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<span id="indexing"></span><h1><span class="yiyi-st" id="yiyi-83">Indexing and Selecting Data</span></h1>
<blockquote>
<p>原文：<a href="http://pandas.pydata.org/pandas-docs/stable/indexing.html">http://pandas.pydata.org/pandas-docs/stable/indexing.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>
<p><span class="yiyi-st" id="yiyi-84">pandas对象中的轴标签信息有很多用途：</span></p>
<blockquote>
<div><ul class="simple">
<li><span class="yiyi-st" id="yiyi-85">使用已知指标识别数据（即提供<em>元数据</em>），对于分析，可视化和交互式控制台显示非常重要</span></li>
<li><span class="yiyi-st" id="yiyi-86">启用自动和显式数据对齐</span></li>
<li><span class="yiyi-st" id="yiyi-87">允许直观获取和设置数据集的子集</span></li>
</ul>
</div></blockquote>
<p><span class="yiyi-st" id="yiyi-88">在本节中，我们将关注最后一点：即，如何切片，删除，以及一般获取和设置pandas对象的子集。</span><span class="yiyi-st" id="yiyi-89">主要关注的是系列和数据框架，因为他们在这一领域受到更多的发展关注。</span><span class="yiyi-st" id="yiyi-90">期望在将来对更高维数据结构（包括<code class="docutils literal"><span class="pre">Panel</span></code>）投入更多的工作，尤其是在基于标签的高级索引中。</span></p>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-91">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-92">Python和NumPy索引操作符<code class="docutils literal"><span class="pre">[]</span></code>和属性操作符<code class="docutils literal"><span class="pre">.</span></code></span><span class="yiyi-st" id="yiyi-93">提供在大范围的用例中快速和容易地访问pandas数据结构。</span><span class="yiyi-st" id="yiyi-94">这使得交互式工作直观，因为如果你已经知道如何处理Python字典和NumPy数组，没有什么新的学习。</span><span class="yiyi-st" id="yiyi-95">但是，由于要访问的数据的类型并不是事先知道的，直接使用标准操作符有一些优化的限制。</span><span class="yiyi-st" id="yiyi-96">对于生产代码，我们建议您利用本章中介绍的优化的熊猫数据访问方法。</span></p>
</div>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-97">警告</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-98">是否为设置操作返回副本或引用可以取决于上下文。</span><span class="yiyi-st" id="yiyi-99">这有时被称为<code class="docutils literal"><span class="pre">链式</span><span class="pre">赋值</span></code>，应该避免。</span><span class="yiyi-st" id="yiyi-100">请参见<a class="reference internal" href="#indexing-view-versus-copy"><span class="std std-ref">Returning a View versus Copy</span></a></span></p>
</div>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-101">警告</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-102">在0.15.0 <code class="docutils literal"><span class="pre">Index</span></code>内部被重构为不再子类化<code class="docutils literal"><span class="pre">ndarray</span></code>，而是子类化<code class="docutils literal"><span class="pre">PandasObject</span></code>，类似于其余的pandas对象。</span><span class="yiyi-st" id="yiyi-103">这应该是一个透明的变化，只有非常有限的API影响（参见<a class="reference internal" href="whatsnew.html#whatsnew-0150-refactoring"><span class="std std-ref">Internal Refactoring</span></a>）</span></p>
</div>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-104">警告</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-105">对于具有浮点数的整数索引的索引已经在0.18.0中阐明，对于变化的总结，参见<a class="reference internal" href="whatsnew.html#whatsnew-0180-float-indexers"><span class="std std-ref">here</span></a>。</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-106">有关<code class="docutils literal"><span class="pre">MultiIndex</span></code>和更高级的索引文档，请参阅<a class="reference internal" href="advanced.html#advanced"><span class="std std-ref">MultiIndex / Advanced Indexing</span></a>。</span></p>
<p><span class="yiyi-st" id="yiyi-107">有关某些高级策略，请参阅<a class="reference internal" href="cookbook.html#cookbook-selection"><span class="std std-ref">cookbook</span></a></span></p>
<div class="section" id="different-choices-for-indexing">
<h2><span class="yiyi-st" id="yiyi-108">Different Choices for Indexing</span></h2>
<div class="versionadded">
<p><span class="yiyi-st" id="yiyi-109"><span class="versionmodified">版本0.11.0中的新功能。</span></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-110">对象选择已经有了许多用户请求的添加，以便支持更明确的基于位置的索引。</span><span class="yiyi-st" id="yiyi-111">pandas现在支持三种类型的多轴索引。</span></p>
<ul>
<li><p class="first"><span class="yiyi-st" id="yiyi-112"><code class="docutils literal"><span class="pre">.loc</span></code>主要是基于标签的，但也可以与布尔数组一起使用。</span><span class="yiyi-st" id="yiyi-113"><code class="docutils literal"><span class="pre">.loc</span></code>会在找不到项目时产生<code class="docutils literal"><span class="pre">KeyError</span></code>。</span><span class="yiyi-st" id="yiyi-114">允许输入为：</span></p>
<ul>
<li><p class="first"><span class="yiyi-st" id="yiyi-115">单个标签，例如<code class="docutils literal"><span class="pre">5</span></code>或<code class="docutils literal"><span class="pre">'a'</span></code>（注意，<code class="docutils literal"><span class="pre">5</span></code>被解释为索引的<em>标签</em>。</span><span class="yiyi-st" id="yiyi-116">这种使用是<strong>不是</strong>沿着索引的整数位置）</span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-117">标签的列表或数组<code class="docutils literal"><span class="pre">['a'，</span> <span class="pre">'b'，</span> <span class="pre">'c']</span> </code></span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-118">具有标签<code class="docutils literal"><span class="pre">'a':'f'</span></code>的切片对象（请注意，与通常的python切片相反，<strong></strong>包括开始和停止）</span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-119">一个布尔数组</span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-120">具有一个参数（调用Series，DataFrame或Panel）的<code class="docutils literal"><span class="pre">callable</span></code>函数，并返回有效的索引输出（上述之一）</span></p>
<blockquote>
<div><div class="versionadded">
<p><span class="yiyi-st" id="yiyi-121"><span class="versionmodified">版本0.18.1中的新功能。</span></span></p>
</div>
</div></blockquote>
</li>
</ul>
<p><span class="yiyi-st" id="yiyi-122">有关详情，请参阅<a class="reference internal" href="#indexing-label"><span class="std std-ref">Selection by Label</span></a></span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-123"><code class="docutils literal"><span class="pre">.iloc</span></code>主要是基于整数位置（从轴的<code class="docutils literal"><span class="pre">0</span></code>到<code class="docutils literal"><span class="pre">length-1</span></code>），但也可以与布尔数组一起使用。</span><span class="yiyi-st" id="yiyi-124">如果所请求的索引器超出边界，则<code class="docutils literal"><span class="pre">.iloc</span></code>将引发<code class="docutils literal"><span class="pre">IndexError</span></code>，除了允许超出索引的<em>slice</em>索引器。</span><span class="yiyi-st" id="yiyi-125">（这符合python / numpy <em>slice</em>语义）。</span><span class="yiyi-st" id="yiyi-126">允许输入为：</span></p>
<ul>
<li><p class="first"><span class="yiyi-st" id="yiyi-127">整数例如<code class="docutils literal"><span class="pre">5</span></code></span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-128">整数列表或数组<code class="docutils literal"><span class="pre">[4，</span> <span class="pre">3，</span> <span class="pre">0]</span></code></span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-129">int <code class="docutils literal"><span class="pre">1:7</span></code>的slice对象</span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-130">一个布尔数组</span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-131">具有一个参数（调用Series，DataFrame或Panel）的<code class="docutils literal"><span class="pre">callable</span></code>函数，并返回有效的索引输出（上述之一）</span></p>
<blockquote>
<div><div class="versionadded">
<p><span class="yiyi-st" id="yiyi-132"><span class="versionmodified">版本0.18.1中的新功能。</span></span></p>
</div>
</div></blockquote>
</li>
</ul>
<p><span class="yiyi-st" id="yiyi-133">详情请参阅<a class="reference internal" href="#indexing-integer"><span class="std std-ref">Selection by Position</span></a></span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-134"><code class="docutils literal"><span class="pre">.ix</span></code>支持混合整数和基于标签的访问。</span><span class="yiyi-st" id="yiyi-135">它主要是基于标签的，但将回落到整数位置访问，除非相应的轴是整数类型。</span><span class="yiyi-st" id="yiyi-136"><code class="docutils literal"><span class="pre">.ix</span></code>是最常用的，并且将支持<code class="docutils literal"><span class="pre">.loc</span></code>和<code class="docutils literal"><span class="pre">.iloc</span></code>中的任何输入。</span><span class="yiyi-st" id="yiyi-137"><code class="docutils literal"><span class="pre">.ix</span></code>也支持浮点标签方案。</span><span class="yiyi-st" id="yiyi-138"><code class="docutils literal"><span class="pre">.ix</span></code>在处理混合位置和基于标签的分层索引时非常有用。</span></p>
<p><span class="yiyi-st" id="yiyi-139">但是，当轴是基于整数的时，仅支持基于标签的访问，而不支持位置访问。</span><span class="yiyi-st" id="yiyi-140">因此，在这种情况下，通常最好是显式并使用<code class="docutils literal"><span class="pre">.iloc</span></code>或<code class="docutils literal"><span class="pre">.loc</span></code>。</span></p>
