</span> <span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="s1">'bbeebbaa'</span><span class="p">),</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s1">'e'</span><span class="p">,</span><span class="s1">'a'</span><span class="p">,</span><span class="s1">'b'</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">True</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="s1">'B'</span> <span class="p">:</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="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="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="p">})</span>
<span class="gp"> ....:</span>
<span class="gp">In [82]: </span><span class="n">dfs</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">])</span>
<span class="gr">Out[82]: </span>
<span class="go"> A B</span>
<span class="go">2 e 1</span>
<span class="go">3 e 2</span>
<span class="go">7 a 1</span>
<span class="go">6 a 2</span>
<span class="go">0 b 1</span>
<span class="go">5 b 1</span>
<span class="go">1 b 2</span>
<span class="go">4 b 2</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-165">重新排序<code class="docutils literal"><span class="pre">categories</span></code>会更改未来排序。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [83]: </span><span class="n">dfs</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfs</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">reorder_categories</span><span class="p">([</span><span class="s1">'a'</span><span class="p">,</span><span class="s1">'b'</span><span class="p">,</span><span class="s1">'e'</span><span class="p">])</span>
<span class="gp">In [84]: </span><span class="n">dfs</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span><span class="s1">'B'</span><span class="p">])</span>
<span class="gr">Out[84]: </span>
<span class="go"> A B</span>
<span class="go">7 a 1</span>
<span class="go">6 a 2</span>
<span class="go">0 b 1</span>
<span class="go">5 b 1</span>
<span class="go">1 b 2</span>
<span class="go">4 b 2</span>
<span class="go">2 e 1</span>
<span class="go">3 e 2</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="comparisons">
<h2><span class="yiyi-st" id="yiyi-166">Comparisons</span></h2>
<p><span class="yiyi-st" id="yiyi-167">在三种情况下可以比较分类数据与其他对象：</span></p>
<blockquote>
<div><ul class="simple">
<li><span class="yiyi-st" id="yiyi-168">将等式（<code class="docutils literal"><span class="pre">==</span></code>和<code class="docutils literal"><span class="pre">!=</span></code>）与类别对象（列表，系列，数组，...）进行比较，其长度与分类数据相同。</span></li>
<li><span class="yiyi-st" id="yiyi-169">all comparisons (<code class="docutils literal"><span class="pre">==</span></code>, <code class="docutils literal"><span class="pre">!=</span></code>, <code class="docutils literal"><span class="pre">></span></code>, <code class="docutils literal"><span class="pre">>=</span></code>, <code class="docutils literal"><span class="pre"><</span></code>, and <code class="docutils literal"><span class="pre"><=</span></code>) of categorical data to another categorical Series, when <code class="docutils literal"><span class="pre">ordered==True</span></code> and the <cite>categories</cite> are the same.</span></li>
<li><span class="yiyi-st" id="yiyi-170">分类数据与标量的所有比较。</span></li>
</ul>
</div></blockquote>
<p><span class="yiyi-st" id="yiyi-171">所有其他比较，特别是具有不同类别的两个分类的“非等同”比较，或者具有任何类似列表的对象的分类，将引起TypeError。</span></p>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-172">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-173">对分类数据与<cite>系列</cite>，<cite>np.array</cite>，<cite>列表</cite>或具有不同类别或排序的分类数据的任何“不等同”比较<cite>TypeError</cite>，因为自定义类别排序可以用两种方式解释：一种考虑到排序，一种没有。</span></p>
</div>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [85]: </span><span class="n">cat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</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">3</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"category"</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</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">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [86]: </span><span class="n">cat_base</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</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">2</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"category"</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</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">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [87]: </span><span class="n">cat_base2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</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">2</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"category"</span><span class="p">,</span> <span class="n">ordered</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">In [88]: </span><span class="n">cat</span>
