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<span id="compare-with-r"></span><h1><span class="yiyi-st" id="yiyi-67">Comparison with R / R libraries</span></h1>
<blockquote>
<p>原文：<a href="http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html">http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.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-68">由于<code class="docutils literal"><span class="pre">pandas</span></code>旨在提供许多人们使用<a class="reference external" href="http://www.r-project.org/">R</a>的数据操作和分析功能，因此该页面开始提供更详细的<a class="reference external" href="http://en.wikipedia.org/wiki/R_(programming_language)">R语言</a>及其许多第三方库，因为它们与<code class="docutils literal"><span class="pre">pandas</span></code>相关。</span><span class="yiyi-st" id="yiyi-69">在与R和CRAN库进行比较时，我们关心以下事项：</span></p>
<blockquote>
<div><ul class="simple">
<li><span class="yiyi-st" id="yiyi-70"><strong>功能/灵活性</strong>：每个工具都可以/不能做</span></li>
<li><span class="yiyi-st" id="yiyi-71"><strong>性能</strong>：操作速度有多快。</span><span class="yiyi-st" id="yiyi-72">硬数/基准是优选的</span></li>
<li><span class="yiyi-st" id="yiyi-73"><strong>易于使用</strong>：一个工具更容易/更难使用（您可能必须是这个的判断，给定并排的代码比较）</span></li>
</ul>
</div></blockquote>
<p><span class="yiyi-st" id="yiyi-74">此页面也提供了一些翻译指南给这些R包的用户。</span></p>
<p><span class="yiyi-st" id="yiyi-75">对于将<code class="docutils literal"><span class="pre">DataFrame</span></code>对象从<code class="docutils literal"><span class="pre">pandas</span></code>传输到R，一个选项是使用HDF5文件，请参见<a class="reference internal" href="io.html#io-external-compatibility"><span class="std std-ref">External Compatibility</span></a></span></p>
<div class="section" id="quick-reference">
<h2><span class="yiyi-st" id="yiyi-76">Quick Reference</span></h2>
<p><span class="yiyi-st" id="yiyi-77">我们将从一个快速参考指南开始，使用<a class="reference external" href="http://cran.r-project.org/web/packages/dplyr/index.html">dplyr</a>与一些常见的R操作配对Pandas等效。</span></p>
<div class="section" id="querying-filtering-sampling">
<h3><span class="yiyi-st" id="yiyi-78">Querying, Filtering, Sampling</span></h3>
<table border="1" class="docutils">
<colgroup>
<col width="43%">
<col width="57%">
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"><span class="yiyi-st" id="yiyi-79">R</span></th>
<th class="head"><span class="yiyi-st" id="yiyi-80">熊猫</span></th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-81"><code class="docutils literal"><span class="pre">dim(df)</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-82"><code class="docutils literal"><span class="pre">df.shape</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-83"><code class="docutils literal"><span class="pre">head(df)</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-84"><code class="docutils literal"><span class="pre">df.head()</span></code></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-85"><code class="docutils literal"><span class="pre">slice（df，</span> <span class="pre">1:10）</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-86"><code class="docutils literal"><span class="pre">df.iloc[:9]</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-87"><code class="docutils literal"><span class="pre">过滤器（df，</span> <span class="pre">col1</span> <span class="pre">==</span> <span class="pre">1，</span> <span class="pre">col2</span> <span class="pre"> ==</span> <span class="pre">1）</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-88"><code class="docutils literal"><span class="pre">df.query('col1</span> <span class="pre">==</span> <span class="pre">1</span> <span class="pre">&</span> <span class="pre">col2</span> <span class="pre">==</span> <span class="pre">1')</span></code></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-89"><code class="docutils literal"><span class="pre">df [df $ col1</span> <span class="pre">==</span> <span class="pre">1</span> <span class="pre">＆amp；</span> <span class="pre">df $ col2 t5 > <span class="pre">==</span> <span class="pre">1，]</span></span></code></span></td>
<td><span class="yiyi-st" id="yiyi-90"><code class="docutils literal"><span class="pre">df [（df.col1</span> <span class="pre">==</span> <span class="pre">1）</span> <span class="pre">＆amp；</span> <span class="pre">（df.col2 </span> <span class="pre">==</span> <span class="pre">1）]</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-91"><code class="docutils literal"><span class="pre">select（df，</span> <span class="pre">col1，</span> <span class="pre">col2）</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-92"><code class="docutils literal"><span class="pre">df [['col1'，</span> <span class="pre">'col2']]</span></code></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-93"><code class="docutils literal"><span class="pre">select（df，</span> <span class="pre">col1：col3）</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-94"><code class="docutils literal"><span class="pre">df.loc [：，</span> <span class="pre">'col1'：'col3']</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-95"><code class="docutils literal"><span class="pre">select（df，</span> <span class="pre"> - （col1：col3））</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-96"><code class="docutils literal"><span class="pre">df.drop（cols_to_drop，</span> <span class="pre">axis = 1）</span></code>但参见<a class="footnote-reference" href="#select-range" id="id1">[1] </a></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-97"><code class="docutils literal"><span class="pre">distinct（select（df，</span> <span class="pre">col1））</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-98"><code class="docutils literal"><span class="pre">df[['col1']].drop_duplicates()</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-99"><code class="docutils literal"><span class="pre">distinct（select（df，</span> <span class="pre">col1，</span> <span class="pre">col2））</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-100"><code class="docutils literal"><span class="pre">df [['col1'，</span> <span class="pre">'col2']] drop_duplicates()</span> </code></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-101"><code class="docutils literal"><span class="pre">sample_n（df，</span> <span class="pre">10）</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-102"><code class="docutils literal"><span class="pre">df.sample(n=10)</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-103"><code class="docutils literal"><span class="pre">sample_frac（df，</span> <span class="pre">0.01）</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-104"><code class="docutils literal"><span class="pre">df.sample(frac=0.01)</span></code></span></td>
</tr>
</tbody>
</table>
<table class="docutils footnote" frame="void" id="select-range" rules="none">
<colgroup><col class="label"><col></colgroup>
<tbody valign="top">
<tr><td class="label"><span class="yiyi-st" id="yiyi-105"><a class="fn-backref" href="#id1">[1]</a></span></td><td><span class="yiyi-st" id="yiyi-106">R的列的子范围（<code class="docutils literal"><span class="pre">select（df，</span> <span class="pre">col1：col3）</span></code>）的简写可以在pandas干净地接近，如果你有列表的列，例如<code class="docutils literal"><span class="pre">df[cols[1:3]]</span></code>或<code class="docutils literal"><span class="pre">df.drop(cols[1:3])</span></code>乱。</span></td></tr>
</tbody>
</table>
</div>
<div class="section" id="sorting">
<h3><span class="yiyi-st" id="yiyi-107">Sorting</span></h3>
<table border="1" class="docutils">
<colgroup>
<col width="50%">
<col width="50%">
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"><span class="yiyi-st" id="yiyi-108">R</span></th>
<th class="head"><span class="yiyi-st" id="yiyi-109">熊猫</span></th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-110"><code class="docutils literal"><span class="pre">arrange（df，</span> <span class="pre">col1，</span> <span class="pre">col2）</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-111"><code class="docutils literal"><span class="pre">df.sort_values（['col1'，</span> <span class="pre">'col2']）</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-112"><code class="docutils literal"><span class="pre">arrange（df，</span> <span class="pre">desc（col1））</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-113"><code class="docutils literal"><span class="pre">df.sort_values（'col1'，</span> <span class="pre">ascending = False）</span></code></span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="transforming">
<h3><span class="yiyi-st" id="yiyi-114">Transforming</span></h3>
<table border="1" class="docutils">
<colgroup>
<col width="45%">
<col width="55%">
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"><span class="yiyi-st" id="yiyi-115">R</span></th>
<th class="head"><span class="yiyi-st" id="yiyi-116">熊猫</span></th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-117"><code class="docutils literal"><span class="pre">select（df，</span> <span class="pre">col_one</span> <span class="pre">=</span> <span class="pre">col1）</span> </code></span></td>
<td><span class="yiyi-st" id="yiyi-118"><code class="docutils literal"><span class="pre">df.rename（columns = {'col1'：</span> <span class="pre">'col_one'}）['col_one']</span> </code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-119"><code class="docutils literal"><span class="pre">rename（df，</span> <span class="pre">col_one</span> <span class="pre">=</span> <span class="pre">col1）</span> </code></span></td>
<td><span class="yiyi-st" id="yiyi-120"><code class="docutils literal"><span class="pre">df.rename（columns = {'col1'：</span> <span class="pre">'col_one'}）</span></code></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-121"><code class="docutils literal"><span class="pre">mutate（df，</span> <span class="pre">c = a-b）</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-122"><code class="docutils literal"><span class="pre">df.assign(c=df.a-df.b)</span></code></span></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="grouping-and-summarizing">