<p><span class="yiyi-st" id="yiyi-141">详情请参阅<a class="reference internal" href="advanced.html#advanced"><span class="std std-ref">Advanced Indexing</span></a>和<a class="reference internal" href="advanced.html#advanced-advanced-hierarchical"><span class="std std-ref">Advanced Hierarchical</span></a>。</span></p>
</li>
<li><p class="first"><span class="yiyi-st" id="yiyi-142"><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>以及<code class="docutils literal"><span class="pre">[]</span></code>索引可以接受<code class="docutils literal"><span class="pre">callable</span></code></span><span class="yiyi-st" id="yiyi-143">查看更多<a class="reference internal" href="#indexing-callable"><span class="std std-ref">Selection By Callable</span></a>。</span></p>
</li>
</ul>
<p><span class="yiyi-st" id="yiyi-144">从多轴选择的对象获取值使用以下符号（使用<code class="docutils literal"><span class="pre">.loc</span></code>作为示例，但适用于<code class="docutils literal"><span class="pre">.iloc</span></code>和<code class="docutils literal"><span class="pre">.ix</span></code></span><span class="yiyi-st" id="yiyi-145">任何轴访问器可以是空片<code class="docutils literal"><span class="pre">:</span></code>。</span><span class="yiyi-st" id="yiyi-146">忽略规范的轴假定为<code class="docutils literal"><span class="pre">:</span></code>。</span><span class="yiyi-st" id="yiyi-147">（例如<code class="docutils literal"><span class="pre">p.loc['a']</span></code>等同于<code class="docutils literal"><span class="pre">p.loc ['a'，</span> <span class="pre">：，</span> <span class="pre">：]</span></code>）</span></p>
<table border="1" class="docutils">
<colgroup>
<col width="38%">
<col width="63%">
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"><span class="yiyi-st" id="yiyi-148">对象类型</span></th>
<th class="head"><span class="yiyi-st" id="yiyi-149">索引器</span></th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-150">Series</span></td>
<td><span class="yiyi-st" id="yiyi-151"><code class="docutils literal"><span class="pre">s.loc[indexer]</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-152">DataFrame</span></td>
<td><span class="yiyi-st" id="yiyi-153"><code class="docutils literal"><span class="pre">df.loc[row_indexer,column_indexer]</span></code></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-154">Panel</span></td>
<td><span class="yiyi-st" id="yiyi-155"><code class="docutils literal"><span class="pre">p.loc[item_indexer,major_indexer,minor_indexer]</span></code></span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="basics">
<span id="indexing-basics"></span><h2><span class="yiyi-st" id="yiyi-156">基础知识</span></h2>
<p><span class="yiyi-st" id="yiyi-157">如在在<a class="reference internal" href="basics.html#basics"><span class="std std-ref">last section</span></a>中引入数据结构时提到的，用<code class="docutils literal"><span class="pre">[]</span></code>（a.k.a.</span><span class="yiyi-st" id="yiyi-158"><code class="docutils literal"><span class="pre">__getitem__</span></code>用于熟悉在Python中实现类行为的人）选择低维切片。</span><span class="yiyi-st" id="yiyi-159">从而，</span></p>
<table border="1" class="docutils">
<colgroup>
<col width="25%">
<col width="25%">
<col width="50%">
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"><span class="yiyi-st" id="yiyi-160">对象类型</span></th>
<th class="head"><span class="yiyi-st" id="yiyi-161">选择</span></th>
<th class="head"><span class="yiyi-st" id="yiyi-162">返回值类型</span></th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-163">Series</span></td>
<td><span class="yiyi-st" id="yiyi-164"><code class="docutils literal"><span class="pre">series[label]</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-165">标量值</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-166">DataFrame</span></td>
<td><span class="yiyi-st" id="yiyi-167"><code class="docutils literal"><span class="pre">frame[colname]</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-168"><code class="docutils literal"><span class="pre">Series</span></code>对应于colname</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-169">Panel</span></td>
<td><span class="yiyi-st" id="yiyi-170"><code class="docutils literal"><span class="pre">panel[itemname]</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-171"><code class="docutils literal"><span class="pre">DataFrame</span></code>对应于项目名称</span></td>
</tr>
</tbody>
</table>
<p><span class="yiyi-st" id="yiyi-172">这里我们构造一个简单的时间序列数据集用于说明索引功能：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'1/1/2000'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="gp">In [2]: </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="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="n">dates</span><span class="p">,</span> <span class="n">columns</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="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">])</span>
<span class="gp">In [3]: </span><span class="n">df</span>
<span class="gr">Out[3]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2000-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2000-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="go">2000-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
<span class="go">2000-01-05 -0.424972 0.567020 0.276232 -1.087401</span>
<span class="go">2000-01-06 -0.673690 0.113648 -1.478427 0.524988</span>
<span class="go">2000-01-07 0.404705 0.577046 -1.715002 -1.039268</span>
<span class="go">2000-01-08 -0.370647 -1.157892 -1.344312 0.844885</span>
<span class="gp">In [4]: </span><span class="n">panel</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Panel</span><span class="p">({</span><span class="s1">'one'</span> <span class="p">:</span> <span class="n">df</span><span class="p">,</span> <span class="s1">'two'</span> <span class="p">:</span> <span class="n">df</span> <span class="o">-</span> <span class="n">df</span><span class="o">.</span><span class="n">mean</span><span class="p">()})</span>
<span class="gp">In [5]: </span><span class="n">panel</span>
<span class="gr">Out[5]: </span>
<span class="go"><class 'pandas.core.panel.Panel'></span>
<span class="go">Dimensions: 2 (items) x 8 (major_axis) x 4 (minor_axis)</span>
<span class="go">Items axis: one to two</span>
<span class="go">Major_axis axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00</span>
<span class="go">Minor_axis axis: A to D</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-173">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-174">除非特别声明，否则索引功能不是时间序列特定的。</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-175">因此，如上所述，我们有使用<code class="docutils literal"><span class="pre">[]</span></code>的最基本的索引：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span>
<span class="gp">In [7]: </span><span class="n">s</span><span class="p">[</span><span class="n">dates</span><span class="p">[</span><span class="mi">5</span><span class="p">]]</span>
<span class="gr">Out[7]: </span><span class="o">-</span><span class="mf">0.67368970808837059</span>
<span class="gp">In [8]: </span><span class="n">panel</span><span class="p">[</span><span class="s1">'two'</span><span class="p">]</span>
<span class="gr">Out[8]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-01 0.409571 0.113086 -0.610826 -0.936507</span>
<span class="go">2000-01-02 1.152571 0.222735 1.017442 -0.845111</span>
<span class="go">2000-01-03 -0.921390 -1.708620 0.403304 1.270929</span>
<span class="go">2000-01-04 0.662014 -0.310822 -0.141342 0.470985</span>
<span class="go">2000-01-05 -0.484513 0.962970 1.174465 -0.888276</span>
<span class="go">2000-01-06 -0.733231 0.509598 -0.580194 0.724113</span>
<span class="go">2000-01-07 0.345164 0.972995 -0.816769 -0.840143</span>
<span class="go">2000-01-08 -0.430188 -0.761943 -0.446079 1.044010</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-176">您可以将列的列表传递到<code class="docutils literal"><span class="pre">[]</span></code>，以按该顺序选择列。</span><span class="yiyi-st" id="yiyi-177">如果DataFrame中不包含列，则会引发异常。</span><span class="yiyi-st" id="yiyi-178">也可以以这种方式设置多个列：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [9]: </span><span class="n">df</span>
<span class="gr">Out[9]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2000-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2000-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="go">2000-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