<span class="gr">Out[88]: </span>
<span class="go">0 1</span>
<span class="go">1 2</span>
<span class="go">2 3</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, int64): [3 < 2 < 1]</span>
<span class="gp">In [89]: </span><span class="n">cat_base</span>
<span class="gr">Out[89]: </span>
<span class="go">0 2</span>
<span class="go">1 2</span>
<span class="go">2 2</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, int64): [3 < 2 < 1]</span>
<span class="gp">In [90]: </span><span class="n">cat_base2</span>
<span class="gr">Out[90]: </span>
<span class="go">0 2</span>
<span class="go">1 2</span>
<span class="go">2 2</span>
<span class="go">dtype: category</span>
<span class="go">Categories (1, int64): [2]</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-174">比较具有相同类别和排序或标量作品的分类：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [91]: </span><span class="n">cat</span> <span class="o">></span> <span class="n">cat_base</span>
<span class="gr">Out[91]: </span>
<span class="go">0 True</span>
<span class="go">1 False</span>
<span class="go">2 False</span>
<span class="go">dtype: bool</span>
<span class="gp">In [92]: </span><span class="n">cat</span> <span class="o">></span> <span class="mi">2</span>
<span class="gr">Out[92]: </span>
<span class="go">0 True</span>
<span class="go">1 False</span>
<span class="go">2 False</span>
<span class="go">dtype: bool</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-175">平等比较与任何相同长度和标量的类似列表对象一起使用：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [93]: </span><span class="n">cat</span> <span class="o">==</span> <span class="n">cat_base</span>
<span class="gr">Out[93]: </span>
<span class="go">0 False</span>
<span class="go">1 True</span>
<span class="go">2 False</span>
<span class="go">dtype: bool</span>
<span class="gp">In [94]: </span><span class="n">cat</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">array</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">3</span><span class="p">])</span>
<span class="gr">Out[94]: </span>
<span class="go">0 True</span>
<span class="go">1 True</span>
<span class="go">2 True</span>
<span class="go">dtype: bool</span>
<span class="gp">In [95]: </span><span class="n">cat</span> <span class="o">==</span> <span class="mi">2</span>
<span class="gr">Out[95]: </span>
<span class="go">0 False</span>
<span class="go">1 True</span>
<span class="go">2 False</span>
<span class="go">dtype: bool</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-176">这不工作，因为类别不一样：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [96]: </span><span class="k">try</span><span class="p">:</span>
<span class="gp"> ....:</span> <span class="n">cat</span> <span class="o">></span> <span class="n">cat_base2</span>
<span class="gp"> ....:</span> <span class="k">except</span> <span class="ne">TypeError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp"> ....:</span> <span class="k">print</span><span class="p">(</span><span class="s2">"TypeError: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span>
<span class="gp"> ....:</span>
<span class="go">TypeError: Categoricals can only be compared if 'categories' are the same</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-177">如果要对类别序列与不是分类数据的类似列表对象执行“非等同”比较，则需要显式并将分类数据转换回原始值：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [97]: </span><span class="n">base</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</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">3</span><span class="p">])</span>
<span class="gp">In [98]: </span><span class="k">try</span><span class="p">:</span>
<span class="gp"> ....:</span> <span class="n">cat</span> <span class="o">></span> <span class="n">base</span>
<span class="gp"> ....:</span> <span class="k">except</span> <span class="ne">TypeError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp"> ....:</span> <span class="k">print</span><span class="p">(</span><span class="s2">"TypeError: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span>
<span class="gp"> ....:</span>
<span class="go">TypeError: Cannot compare a Categorical for op __gt__ with type <type 'numpy.ndarray'>.</span>
<span class="go">If you want to compare values, use 'np.asarray(cat) <op> other'.</span>
<span class="gp">In [99]: </span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">cat</span><span class="p">)</span> <span class="o">></span> <span class="n">base</span>
<span class="gr">Out[99]: </span><span class="n">array</span><span class="p">([</span><span class="bp">False</span><span class="p">,</span> <span class="bp">False</span><span class="p">,</span> <span class="bp">False</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="operations">