<h3><span class="yiyi-st" id="yiyi-123">Grouping and Summarizing</span></h3>
<table border="1" class="docutils">
<colgroup>
<col width="51%">
<col width="49%">
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"><span class="yiyi-st" id="yiyi-124">R</span></th>
<th class="head"><span class="yiyi-st" id="yiyi-125">熊猫</span></th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-126"><code class="docutils literal"><span class="pre">summary(df)</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-127"><code class="docutils literal"><span class="pre">df.describe()</span></code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-128"><code class="docutils literal"><span class="pre">gdf </span> <span class="pre"> <span class="pre">group_by（df，</span> <span class="pre">col1）</span> </span></code></span></td>
<td><span class="yiyi-st" id="yiyi-129"><code class="docutils literal"><span class="pre">gdf </span> <span class="pre">=</span> <span class="pre">df.groupby（'col1'）</span></code></span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-130"><code class="docutils literal"><span class="pre">summarize（gdf，</span> <span class="pre">avg = mean（col1，</span> <span class="pre">na.rm = TRUE））</span> </code></span></td>
<td><span class="yiyi-st" id="yiyi-131"><code class="docutils literal"><span class="pre">df.groupby（'col1'）。agg（{'col1'：</span> <span class="pre">'mean'}）</span> </code></span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-132"><code class="docutils literal"><span class="pre">summarize（gdf，</span> <span class="pre">total = sum（col1））</span></code></span></td>
<td><span class="yiyi-st" id="yiyi-133"><code class="docutils literal"><span class="pre">df.groupby('col1').sum()</span></code></span></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="base-r">
<h2><span class="yiyi-st" id="yiyi-134">Base R</span></h2>
<div class="section" id="slicing-with-r-s-c">
<h3><span class="yiyi-st" id="yiyi-135">Slicing with R’s <a class="reference external" href="http://stat.ethz.ch/R-manual/R-patched/library/base/html/c.html"><code class="docutils literal"><span class="pre">c</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-136">R可以方便地按名称访问<code class="docutils literal"><span class="pre">data.frame</span></code>列</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>df <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span>a<span class="o">=</span>rnorm<span class="p">(</span><span class="m">5</span><span class="p">),</span> b<span class="o">=</span>rnorm<span class="p">(</span><span class="m">5</span><span class="p">),</span> <span class="kt">c</span><span class="o">=</span>rnorm<span class="p">(</span><span class="m">5</span><span class="p">),</span> d<span class="o">=</span>rnorm<span class="p">(</span><span class="m">5</span><span class="p">),</span> e<span class="o">=</span>rnorm<span class="p">(</span><span class="m">5</span><span class="p">))</span>
df<span class="p">[,</span> <span class="kt">c</span><span class="p">(</span><span class="s">"a"</span><span class="p">,</span> <span class="s">"c"</span><span class="p">,</span> <span class="s">"e"</span><span class="p">)]</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-137">或通过整数位置</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>df <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span><span class="kt">matrix</span><span class="p">(</span>rnorm<span class="p">(</span><span class="m">1000</span><span class="p">),</span> ncol<span class="o">=</span><span class="m">100</span><span class="p">))</span>
df<span class="p">[,</span> <span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">10</span><span class="p">,</span> <span class="m">25</span><span class="o">:</span><span class="m">30</span><span class="p">,</span> <span class="m">40</span><span class="p">,</span> <span class="m">50</span><span class="o">:</span><span class="m">100</span><span class="p">)]</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-138">在<code class="docutils literal"><span class="pre">pandas</span></code>中按名称选择多个列很简单</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [1]: </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">10</span><span class="p">,</span> <span class="mi">3</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">'abc'</span><span class="p">))</span>
<span class="gp">In [2]: </span><span class="n">df</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[2]: </span>
<span class="go"> a c</span>
<span class="go">0 -1.039575 -0.424972</span>
<span class="go">1 0.567020 -1.087401</span>
<span class="go">2 -0.673690 -1.478427</span>
<span class="go">3 0.524988 0.577046</span>
<span class="go">4 -1.715002 -0.370647</span>
<span class="go">5 -1.157892 0.844885</span>