<span class="go">2000-01-05 -0.424972 0.567020 0.276232 -1.087401</span>
<span class="go">2000-01-06 -0.673690 0.113648 -1.478427 0.524988</span>
<span class="go">2000-01-07 0.404705 0.577046 -1.715002 -1.039268</span>
<span class="go">2000-01-08 -0.370647 -1.157892 -1.344312 0.844885</span>
<span class="gp">In [10]: </span><span class="n">df</span><span class="p">[[</span><span class="s1">'B'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</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="gp">In [11]: </span><span class="n">df</span>
<span class="gr">Out[11]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-01 -0.282863 0.469112 -1.509059 -1.135632</span>
<span class="go">2000-01-02 -0.173215 1.212112 0.119209 -1.044236</span>
<span class="go">2000-01-03 -2.104569 -0.861849 -0.494929 1.071804</span>
<span class="go">2000-01-04 -0.706771 0.721555 -1.039575 0.271860</span>
<span class="go">2000-01-05 0.567020 -0.424972 0.276232 -1.087401</span>
<span class="go">2000-01-06 0.113648 -0.673690 -1.478427 0.524988</span>
<span class="go">2000-01-07 0.577046 0.404705 -1.715002 -1.039268</span>
<span class="go">2000-01-08 -1.157892 -0.370647 -1.344312 0.844885</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-179">您可能会发现这对于将变换（就地）应用于列的子集很有用。</span></p>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-180">警告</span></p>
<p><span class="yiyi-st" id="yiyi-181">pandas aligns all AXES when setting <code class="docutils literal"><span class="pre">Series</span></code> and <code class="docutils literal"><span class="pre">DataFrame</span></code> from <code class="docutils literal"><span class="pre">.loc</span></code>, <code class="docutils literal"><span class="pre">.iloc</span></code> and <code class="docutils literal"><span class="pre">.ix</span></code>.</span></p>
<p><span class="yiyi-st" id="yiyi-182">这将<strong>不会</strong>修改<code class="docutils literal"><span class="pre">df</span></code>，因为列对齐在赋值之前。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [12]: </span><span class="n">df</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[12]: </span>
<span class="go"> A B</span>
<span class="go">2000-01-01 -0.282863 0.469112</span>
<span class="go">2000-01-02 -0.173215 1.212112</span>
<span class="go">2000-01-03 -2.104569 -0.861849</span>
<span class="go">2000-01-04 -0.706771 0.721555</span>
<span class="go">2000-01-05 0.567020 -0.424972</span>
<span class="go">2000-01-06 0.113648 -0.673690</span>
<span class="go">2000-01-07 0.577046 0.404705</span>
<span class="go">2000-01-08 -1.157892 -0.370647</span>
<span class="gp">In [13]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,[</span><span class="s1">'B'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</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="gp">In [14]: </span><span class="n">df</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[14]: </span>
<span class="go"> A B</span>
<span class="go">2000-01-01 -0.282863 0.469112</span>
<span class="go">2000-01-02 -0.173215 1.212112</span>
<span class="go">2000-01-03 -2.104569 -0.861849</span>
<span class="go">2000-01-04 -0.706771 0.721555</span>
<span class="go">2000-01-05 0.567020 -0.424972</span>
<span class="go">2000-01-06 0.113648 -0.673690</span>
<span class="go">2000-01-07 0.577046 0.404705</span>
<span class="go">2000-01-08 -1.157892 -0.370647</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-183">正确的方法是使用原始值</span></p>
<div class="last highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [15]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,[</span><span class="s1">'B'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</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="o">.</span><span class="n">values</span>
<span class="gp">In [16]: </span><span class="n">df</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[16]: </span>
<span class="go"> A B</span>
<span class="go">2000-01-01 0.469112 -0.282863</span>
<span class="go">2000-01-02 1.212112 -0.173215</span>
<span class="go">2000-01-03 -0.861849 -2.104569</span>
<span class="go">2000-01-04 0.721555 -0.706771</span>
<span class="go">2000-01-05 -0.424972 0.567020</span>
<span class="go">2000-01-06 -0.673690 0.113648</span>
<span class="go">2000-01-07 0.404705 0.577046</span>
<span class="go">2000-01-08 -0.370647 -1.157892</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="attribute-access">
<h2><span class="yiyi-st" id="yiyi-184">Attribute Access</span></h2>
<p id="indexing-attribute-access"><span class="yiyi-st" id="yiyi-185"><span id="indexing-df-cols"></span><span id="indexing-columns-multiple"></span>You may access an index on a <code class="docutils literal"><span class="pre">Series</span></code>, column on a <code class="docutils literal"><span class="pre">DataFrame</span></code>, and an item on a <code class="docutils literal"><span class="pre">Panel</span></code> directly as an attribute:</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [17]: </span><span class="n">sa</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="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">'abc'</span><span class="p">))</span>
<span class="gp">In [18]: </span><span class="n">dfa</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
</pre></div>
</div>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [19]: </span><span class="n">sa</span><span class="o">.</span><span class="n">b</span>
<span class="gr">Out[19]: </span><span class="mi">2</span>
<span class="gp">In [20]: </span><span class="n">dfa</span><span class="o">.</span><span class="n">A</span>
<span class="gr">Out[20]: </span>
<span class="go">2000-01-01 0.469112</span>
<span class="go">2000-01-02 1.212112</span>
<span class="go">2000-01-03 -0.861849</span>
<span class="go">2000-01-04 0.721555</span>
<span class="go">2000-01-05 -0.424972</span>
<span class="go">2000-01-06 -0.673690</span>
<span class="go">2000-01-07 0.404705</span>
<span class="go">2000-01-08 -0.370647</span>
<span class="go">Freq: D, Name: A, dtype: float64</span>
<span class="gp">In [21]: </span><span class="n">panel</span><span class="o">.</span><span class="n">one</span>
<span class="gr">Out[21]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2000-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2000-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="go">2000-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
<span class="go">2000-01-05 -0.424972 0.567020 0.276232 -1.087401</span>
<span class="go">2000-01-06 -0.673690 0.113648 -1.478427 0.524988</span>
<span class="go">2000-01-07 0.404705 0.577046 -1.715002 -1.039268</span>
<span class="go">2000-01-08 -0.370647 -1.157892 -1.344312 0.844885</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-186">您可以使用属性访问来修改DataFrame的系列或列的现有元素，但要小心；如果您尝试使用属性访问来创建新列，则会无声地失败，从而创建新属性而不是新列。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [22]: </span><span class="n">sa</span><span class="o">.</span><span class="n">a</span> <span class="o">=</span> <span class="mi">5</span>
<span class="gp">In [23]: </span><span class="n">sa</span>
<span class="gr">Out[23]: </span>
<span class="go">a 5</span>
<span class="go">b 2</span>
<span class="go">c 3</span>
<span class="go">dtype: int64</span>
<span class="gp">In [24]: </span><span class="n">dfa</span><span class="o">.</span><span class="n">A</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dfa</span><span class="o">.</span><span class="n">index</span><span class="p">)))</span> <span class="c1"># ok if A already exists</span>
<span class="gp">In [25]: </span><span class="n">dfa</span>
<span class="gr">Out[25]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-01 0 -0.282863 -1.509059 -1.135632</span>
<span class="go">2000-01-02 1 -0.173215 0.119209 -1.044236</span>
<span class="go">2000-01-03 2 -2.104569 -0.494929 1.071804</span>
<span class="go">2000-01-04 3 -0.706771 -1.039575 0.271860</span>
<span class="go">2000-01-05 4 0.567020 0.276232 -1.087401</span>
<span class="go">2000-01-06 5 0.113648 -1.478427 0.524988</span>
<span class="go">2000-01-07 6 0.577046 -1.715002 -1.039268</span>
<span class="go">2000-01-08 7 -1.157892 -1.344312 0.844885</span>