<h2><span class="yiyi-st" id="yiyi-178">Operations</span></h2>
<p><span class="yiyi-st" id="yiyi-179">除了<code class="docutils literal"><span class="pre">Series.min()</span></code>，<code class="docutils literal"><span class="pre">Series.max()</span></code>和<code class="docutils literal"><span class="pre">Series.mode()</span></code>，以下操作对于分类数据是可能的：</span></p>
<p><span class="yiyi-st" id="yiyi-180"><cite>Series.value_counts()</cite>的<cite>系列</cite>方法将使用所有类别，即使数据中不存在某些类别：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [100]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">]))</span>
<span class="gp">In [101]: </span><span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="gr">Out[101]: </span>
<span class="go">c 2</span>
<span class="go">b 1</span>
<span class="go">a 1</span>
<span class="go">d 0</span>
<span class="go">dtype: int64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-181">Groupby还将显示“未使用”类别：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [102]: </span><span class="n">cats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">])</span>
<span class="gp">In [103]: </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="s2">"cats"</span><span class="p">:</span><span class="n">cats</span><span class="p">,</span><span class="s2">"values"</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="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]})</span>
<span class="gp">In [104]: </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"cats"</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="gr">Out[104]: </span>
<span class="go"> values</span>
<span class="go">cats </span>
<span class="go">a 1.0</span>
<span class="go">b 2.0</span>
<span class="go">c 4.0</span>
<span class="go">d NaN</span>
<span class="gp">In [105]: </span><span class="n">cats2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">])</span>
<span class="gp">In [106]: </span><span class="n">df2</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="s2">"cats"</span><span class="p">:</span><span class="n">cats2</span><span class="p">,</span><span class="s2">"B"</span><span class="p">:[</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">],</span> <span class="s2">"values"</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">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]})</span>
<span class="gp">In [107]: </span><span class="n">df2</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">"cats"</span><span class="p">,</span><span class="s2">"B"</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="gr">Out[107]: </span>
<span class="go"> values</span>
<span class="go">cats B </span>
<span class="go">a c 1.0</span>
<span class="go"> d 2.0</span>
<span class="go">b c 3.0</span>
<span class="go"> d 4.0</span>
<span class="go">c c NaN</span>
<span class="go"> d NaN</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-182">数据透视表：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [108]: </span><span class="n">raw_cat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">])</span>
<span class="gp">In [109]: </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="s2">"A"</span><span class="p">:</span><span class="n">raw_cat</span><span class="p">,</span><span class="s2">"B"</span><span class="p">:[</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">],</span> <span class="s2">"values"</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">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]})</span>
<span class="gp">In [110]: </span><span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">'values'</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">])</span>
<span class="gr">Out[110]: </span>
<span class="go">A B</span>
<span class="go">a c 1.0</span>
<span class="go"> d 2.0</span>
<span class="go">b c 3.0</span>
<span class="go"> d 4.0</span>
<span class="go">c c NaN</span>
<span class="go"> d NaN</span>
<span class="go">Name: values, dtype: float64</span>
</pre></div>
</div>
</div>
<div class="section" id="data-munging">
<h2><span class="yiyi-st" id="yiyi-183">Data munging</span></h2>
<p><span class="yiyi-st" id="yiyi-184">优化的pandas数据访问方法<code class="docutils literal"><span class="pre">.loc</span></code>，<code class="docutils literal"><span class="pre">.iloc</span></code>，<code class="docutils literal"><span class="pre">.ix</span></code> <code class="docutils literal"><span class="pre">.at</span></code>和<code class="docutils literal"><span class="pre">.iat</span></code>，正常工作。</span><span class="yiyi-st" id="yiyi-185">唯一的区别是返回类型（用于获取），并且只能分配<cite>类别</cite>中的值。</span></p>