<span class="gp">In [26]: </span><span class="n">dfa</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dfa</span><span class="o">.</span><span class="n">index</span><span class="p">)))</span> <span class="c1"># use this form to create a new column</span>
<span class="gp">In [27]: </span><span class="n">dfa</span>
<span class="gr">Out[27]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-01 0 -0.282863 -1.509059 -1.135632</span>
<span class="go">2000-01-02 1 -0.173215 0.119209 -1.044236</span>
<span class="go">2000-01-03 2 -2.104569 -0.494929 1.071804</span>
<span class="go">2000-01-04 3 -0.706771 -1.039575 0.271860</span>
<span class="go">2000-01-05 4 0.567020 0.276232 -1.087401</span>
<span class="go">2000-01-06 5 0.113648 -1.478427 0.524988</span>
<span class="go">2000-01-07 6 0.577046 -1.715002 -1.039268</span>
<span class="go">2000-01-08 7 -1.157892 -1.344312 0.844885</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-187">警告</span></p>
<ul class="last simple">
<li><span class="yiyi-st" id="yiyi-188">只有当索引元素是有效的Python标识符时，才能使用此访问。 <code class="docutils literal"><span class="pre">s.1</span></code>不允许。</span><span class="yiyi-st" id="yiyi-189">有关有效标识符的说明，请参见<a class="reference external" href="http://docs.python.org/2.7/reference/lexical_analysis.html#identifiers">。</a></span></li>
<li><span class="yiyi-st" id="yiyi-190">如果该属性与现有方法名称冲突，则该属性将不可用。 <code class="docutils literal"><span class="pre">s.min</span></code>不允许。</span></li>
<li><span class="yiyi-st" id="yiyi-191">Similarly, the attribute will not be available if it conflicts with any of the following list: <code class="docutils literal"><span class="pre">index</span></code>, <code class="docutils literal"><span class="pre">major_axis</span></code>, <code class="docutils literal"><span class="pre">minor_axis</span></code>, <code class="docutils literal"><span class="pre">items</span></code>, <code class="docutils literal"><span class="pre">labels</span></code>.</span></li>
<li><span class="yiyi-st" id="yiyi-192">在任何这些情况下，标准索引仍将工作，例如。 <code class="docutils literal"><span class="pre">s['1']</span></code>，<code class="docutils literal"><span class="pre">s['min']</span></code>和<code class="docutils literal"><span class="pre">s['index']</span></code>将访问相应的元素或列。</span></li>
<li><span class="yiyi-st" id="yiyi-193"><code class="docutils literal"><span class="pre">Series/Panel</span></code>访问从0.13.0开始提供。</span></li>
</ul>
</div>
<p><span class="yiyi-st" id="yiyi-194">如果使用IPython环境，您还可以使用制表符完成来查看这些可访问的属性。</span></p>
<p><span class="yiyi-st" id="yiyi-195">您还可以将<code class="docutils literal"><span class="pre">dict</span></code>分配给<code class="docutils literal"><span class="pre">DataFrame</span></code>的行：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [28]: </span><span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'x'</span><span class="p">:</span> <span class="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="s1">'y'</span><span class="p">:</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 [29]: </span><span class="n">x</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">99</span><span class="p">)</span>
<span class="gp">In [30]: </span><span class="n">x</span>
<span class="gr">Out[30]: </span>
<span class="go"> x y</span>
<span class="go">0 1 3</span>
<span class="go">1 9 99</span>
<span class="go">2 3 5</span>
</pre></div>
</div>
</div>
<div class="section" id="slicing-ranges">
<h2><span class="yiyi-st" id="yiyi-196">Slicing ranges</span></h2>
<p><span class="yiyi-st" id="yiyi-197">在<code class="docutils literal"><span class="pre">.iloc</span></code>方法的<a class="reference internal" href="#indexing-integer"><span class="std std-ref">Selection by Position</span></a>部分中描述了沿任意轴切分范围的最稳健和一致的方法。</span><span class="yiyi-st" id="yiyi-198">现在，我们使用<code class="docutils literal"><span class="pre">[]</span></code>运算符解释切片的语义。</span></p>
<p><span class="yiyi-st" id="yiyi-199">使用Series，语法与ndarray完全一样，返回值的一部分和相应的标签：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [31]: </span><span class="n">s</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
<span class="gr">Out[31]: </span>
<span class="go">2000-01-01 0.469112</span>
<span class="go">2000-01-02 1.212112</span>
<span class="go">2000-01-03 -0.861849</span>
<span class="go">2000-01-04 0.721555</span>
<span class="go">2000-01-05 -0.424972</span>
<span class="go">Freq: D, Name: A, dtype: float64</span>
<span class="gp">In [32]: </span><span class="n">s</span><span class="p">[::</span><span class="mi">2</span><span class="p">]</span>
<span class="gr">Out[32]: </span>
<span class="go">2000-01-01 0.469112</span>
<span class="go">2000-01-03 -0.861849</span>
<span class="go">2000-01-05 -0.424972</span>
<span class="go">2000-01-07 0.404705</span>
<span class="go">Freq: 2D, Name: A, dtype: float64</span>
<span class="gp">In [33]: </span><span class="n">s</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="gr">Out[33]: </span>
<span class="go">2000-01-08 -0.370647</span>
<span class="go">2000-01-07 0.404705</span>
<span class="go">2000-01-06 -0.673690</span>
<span class="go">2000-01-05 -0.424972</span>
<span class="go">2000-01-04 0.721555</span>
<span class="go">2000-01-03 -0.861849</span>
<span class="go">2000-01-02 1.212112</span>
<span class="go">2000-01-01 0.469112</span>
<span class="go">Freq: -1D, Name: A, dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-200">请注意，也可以通过切片赋值：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [34]: </span><span class="n">s2</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="gp">In [35]: </span><span class="n">s2</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="gp">In [36]: </span><span class="n">s2</span>
<span class="gr">Out[36]: </span>
<span class="go">2000-01-01 0.000000</span>
<span class="go">2000-01-02 0.000000</span>
<span class="go">2000-01-03 0.000000</span>
<span class="go">2000-01-04 0.000000</span>
<span class="go">2000-01-05 0.000000</span>
<span class="go">2000-01-06 -0.673690</span>
<span class="go">2000-01-07 0.404705</span>
<span class="go">2000-01-08 -0.370647</span>
<span class="go">Freq: D, Name: A, dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-201">使用DataFrame，在<code class="docutils literal"><span class="pre">[]</span></code> <strong>内切片将</strong>切片。</span><span class="yiyi-st" id="yiyi-202">这主要是为了方便，因为它是这样的常见操作。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [37]: </span><span class="n">df</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="gr">Out[37]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2000-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2000-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="gp">In [38]: </span><span class="n">df</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="gr">Out[38]: </span>
<span class="go"> A B C D</span>
<span class="go">2000-01-08 -0.370647 -1.157892 -1.344312 0.844885</span>
<span class="go">2000-01-07 0.404705 0.577046 -1.715002 -1.039268</span>
<span class="go">2000-01-06 -0.673690 0.113648 -1.478427 0.524988</span>
<span class="go">2000-01-05 -0.424972 0.567020 0.276232 -1.087401</span>
<span class="go">2000-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
<span class="go">2000-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="go">2000-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2000-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
</pre></div>
</div>
</div>
<div class="section" id="selection-by-label">
<span id="indexing-label"></span><h2><span class="yiyi-st" id="yiyi-203">Selection By Label</span></h2>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-204">警告</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-205">是否为设置操作返回副本或引用可以取决于上下文。</span><span class="yiyi-st" id="yiyi-206">这有时被称为<code class="docutils literal"><span class="pre">链接</span> <span class="pre">分配</span></code>，应该避免。</span><span class="yiyi-st" id="yiyi-207">请参见<a class="reference internal" href="#indexing-view-versus-copy"><span class="std std-ref">Returning a View versus Copy</span></a></span></p>
</div>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-208">警告</span></p>
<blockquote>