<div class="section" id="getting">
<h3><span class="yiyi-st" id="yiyi-186">Getting</span></h3>
<p><span class="yiyi-st" id="yiyi-187">如果切片操作返回<cite>DataFrame</cite>或类型<cite>系列</cite>的列，则会保留<code class="docutils literal"><span class="pre">category</span></code> dtype。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [111]: </span><span class="n">idx</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">([</span><span class="s2">"h"</span><span class="p">,</span><span class="s2">"i"</span><span class="p">,</span><span class="s2">"j"</span><span class="p">,</span><span class="s2">"k"</span><span class="p">,</span><span class="s2">"l"</span><span class="p">,</span><span class="s2">"m"</span><span class="p">,</span><span class="s2">"n"</span><span class="p">,])</span>
<span class="gp">In [112]: </span><span class="n">cats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"category"</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">idx</span><span class="p">)</span>
<span class="gp">In [113]: </span><span class="n">values</span><span class="o">=</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="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
<span class="gp">In [114]: </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="s2">"cats"</span><span class="p">:</span><span class="n">cats</span><span class="p">,</span><span class="s2">"values"</span><span class="p">:</span><span class="n">values</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="n">idx</span><span class="p">)</span>
<span class="gp">In [115]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">,:]</span>
<span class="gr">Out[115]: </span>
<span class="go"> cats values</span>
<span class="go">j b 2</span>
<span class="go">k b 2</span>
<span class="gp">In [116]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">,:]</span><span class="o">.</span><span class="n">dtypes</span>
<span class="gr">Out[116]: </span>
<span class="go">cats category</span>
<span class="go">values int64</span>
<span class="go">dtype: object</span>
<span class="gp">In [117]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"h"</span><span class="p">:</span><span class="s2">"j"</span><span class="p">,</span><span class="s2">"cats"</span><span class="p">]</span>
<span class="gr">Out[117]: </span>
<span class="go">h a</span>
<span class="go">i b</span>
<span class="go">j b</span>
<span class="go">Name: cats, dtype: category</span>
<span class="go">Categories (3, object): [a, b, c]</span>
<span class="gp">In [118]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="s2">"h"</span><span class="p">:</span><span class="s2">"j"</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="gr">Out[118]: </span>
<span class="go"> cats</span>
<span class="go">h a</span>
<span class="go">i b</span>
<span class="go">j b</span>
<span class="gp">In [119]: </span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">"cats"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"b"</span><span class="p">]</span>
<span class="gr">Out[119]: </span>
<span class="go"> cats values</span>
<span class="go">i b 2</span>
<span class="go">j b 2</span>
<span class="go">k b 2</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-188">未保留类别类型的示例是，如果您只需要一行：生成的<cite>Series</cite>是dtype <code class="docutils literal"><span class="pre">object</span></code>：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="c"># get the complete "h" row as a Series</span>
<span class="gp">In [120]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"h"</span><span class="p">,</span> <span class="p">:]</span>
<span class="gr">Out[120]: </span>
<span class="go">cats a</span>
<span class="go">values 1</span>
<span class="go">Name: h, dtype: object</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-189">从分类数据返回单个项目也将返回值，而不是长度为“1”的分类。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [121]: </span><span class="n">df</span><span class="o">.</span><span class="n">iat</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="gr">Out[121]: </span><span class="s1">'a'</span>
<span class="gp">In [122]: </span><span class="n">df</span><span class="p">[</span><span class="s2">"cats"</span><span class="p">]</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"x"</span><span class="p">,</span><span class="s2">"y"</span><span class="p">,</span><span class="s2">"z"</span><span class="p">]</span>
<span class="gp">In [123]: </span><span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s2">"h"</span><span class="p">,</span><span class="s2">"cats"</span><span class="p">]</span> <span class="c1"># returns a string</span>