<div><span class="yiyi-st" id="yiyi-209"><code class="docutils literal"><span class="pre">.loc</span></code>在您呈现不兼容（或可转换）索引类型的切片器时是严格的。</span><span class="yiyi-st" id="yiyi-210">例如，在<code class="docutils literal"><span class="pre">DatetimeIndex</span></code>中使用整数。</span><span class="yiyi-st" id="yiyi-211">这将产生<code class="docutils literal"><span class="pre">TypeError</span></code>。</span></div></blockquote>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [39]: </span><span class="n">dfl</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="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">columns</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="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'20130101'</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 [40]: </span><span class="n">dfl</span>
<span class="gr">Out[40]: </span>
<span class="go"> A B C D</span>
<span class="go">2013-01-01 1.075770 -0.109050 1.643563 -1.469388</span>
<span class="go">2013-01-02 0.357021 -0.674600 -1.776904 -0.968914</span>
<span class="go">2013-01-03 -1.294524 0.413738 0.276662 -0.472035</span>
<span class="go">2013-01-04 -0.013960 -0.362543 -0.006154 -0.923061</span>
<span class="go">2013-01-05 0.895717 0.805244 -1.206412 2.565646</span>
</pre></div>
</div>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">dfl</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="go">TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'></span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-212">在切片<em>中的字符串喜欢</em>可以转换为索引的类型，并导致自然切片。</span></p>
<div class="last highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [41]: </span><span class="n">dfl</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'20130102'</span><span class="p">:</span><span class="s1">'20130104'</span><span class="p">]</span>
<span class="gr">Out[41]: </span>
<span class="go"> A B C D</span>
<span class="go">2013-01-02 0.357021 -0.674600 -1.776904 -0.968914</span>
<span class="go">2013-01-03 -1.294524 0.413738 0.276662 -0.472035</span>
<span class="go">2013-01-04 -0.013960 -0.362543 -0.006154 -0.923061</span>
</pre></div>
</div>
</div>
<p><span class="yiyi-st" id="yiyi-213">pandas提供了一套方法，以便<strong>完全基于标签的索引</strong>。</span><span class="yiyi-st" id="yiyi-214">这是一个严格的包含协议。</span><span class="yiyi-st" id="yiyi-215">您要求的标签的<strong>至少1个</strong>必须在索引中，否则会出现<code class="docutils literal"><span class="pre">KeyError</span></code>！</span><span class="yiyi-st" id="yiyi-216">当切片时，起始边界是<em>包括</em>，<strong>AND</strong>停止边界是<em>包括</em>。</span><span class="yiyi-st" id="yiyi-217">整数是有效标签，但它们指的是标签<strong>，而不是位置</strong>。</span></p>
<p><span class="yiyi-st" id="yiyi-218"><code class="docutils literal"><span class="pre">.loc</span></code>属性是主访问方法。</span><span class="yiyi-st" id="yiyi-219">以下是有效输入：</span></p>
<ul class="simple">
<li><span class="yiyi-st" id="yiyi-220">单个标签，例如<code class="docutils literal"><span class="pre">5</span></code>或<code class="docutils literal"><span class="pre">'a'</span></code>（注意，<code class="docutils literal"><span class="pre">5</span></code>被解释为索引的<em>标签</em>。</span><span class="yiyi-st" id="yiyi-221">这种使用是<strong>不是</strong>沿着索引的整数位置）</span></li>
<li><span class="yiyi-st" id="yiyi-222">标签的列表或数组<code class="docutils literal"><span class="pre">['a'，</span> <span class="pre">'b'，</span> <span class="pre">'c']</span> </code></span></li>
<li><span class="yiyi-st" id="yiyi-223">具有标签<code class="docutils literal"><span class="pre">'a':'f'</span></code>的切片对象（请注意，与通常的python切片相反，<strong></strong>包括开始和停止）</span></li>
<li><span class="yiyi-st" id="yiyi-224">一个布尔数组</span></li>
<li><span class="yiyi-st" id="yiyi-225">A <code class="docutils literal"><span class="pre">callable</span></code>，请参阅<a class="reference internal" href="#indexing-callable"><span class="std std-ref">Selection By Callable</span></a></span></li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [42]: </span><span class="n">s1</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">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">),</span><span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">'abcdef'</span><span class="p">))</span>
<span class="gp">In [43]: </span><span class="n">s1</span>
<span class="gr">Out[43]: </span>
<span class="go">a 1.431256</span>
<span class="go">b 1.340309</span>
<span class="go">c -1.170299</span>
<span class="go">d -0.226169</span>
<span class="go">e 0.410835</span>
<span class="go">f 0.813850</span>
<span class="go">dtype: float64</span>
<span class="gp">In [44]: </span><span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'c'</span><span class="p">:]</span>
<span class="gr">Out[44]: </span>
<span class="go">c -1.170299</span>
<span class="go">d -0.226169</span>
<span class="go">e 0.410835</span>
<span class="go">f 0.813850</span>
<span class="go">dtype: float64</span>
<span class="gp">In [45]: </span><span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'b'</span><span class="p">]</span>
<span class="gr">Out[45]: </span><span class="mf">1.3403088497993827</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-226">请注意，设置也很好：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [46]: </span><span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'c'</span><span class="p">:]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="gp">In [47]: </span><span class="n">s1</span>
<span class="gr">Out[47]: </span>
<span class="go">a 1.431256</span>
<span class="go">b 1.340309</span>
<span class="go">c 0.000000</span>
<span class="go">d 0.000000</span>
<span class="go">e 0.000000</span>
<span class="go">f 0.000000</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-227">用DataFrame</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [48]: </span><span class="n">df1</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="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">'abcdef'</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="n">columns</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"> ....:</span>
<span class="gp">In [49]: </span><span class="n">df1</span>
<span class="gr">Out[49]: </span>
<span class="go"> A B C D</span>
<span class="go">a 0.132003 -0.827317 -0.076467 -1.187678</span>
<span class="go">b 1.130127 -1.436737 -1.413681 1.607920</span>
<span class="go">c 1.024180 0.569605 0.875906 -2.211372</span>
<span class="go">d 0.974466 -2.006747 -0.410001 -0.078638</span>
<span class="go">e 0.545952 -1.219217 -1.226825 0.769804</span>
<span class="go">f -1.281247 -0.727707 -0.121306 -0.097883</span>
<span class="gp">In [50]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</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">'d'</span><span class="p">],</span> <span class="p">:]</span>
<span class="gr">Out[50]: </span>
<span class="go"> A B C D</span>
<span class="go">a 0.132003 -0.827317 -0.076467 -1.187678</span>
<span class="go">b 1.130127 -1.436737 -1.413681 1.607920</span>
<span class="go">d 0.974466 -2.006747 -0.410001 -0.078638</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-228">通过标签切片访问</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [51]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'d'</span><span class="p">:,</span> <span class="s1">'A'</span><span class="p">:</span><span class="s1">'C'</span><span class="p">]</span>
<span class="gr">Out[51]: </span>
<span class="go"> A B C</span>
<span class="go">d 0.974466 -2.006747 -0.410001</span>
<span class="go">e 0.545952 -1.219217 -1.226825</span>
<span class="go">f -1.281247 -0.727707 -0.121306</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-229">要获取使用标签的横截面（等效于<code class="docutils literal"><span class="pre">df.xs('a')</span></code>）</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [52]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'a'</span><span class="p">]</span>
<span class="gr">Out[52]: </span>
<span class="go">A 0.132003</span>
<span class="go">B -0.827317</span>
<span class="go">C -0.076467</span>
<span class="go">D -1.187678</span>
<span class="go">Name: a, dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-230">获取带有布尔数组的值</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [53]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'a'</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span>
<span class="gr">Out[53]: </span>
<span class="go">A True</span>
<span class="go">B False</span>
<span class="go">C False</span>
<span class="go">D False</span>
<span class="go">Name: a, dtype: bool</span>
<span class="gp">In [54]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'a'</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span>
<span class="gr">Out[54]: </span>
<span class="go"> A</span>
<span class="go">a 0.132003</span>
<span class="go">b 1.130127</span>
<span class="go">c 1.024180</span>
<span class="go">d 0.974466</span>
<span class="go">e 0.545952</span>
<span class="go">f -1.281247</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-231">显式获取值（等同于弃用的<code class="docutils literal"><span class="pre">df.get_value('a','A')</span></code>）</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="c"># this is also equivalent to ``df1.at['a','A']``</span>
<span class="gp">In [55]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">]</span>
<span class="gr">Out[55]: </span><span class="mf">0.13200317033032932</span>
</pre></div>
</div>
</div>
<div class="section" id="selection-by-position">
<span id="indexing-integer"></span><h2><span class="yiyi-st" id="yiyi-232">Selection By Position</span></h2>
<div class="admonition warning">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-233">警告</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-234">是否为设置操作返回副本或引用可以取决于上下文。</span><span class="yiyi-st" id="yiyi-235">这有时被称为<code class="docutils literal"><span class="pre">链接</span> <span class="pre">分配</span></code>，应该避免。</span><span class="yiyi-st" id="yiyi-236">请参见<a class="reference internal" href="#indexing-view-versus-copy"><span class="std std-ref">Returning a View versus Copy</span></a></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-237">pandas提供了一套方法，以便获得<strong>纯粹基于整数的索引</strong>。</span><span class="yiyi-st" id="yiyi-238">语义遵循python和numpy切片。</span><span class="yiyi-st" id="yiyi-239">这些是基于<code class="docutils literal"><span class="pre">0-based</span></code>索引。</span><span class="yiyi-st" id="yiyi-240">当切片时，起始边界是<em>包括</em>，而上边界是<em>排除</em>。</span><span class="yiyi-st" id="yiyi-241">尝试使用非整数，即使是<strong>有效的</strong>标签也会产生<code class="docutils literal"><span class="pre">IndexError</span></code>。</span></p>
<p><span class="yiyi-st" id="yiyi-242"><code class="docutils literal"><span class="pre">.iloc</span></code>属性是主要访问方法。</span><span class="yiyi-st" id="yiyi-243">以下是有效输入：</span></p>
<ul class="simple">
<li><span class="yiyi-st" id="yiyi-244">整数例如<code class="docutils literal"><span class="pre">5</span></code></span></li>
<li><span class="yiyi-st" id="yiyi-245">整数列表或数组<code class="docutils literal"><span class="pre">[4，</span> <span class="pre">3，</span> <span class="pre">0]</span></code></span></li>
<li><span class="yiyi-st" id="yiyi-246">int <code class="docutils literal"><span class="pre">1:7</span></code>的slice对象</span></li>
<li><span class="yiyi-st" id="yiyi-247">一个布尔数组</span></li>
<li><span class="yiyi-st" id="yiyi-248">A <code class="docutils literal"><span class="pre">callable</span></code>，请参阅<a class="reference internal" href="#indexing-callable"><span class="std std-ref">Selection By Callable</span></a></span></li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [56]: </span><span class="n">s1</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">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</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">10</span><span class="p">,</span><span class="mi">2</span><span class="p">)))</span>
<span class="gp">In [57]: </span><span class="n">s1</span>
<span class="gr">Out[57]: </span>
<span class="go">0 0.695775</span>
<span class="go">2 0.341734</span>
<span class="go">4 0.959726</span>
<span class="go">6 -1.110336</span>
<span class="go">8 -0.619976</span>
<span class="go">dtype: float64</span>
<span class="gp">In [58]: </span><span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="gr">Out[58]: </span>
<span class="go">0 0.695775</span>
<span class="go">2 0.341734</span>
<span class="go">4 0.959726</span>
<span class="go">dtype: float64</span>
<span class="gp">In [59]: </span><span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="gr">Out[59]: </span><span class="o">-</span><span class="mf">1.1103361028911669</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-249">请注意，iloc也可以用来赋值：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [60]: </span><span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="gp">In [61]: </span><span class="n">s1</span>
<span class="gr">Out[61]: </span>
<span class="go">0 0.000000</span>
<span class="go">2 0.000000</span>
<span class="go">4 0.000000</span>
<span class="go">6 -1.110336</span>
<span class="go">8 -0.619976</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-250">用DataFrame</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [62]: </span><span class="n">df1</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="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</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">12</span><span class="p">,</span><span class="mi">2</span><span class="p">)),</span>
<span class="gp"> ....:</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</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">8</span><span class="p">,</span><span class="mi">2</span><span class="p">)))</span>
<span class="gp"> ....:</span>
<span class="gp">In [63]: </span><span class="n">df1</span>
<span class="gr">Out[63]: </span>
<span class="go"> 0 2 4 6</span>
<span class="go">0 0.149748 -0.732339 0.687738 0.176444</span>
<span class="go">2 0.403310 -0.154951 0.301624 -2.179861</span>
<span class="go">4 -1.369849 -0.954208 1.462696 -1.743161</span>
<span class="go">6 -0.826591 -0.345352 1.314232 0.690579</span>
<span class="go">8 0.995761 2.396780 0.014871 3.357427</span>
<span class="go">10 -0.317441 -1.236269 0.896171 -0.487602</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-251">通过整数切片选择</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [64]: </span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="gr">Out[64]: </span>
<span class="go"> 0 2 4 6</span>
<span class="go">0 0.149748 -0.732339 0.687738 0.176444</span>
<span class="go">2 0.403310 -0.154951 0.301624 -2.179861</span>
<span class="go">4 -1.369849 -0.954208 1.462696 -1.743161</span>
<span class="gp">In [65]: </span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</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="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
<span class="gr">Out[65]: </span>
<span class="go"> 4 6</span>
<span class="go">2 0.301624 -2.179861</span>
<span class="go">4 1.462696 -1.743161</span>
<span class="go">6 1.314232 0.690579</span>
<span class="go">8 0.014871 3.357427</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-252">通过整数列表选择</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [66]: </span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span>
<span class="gr">Out[66]: </span>
<span class="go"> 2 6</span>
<span class="go">2 -0.154951 -2.179861</span>
<span class="go">6 -0.345352 0.690579</span>
<span class="go">10 -1.236269 -0.487602</span>
</pre></div>
</div>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [67]: </span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="p">:]</span>
<span class="gr">Out[67]: </span>
<span class="go"> 0 2 4 6</span>
<span class="go">2 0.403310 -0.154951 0.301624 -2.179861</span>
<span class="go">4 -1.369849 -0.954208 1.462696 -1.743161</span>
</pre></div>
</div>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [68]: </span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
<span class="gr">Out[68]: </span>
<span class="go"> 2 4</span>
<span class="go">0 -0.732339 0.687738</span>
<span class="go">2 -0.154951 0.301624</span>
<span class="go">4 -0.954208 1.462696</span>
<span class="go">6 -0.345352 1.314232</span>
<span class="go">8 2.396780 0.014871</span>
<span class="go">10 -1.236269 0.896171</span>
</pre></div>
</div>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="c"># this is also equivalent to ``df1.iat[1,1]``</span>
<span class="gp">In [69]: </span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</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="gr">Out[69]: </span><span class="o">-</span><span class="mf">0.15495077442490321</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-253">为了获得使用整数位置的横截面（等于<code class="docutils literal"><span class="pre">df.xs(1)</span></code>）</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [70]: </span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="gr">Out[70]: </span>
<span class="go">0 0.403310</span>
<span class="go">2 -0.154951</span>