<span class="gr">Out[123]: </span><span class="s1">'x'</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-190">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-191">这与R的<cite>因子</cite>函数不同，其中<code class="docutils literal"><span class="pre">factor(c(1,2,3))[1]</span></code>返回单个值<cite>。</cite></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-192">要获取类型<code class="docutils literal"><span class="pre">category</span></code>类型的单个值<cite>Series</cite>，请传入具有单个值的列表：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [124]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s2">"h"</span><span class="p">],</span><span class="s2">"cats"</span><span class="p">]</span>
<span class="gr">Out[124]: </span>
<span class="go">h x</span>
<span class="go">Name: cats, dtype: category</span>
<span class="go">Categories (3, object): [x, y, z]</span>
</pre></div>
</div>
</div>
<div class="section" id="string-and-datetime-accessors">
<h3><span class="yiyi-st" id="yiyi-193">String and datetime accessors</span></h3>
<div class="versionadded">
<p><span class="yiyi-st" id="yiyi-194"><span class="versionmodified">版本0.17.1中的新功能。</span></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-195">如果<code class="docutils literal"><span class="pre">s.cat.categories</span></code>是适当类型，则访问器<code class="docutils literal"><span class="pre">.dt</span></code>和<code class="docutils literal"><span class="pre">.str</span></code></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [125]: </span><span class="n">str_s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="s1">'aabb'</span><span class="p">))</span>
<span class="gp">In [126]: </span><span class="n">str_cat</span> <span class="o">=</span> <span class="n">str_s</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'category'</span><span class="p">)</span>
<span class="gp">In [127]: </span><span class="n">str_cat</span>
<span class="gr">Out[127]: </span>
<span class="go">0 a</span>
<span class="go">1 a</span>
<span class="go">2 b</span>
<span class="go">3 b</span>
<span class="go">dtype: category</span>
<span class="go">Categories (2, object): [a, b]</span>
<span class="gp">In [128]: </span><span class="n">str_cat</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s2">"a"</span><span class="p">)</span>
<span class="gr">Out[128]: </span>
<span class="go">0 True</span>
<span class="go">1 True</span>
<span class="go">2 False</span>
<span class="go">3 False</span>
<span class="go">dtype: bool</span>
<span class="gp">In [129]: </span><span class="n">date_s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'1/1/2015'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
<span class="gp">In [130]: </span><span class="n">date_cat</span> <span class="o">=</span> <span class="n">date_s</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'category'</span><span class="p">)</span>
<span class="gp">In [131]: </span><span class="n">date_cat</span>
<span class="gr">Out[131]: </span>
<span class="go">0 2015-01-01</span>
<span class="go">1 2015-01-02</span>
<span class="go">2 2015-01-03</span>
<span class="go">3 2015-01-04</span>
<span class="go">4 2015-01-05</span>
<span class="go">dtype: category</span>
<span class="go">Categories (5, datetime64[ns]): [2015-01-01, 2015-01-02, 2015-01-03, 2015-01-04, 2015-01-05]</span>
<span class="gp">In [132]: </span><span class="n">date_cat</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">day</span>
<span class="gr">Out[132]: </span>
<span class="go">0 1</span>
<span class="go">1 2</span>
<span class="go">2 3</span>
<span class="go">3 4</span>
<span class="go">4 5</span>
<span class="go">dtype: int64</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-196">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-197">The returned <code class="docutils literal"><span class="pre">Series</span></code> (or <code class="docutils literal"><span class="pre">DataFrame</span></code>) is of the same type as if you used the <code class="docutils literal"><span class="pre">.str.<method></span></code> / <code class="docutils literal"><span class="pre">.dt.<method></span></code> on a <code class="docutils literal"><span class="pre">Series</span></code> of that type (and not of type <code class="docutils literal"><span class="pre">category</span></code>! </span><span class="yiyi-st" id="yiyi-198">）。</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-199">这意味着，从<code class="docutils literal"><span class="pre">Series</span></code>的访问器上的方法和属性返回的值以及此<code class="docutils literal"><span class="pre">Series</span></code>的访问器上的方法和属性的返回值都转换为类型之一<cite>类别</cite>将相等：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [133]: </span><span class="n">ret_s</span> <span class="o">=</span> <span class="n">str_s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s2">"a"</span><span class="p">)</span>