<span class="go">4 0.301624</span>
<span class="go">6 -2.179861</span>
<span class="go">Name: 2, dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-254">超出范围的切片索引会像在Python / Numpy中一样优雅地处理。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="c"># these are allowed in python/numpy.</span>
<span class="c"># Only works in Pandas starting from v0.14.0.</span>
<span class="gp">In [71]: </span><span class="n">x</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="s1">'abcdef'</span><span class="p">)</span>
<span class="gp">In [72]: </span><span class="n">x</span>
<span class="gr">Out[72]: </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">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">]</span>
<span class="gp">In [73]: </span><span class="n">x</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
<span class="gr">Out[73]: </span><span class="p">[</span><span class="s1">'e'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">]</span>
<span class="gp">In [74]: </span><span class="n">x</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
<span class="gr">Out[74]: </span><span class="p">[]</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="n">x</span><span class="p">)</span>
<span class="gp">In [76]: </span><span class="n">s</span>
<span class="gr">Out[76]: </span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 d</span>
<span class="go">4 e</span>
<span class="go">5 f</span>
<span class="go">dtype: object</span>
<span class="gp">In [77]: </span><span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
<span class="gr">Out[77]: </span>
<span class="go">4 e</span>
<span class="go">5 f</span>
<span class="go">dtype: object</span>
<span class="gp">In [78]: </span><span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
<span class="gr">Out[78]: </span><span class="n">Series</span><span class="p">([],</span> <span class="n">dtype</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-255">注意</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-256">在v0.14.0之前，<code class="docutils literal"><span class="pre">iloc</span></code>将不接受切片的边界索引器，例如。超过被索引的对象的长度的值。</span></p>
</div>
<p><span class="yiyi-st" id="yiyi-257">请注意，这可能会导致空的轴（例如返回一个空DataFrame）</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [79]: </span><span class="n">dfl</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="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">'AB'</span><span class="p">))</span>
<span class="gp">In [80]: </span><span class="n">dfl</span>
<span class="gr">Out[80]: </span>
<span class="go"> A B</span>
<span class="go">0 -0.082240 -2.182937</span>
<span class="go">1 0.380396 0.084844</span>
<span class="go">2 0.432390 1.519970</span>
<span class="go">3 -0.493662 0.600178</span>
<span class="go">4 0.274230 0.132885</span>
<span class="gp">In [81]: </span><span class="n">dfl</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">3</span><span class="p">]</span>
<span class="gr">Out[81]: </span>
<span class="go">Empty DataFrame</span>
<span class="go">Columns: []</span>
<span class="go">Index: [0, 1, 2, 3, 4]</span>
<span class="gp">In [82]: </span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
<span class="gr">Out[82]: </span>
<span class="go"> B</span>
<span class="go">0 -2.182937</span>
<span class="go">1 0.084844</span>
<span class="go">2 1.519970</span>
<span class="go">3 0.600178</span>
<span class="go">4 0.132885</span>
<span class="gp">In [83]: </span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span>
<span class="gr">Out[83]: </span>
<span class="go"> A B</span>
<span class="go">4 0.27423 0.132885</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-258">超出范围的单个索引器将生成<code class="docutils literal"><span class="pre">IndexError</span></code>。</span><span class="yiyi-st" id="yiyi-259">任何元素超出边界的索引器列表将生成<code class="docutils literal"><span class="pre">IndexError</span></code></span></p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</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="mi">6</span><span class="p">]]</span>
<span class="ne">IndexError</span><span class="p">:</span> <span class="n">positional</span> <span class="n">indexers</span> <span class="n">are</span> <span class="n">out</span><span class="o">-</span><span class="n">of</span><span class="o">-</span><span class="n">bounds</span>
<span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span>
<span class="ne">IndexError</span><span class="p">:</span> <span class="n">single</span> <span class="n">positional</span> <span class="n">indexer</span> <span class="ow">is</span> <span class="n">out</span><span class="o">-</span><span class="n">of</span><span class="o">-</span><span class="n">bounds</span>
</pre></div>
</div>
</div>
<div class="section" id="selection-by-callable">
<span id="indexing-callable"></span><h2><span class="yiyi-st" id="yiyi-260">Selection By Callable</span></h2>
<div class="versionadded">
<p><span class="yiyi-st" id="yiyi-261"><span class="versionmodified">版本0.18.1中的新功能。</span></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-262"><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>以及<code class="docutils literal"><span class="pre">[]</span></code>索引可以接受<code class="docutils literal"><span class="pre">callable</span></code></span><span class="yiyi-st" id="yiyi-263"><code class="docutils literal"><span class="pre">callable</span></code>必须是具有一个参数（调用Series，DataFrame或Panel）的函数，并返回有效的索引输出。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [84]: </span><span class="n">df1</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="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">'abcdef'</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="n">columns</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"> ....:</span>
<span class="gp">In [85]: </span><span class="n">df1</span>
<span class="gr">Out[85]: </span>
<span class="go"> A B C D</span>
<span class="go">a -0.023688 2.410179 1.450520 0.206053</span>
<span class="go">b -0.251905 -2.213588 1.063327 1.266143</span>
<span class="go">c 0.299368 -0.863838 0.408204 -1.048089</span>
<span class="go">d -0.025747 -0.988387 0.094055 1.262731</span>
<span class="go">e 1.289997 0.082423 -0.055758 0.536580</span>
<span class="go">f -0.489682 0.369374 -0.034571 -2.484478</span>
<span class="gp">In [86]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">A</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
<span class="gr">Out[86]: </span>
<span class="go"> A B C D</span>
<span class="go">c 0.299368 -0.863838 0.408204 -1.048089</span>
<span class="go">e 1.289997 0.082423 -0.055758 0.536580</span>
<span class="gp">In [87]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="k">lambda</span> <span class="n">df</span><span class="p">:</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[87]: </span>
<span class="go"> A B</span>
<span class="go">a -0.023688 2.410179</span>
<span class="go">b -0.251905 -2.213588</span>
<span class="go">c 0.299368 -0.863838</span>
<span class="go">d -0.025747 -0.988387</span>
<span class="go">e 1.289997 0.082423</span>
<span class="go">f -0.489682 0.369374</span>
<span class="gp">In [88]: </span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="k">lambda</span> <span class="n">df</span><span class="p">:</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[88]: </span>
<span class="go"> A B</span>
<span class="go">a -0.023688 2.410179</span>
<span class="go">b -0.251905 -2.213588</span>
<span class="go">c 0.299368 -0.863838</span>
<span class="go">d -0.025747 -0.988387</span>
<span class="go">e 1.289997 0.082423</span>
<span class="go">f -0.489682 0.369374</span>
<span class="gp">In [89]: </span><span class="n">df1</span><span class="p">[</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
<span class="gr">Out[89]: </span>
<span class="go">a -0.023688</span>
<span class="go">b -0.251905</span>
<span class="go">c 0.299368</span>
<span class="go">d -0.025747</span>
<span class="go">e 1.289997</span>
<span class="go">f -0.489682</span>
<span class="go">Name: A, dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-264">您可以在<code class="docutils literal"><span class="pre">Series</span></code>中使用可调用索引。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [90]: </span><span class="n">df1</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span>