<span class="gp">In [134]: </span><span class="n">ret_cat</span> <span class="o">=</span> <span class="n">str_cat</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s2">"a"</span><span class="p">)</span>
<span class="gp">In [135]: </span><span class="n">ret_s</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">ret_cat</span><span class="o">.</span><span class="n">dtype</span>
<span class="gr">Out[135]: </span><span class="bp">True</span>
<span class="gp">In [136]: </span><span class="n">ret_s</span> <span class="o">==</span> <span class="n">ret_cat</span>
<span class="gr">Out[136]: </span>
<span class="go">0 True</span>
<span class="go">1 True</span>
<span class="go">2 True</span>
<span class="go">3 True</span>
<span class="go">dtype: bool</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-200">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-201">工作在<code class="docutils literal"><span class="pre">categories</span></code>上完成，然后构建新的<code class="docutils literal"><span class="pre">Series</span></code>。</span><span class="yiyi-st" id="yiyi-202">如果你有一个<code class="docutils literal"><span class="pre">Series</span></code>类型字符串，其中很多元素被重复（即，<code class="docutils literal"><span class="pre">Series</span></code>中的唯一元素的数量比<code class="docutils literal"><span class="pre">Series</span></code>的长度）。</span><span class="yiyi-st" id="yiyi-203">在这种情况下，将原始<code class="docutils literal"><span class="pre">Series</span></code>转换为<code class="docutils literal"><span class="pre">category</span></code>之一并使用<code class="docutils literal"><span class="pre">.str.<method></span></code>或<code class="docutils literal"><span class="pre">.dt.<property></span></code>。</span></p>
</div>
</div>
<div class="section" id="setting">
<h3><span class="yiyi-st" id="yiyi-204">Setting</span></h3>
<p><span class="yiyi-st" id="yiyi-205">在分类列（或<cite>系列</cite>）中设置值可以工作，只要该值包含在<cite>类别</cite>中：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [137]: </span><span class="n">idx</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">([</span><span class="s2">"h"</span><span class="p">,</span><span class="s2">"i"</span><span class="p">,</span><span class="s2">"j"</span><span class="p">,</span><span class="s2">"k"</span><span class="p">,</span><span class="s2">"l"</span><span class="p">,</span><span class="s2">"m"</span><span class="p">,</span><span class="s2">"n"</span><span class="p">])</span>
<span class="gp">In [138]: </span><span class="n">cats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">])</span>
<span class="gp">In [139]: </span><span class="n">values</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="gp">In [140]: </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="s2">"cats"</span><span class="p">:</span><span class="n">cats</span><span class="p">,</span><span class="s2">"values"</span><span class="p">:</span><span class="n">values</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="n">idx</span><span class="p">)</span>
<span class="gp">In [141]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">,:]</span> <span class="o">=</span> <span class="p">[[</span><span class="s2">"b"</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="s2">"b"</span><span class="p">,</span><span class="mi">2</span><span class="p">]]</span>
<span class="gp">In [142]: </span><span class="n">df</span>
<span class="gr">Out[142]: </span>
<span class="go"> cats values</span>
<span class="go">h a 1</span>
<span class="go">i a 1</span>
<span class="go">j b 2</span>
<span class="go">k b 2</span>
<span class="go">l a 1</span>
<span class="go">m a 1</span>
<span class="go">n a 1</span>
<span class="gp">In [143]: </span><span class="k">try</span><span class="p">:</span>
<span class="gp"> .....:</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">,:]</span> <span class="o">=</span> <span class="p">[[</span><span class="s2">"c"</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="s2">"c"</span><span class="p">,</span><span class="mi">3</span><span class="p">]]</span>
<span class="gp"> .....:</span> <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp"> .....:</span> <span class="k">print</span><span class="p">(</span><span class="s2">"ValueError: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span>
<span class="gp"> .....:</span>
<span class="go">ValueError: Cannot setitem on a Categorical with a new category, set the categories first</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-206">通过指定分类数据设置值还将检查<cite>类别</cite>是否匹配：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [144]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"j"</span><span class="p">:</span><span class="s2">"k"</span><span class="p">,</span><span class="s2">"cats"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">])</span>