<span class="gr">Out[90]: </span>
<span class="go">c 0.299368</span>
<span class="go">e 1.289997</span>
<span class="go">Name: A, dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-265">使用这些方法/索引器，您可以链接数据选择操作，而不使用临时变量。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [91]: </span><span class="n">bb</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'data/baseball.csv'</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="s1">'id'</span><span class="p">)</span>
<span class="gp">In [92]: </span><span class="p">(</span><span class="n">bb</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'year'</span><span class="p">,</span> <span class="s1">'team'</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="gp"> ....:</span> <span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">r</span> <span class="o">></span> <span class="mi">100</span><span class="p">])</span>
<span class="gp"> ....:</span>
<span class="gr">Out[92]: </span>
<span class="go"> stint g ab r h X2b X3b hr rbi sb cs bb \</span>
<span class="go">year team </span>
<span class="go">2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 </span>
<span class="go"> DET 5 301 1062 162 283 54 4 37 144.0 24.0 7.0 97 </span>
<span class="go"> HOU 4 311 926 109 218 47 6 14 77.0 10.0 4.0 60 </span>
<span class="go"> LAN 11 413 1021 153 293 61 3 36 154.0 7.0 5.0 114 </span>
<span class="go"> NYN 13 622 1854 240 509 101 3 61 243.0 22.0 4.0 174 </span>
<span class="go"> SFN 5 482 1305 198 337 67 6 40 171.0 26.0 7.0 235 </span>
<span class="go"> TEX 2 198 729 115 200 40 4 28 115.0 21.0 4.0 73 </span>
<span class="go"> TOR 4 459 1408 187 378 96 2 58 223.0 4.0 2.0 190 </span>
<span class="go"> so ibb hbp sh sf gidp </span>
<span class="go">year team </span>
<span class="go">2007 CIN 127.0 14.0 1.0 1.0 15.0 18.0 </span>
<span class="go"> DET 176.0 3.0 10.0 4.0 8.0 28.0 </span>
<span class="go"> HOU 212.0 3.0 9.0 16.0 6.0 17.0 </span>
<span class="go"> LAN 141.0 8.0 9.0 3.0 8.0 29.0 </span>
<span class="go"> NYN 310.0 24.0 23.0 18.0 15.0 48.0 </span>
<span class="go"> SFN 188.0 51.0 8.0 16.0 6.0 41.0 </span>
<span class="go"> TEX 140.0 4.0 5.0 2.0 8.0 16.0 </span>
<span class="go"> TOR 265.0 16.0 12.0 4.0 16.0 38.0 </span>
</pre></div>
</div>
</div>
<div class="section" id="selecting-random-samples">
<span id="indexing-basics-partial-setting"></span><h2><span class="yiyi-st" id="yiyi-266">Selecting Random Samples</span></h2>
<p><span class="yiyi-st" id="yiyi-267">使用<a class="reference internal" href="generated/pandas.DataFrame.sample.html#pandas.DataFrame.sample" title="pandas.DataFrame.sample"><code class="xref py py-meth docutils literal"><span class="pre">sample()</span></code></a>方法从Series，DataFrame或Panel随机选择行或列。</span><span class="yiyi-st" id="yiyi-268">该方法将默认对行进行抽样，并接受要返回的特定行数/列数，或一小部分行数。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [93]: </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">0</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="mi">5</span><span class="p">])</span>
<span class="c"># When no arguments are passed, returns 1 row.</span>
<span class="gp">In [94]: </span><span class="n">s</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span>
<span class="gr">Out[94]: </span>
<span class="go">4 4</span>
<span class="go">dtype: int64</span>
<span class="c"># One may specify either a number of rows:</span>
<span class="gp">In [95]: </span><span class="n">s</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gr">Out[95]: </span>
<span class="go">0 0</span>
<span class="go">4 4</span>
<span class="go">1 1</span>
<span class="go">dtype: int64</span>
<span class="c"># Or a fraction of the rows:</span>
<span class="gp">In [96]: </span><span class="n">s</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">frac</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gr">Out[96]: </span>
<span class="go">5 5</span>
<span class="go">3 3</span>
<span class="go">1 1</span>
<span class="go">dtype: int64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-269">默认情况下，<code class="docutils literal"><span class="pre">sample</span></code>最多只返回一行，但也可以使用<code class="docutils literal"><span class="pre">replace</span></code>选项表示能否重复抽取：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [97]: </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">0</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="mi">5</span><span class="p">])</span>
<span class="go"> # Without replacement (default):</span>
<span class="gp">In [98]: </span><span class="n">s</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="gr">Out[98]: </span>
<span class="go">0 0</span>
<span class="go">1 1</span>
<span class="go">5 5</span>
<span class="go">3 3</span>
<span class="go">2 2</span>
<span class="go">4 4</span>
<span class="go">dtype: int64</span>
<span class="go"> # With replacement:</span>
<span class="gp">In [99]: </span><span class="n">s</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gr">Out[99]: </span>
<span class="go">0 0</span>
<span class="go">4 4</span>
<span class="go">3 3</span>
<span class="go">2 2</span>
<span class="go">4 4</span>
<span class="go">4 4</span>
<span class="go">dtype: int64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-270">默认情况下，每行具有相等的选择概率，但如果您希望行具有不同的概率，则可以将<code class="docutils literal"><span class="pre">sample</span></code>函数抽样权重作为<code class="docutils literal"><span class="pre">weights</span></code>。</span><span class="yiyi-st" id="yiyi-271">这些权重可以是列表，numpy数组或系列，但它们必须与要采样的对象长度相同。</span><span class="yiyi-st" id="yiyi-272">缺少的值将被视为零的权重，不允许使用inf值。</span><span class="yiyi-st" id="yiyi-273">如果权重不等于1，则它们将通过将所有权重除以权重的总和而被重新归一化。</span><span class="yiyi-st" id="yiyi-274">例如：</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="mi">0</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="mi">5</span><span class="p">])</span>
<span class="gp">In [101]: </span><span class="n">example_weights</span> <span class="o">=</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="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]</span>
<span class="gp">In [102]: </span><span class="n">s</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">example_weights</span><span class="p">)</span>
<span class="gr">Out[102]: </span>
<span class="go">5 5</span>
<span class="go">4 4</span>
<span class="go">3 3</span>
<span class="go">dtype: int64</span>
<span class="c"># Weights will be re-normalized automatically</span>
<span class="gp">In [103]: </span><span class="n">example_weights2</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="gp">In [104]: </span><span class="n">s</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">example_weights2</span><span class="p">)</span>
<span class="gr">Out[104]: </span>
<span class="go">0 0</span>
<span class="go">dtype: int64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-275">应用于DataFrame时，只需将列的名称作为字符串传递，就可以使用DataFrame的列作为抽样权重（前提是您正在对行进行抽样而不是列）。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [105]: </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="s1">'col1'</span><span class="p">:[</span><span class="mi">9</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span> <span class="s1">'weight_column'</span><span class="p">:[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]})</span>
<span class="gp">In [106]: </span><span class="n">df2</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="s1">'weight_column'</span><span class="p">)</span>
<span class="gr">Out[106]: </span>
<span class="go"> col1 weight_column</span>
<span class="go">1 8 0.4</span>
<span class="go">0 9 0.5</span>
<span class="go">2 7 0.1</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-276"><code class="docutils literal"><span class="pre">sample</span></code>还允许用户使用<code class="docutils literal"><span class="pre">axis</span></code>参数对列而不是行进行抽样。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [107]: </span><span class="n">df3</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">'col1'</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="s1">'col2'</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 [108]: </span><span class="n">df3</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gr">Out[108]: </span>
<span class="go"> col1</span>
<span class="go">0 1</span>
<span class="go">1 2</span>
<span class="go">2 3</span>
</pre></div>