<span class="gp">In [145]: </span><span class="n">df</span>
<span class="gr">Out[145]: </span>
<span class="go"> cats values</span>
<span class="go">h a 1</span>
<span class="go">i a 1</span>
<span class="go">j a 2</span>
<span class="go">k a 2</span>
<span class="go">l a 1</span>
<span class="go">m a 1</span>
<span class="go">n a 1</span>
<span class="gp">In [146]: </span><span class="k">try</span><span class="p">:</span>
<span class="gp"> .....:</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"j"</span><span class="p">:</span><span class="s2">"k"</span><span class="p">,</span><span class="s2">"cats"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">])</span>
<span class="gp"> .....:</span> <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp"> .....:</span> <span class="k">print</span><span class="p">(</span><span class="s2">"ValueError: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span>
<span class="gp"> .....:</span>
<span class="go">ValueError: Cannot set a Categorical with another, without identical categories</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-207">为其他类型的列的某些部分分配<cite>分类</cite>将使用以下值：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [147]: </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="s2">"a"</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="s2">"b"</span><span class="p">:[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"a"</span><span class="p">]})</span>
<span class="gp">In [148]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</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="s2">"a"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">])</span>
<span class="gp">In [149]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s2">"b"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">])</span>
<span class="gp">In [150]: </span><span class="n">df</span>
<span class="gr">Out[150]: </span>
<span class="go"> a b</span>
<span class="go">0 1 a</span>
<span class="go">1 b a</span>
<span class="go">2 b b</span>
<span class="go">3 1 b</span>
<span class="go">4 1 a</span>
<span class="gp">In [151]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
<span class="gr">Out[151]: </span>
<span class="go">a object</span>
<span class="go">b object</span>
<span class="go">dtype: object</span>
</pre></div>
</div>
</div>
<div class="section" id="merging">
<h3><span class="yiyi-st" id="yiyi-208">Merging</span></h3>
<p><span class="yiyi-st" id="yiyi-209">您可以将包含分类数据的两个<cite>DataFrames</cite>合并在一起，但这些分类的类别需要相同：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [152]: </span><span class="n">cat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"category"</span><span class="p">)</span>
<span class="gp">In [153]: </span><span class="n">vals</span> <span class="o">=</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="gp">In [154]: </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="s2">"cats"</span><span class="p">:</span><span class="n">cat</span><span class="p">,</span> <span class="s2">"vals"</span><span class="p">:</span><span class="n">vals</span><span class="p">})</span>
<span class="gp">In [155]: </span><span class="n">res</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span><span class="n">df</span><span class="p">])</span>
<span class="gp">In [156]: </span><span class="n">res</span>
<span class="gr">Out[156]: </span>
<span class="go"> cats vals</span>
<span class="go">0 a 1</span>
<span class="go">1 b 2</span>
<span class="go">0 a 1</span>
<span class="go">1 b 2</span>
<span class="gp">In [157]: </span><span class="n">res</span><span class="o">.</span><span class="n">dtypes</span>
<span class="gr">Out[157]: </span>
<span class="go">cats category</span>
<span class="go">vals int64</span>
<span class="go">dtype: object</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-210">在这种情况下，类别不一样，因此会出现错误：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [158]: </span><span class="n">df_different</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="gp">In [159]: </span><span class="n">df_different</span><span class="p">[</span><span class="s2">"cats"</span><span class="p">]</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"c"</span><span class="p">,</span><span class="s2">"d"</span><span class="p">]</span>
<span class="gp">In [160]: </span><span class="k">try</span><span class="p">:</span>
<span class="gp"> .....:</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span><span class="n">df_different</span><span class="p">])</span>
<span class="gp"> .....:</span> <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>