<span class="go">6 1.075770 1.643563</span>
<span class="go">7 -1.469388 -0.674600</span>
<span class="go">8 -1.776904 -1.294524</span>
<span class="go">9 0.413738 -0.472035</span>
<span class="gp">In [3]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</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[3]: </span>
<span class="go"> a c</span>
<span class="go">0 -1.039575 -0.424972</span>
<span class="go">1 0.567020 -1.087401</span>
<span class="go">2 -0.673690 -1.478427</span>
<span class="go">3 0.524988 0.577046</span>
<span class="go">4 -1.715002 -0.370647</span>
<span class="go">5 -1.157892 0.844885</span>
<span class="go">6 1.075770 1.643563</span>
<span class="go">7 -1.469388 -0.674600</span>
<span class="go">8 -1.776904 -1.294524</span>
<span class="go">9 0.413738 -0.472035</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-139">通过整数位置选择多个非连续列可以通过<code class="docutils literal"><span class="pre">iloc</span></code>索引器属性和<code class="docutils literal"><span class="pre">numpy.r_</span></code>的组合来实现。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">named</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="s1">'abcdefg'</span><span class="p">)</span>
<span class="gp">In [5]: </span><span class="n">n</span> <span class="o">=</span> <span class="mi">30</span>
<span class="gp">In [6]: </span><span class="n">columns</span> <span class="o">=</span> <span class="n">named</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">named</span><span class="p">),</span> <span class="n">n</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="gp">In [7]: </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="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="gp">In [8]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[:</span><span class="mi">10</span><span class="p">,</span> <span class="mi">24</span><span class="p">:</span><span class="mi">30</span><span class="p">]]</span>
<span class="gr">Out[8]: </span>
<span class="go"> a b c d e f g \</span>
<span class="go">0 -0.013960 -0.362543 -0.006154 -0.923061 0.895717 0.805244 -1.206412 </span>
<span class="go">1 0.545952 -1.219217 -1.226825 0.769804 -1.281247 -0.727707 -0.121306 </span>
<span class="go">2 2.396780 0.014871 3.357427 -0.317441 -1.236269 0.896171 -0.487602 </span>
<span class="go">3 -0.988387 0.094055 1.262731 1.289997 0.082423 -0.055758 0.536580 </span>
<span class="go">4 -1.340896 1.846883 -1.328865 1.682706 -1.717693 0.888782 0.228440 </span>
<span class="go">5 0.464000 0.227371 -0.496922 0.306389 -2.290613 -1.134623 -1.561819 </span>
<span class="go">6 -0.507516 -0.230096 0.394500 -1.934370 -1.652499 1.488753 -0.896484 </span>
<span class="go">.. ... ... ... ... ... ... ... </span>
<span class="go">23 -0.083272 -0.273955 -0.772369 -1.242807 -0.386336 -0.182486 0.164816 </span>
<span class="go">24 2.071413 -1.364763 1.122066 0.066847 1.751987 0.419071 -1.118283 </span>
<span class="go">25 0.036609 0.359986 1.211905 0.850427 1.554957 -0.888463 -1.508808 </span>
<span class="go">26 -1.179240 0.238923 1.756671 -0.747571 0.543625 -0.159609 -0.051458 </span>
<span class="go">27 0.025645 0.932436 -1.694531 -0.182236 -1.072710 0.466764 -0.072673 </span>
<span class="go">28 0.439086 0.812684 -0.128932 -0.142506 -1.137207 0.462001 -0.159466 </span>
<span class="go">29 -0.909806 -0.312006 0.383630 -0.631606 1.321415 -0.004799 -2.008210 </span>
<span class="go"> 7 8 9 24 25 26 27 \</span>
<span class="go">0 2.565646 1.431256 1.340309 0.875906 -2.211372 0.974466 -2.006747 </span>
<span class="go">1 -0.097883 0.695775 0.341734 -1.743161 -0.826591 -0.345352 1.314232 </span>
<span class="go">2 -0.082240 -2.182937 0.380396 1.266143 0.299368 -0.863838 0.408204 </span>
<span class="go">3 -0.489682 0.369374 -0.034571 0.221471 -0.744471 0.758527 1.729689 </span>
<span class="go">4 0.901805 1.171216 0.520260 0.650776 -1.461665 -1.137707 -0.891060 </span>
<span class="go">5 -0.260838 0.281957 1.523962 -0.008434 1.952541 -1.056652 0.533946 </span>
<span class="go">6 0.576897 1.146000 1.487349 2.015523 -1.833722 1.771740 -0.670027 </span>
<span class="go">.. ... ... ... ... ... ... ... </span>
<span class="go">23 0.065624 0.307665 -1.898358 1.389045 -0.873585 -0.699862 0.812477 </span>
<span class="go">24 1.010694 0.877138 -0.611561 -1.040389 -0.796211 0.241596 0.385922 </span>
<span class="go">25 -0.617855 0.536164 2.175585 1.872601 -2.513465 -0.139184 0.810491 </span>
<span class="go">26 0.937882 0.617547 0.287918 -1.584814 0.307941 1.809049 0.296237 </span>
<span class="go">27 -0.026233 -0.051744 0.001402 0.150664 -3.060395 0.040268 0.066091 </span>
<span class="go">28 -1.788308 0.753604 0.918071 0.922729 0.869610 0.364726 -0.226101 </span>
<span class="go">29 -0.481634 -2.056211 -2.106095 0.039227 0.211283 1.440190 -0.989193 </span>
<span class="go"> 28 29 </span>
<span class="go">0 -0.410001 -0.078638 </span>
<span class="go">1 0.690579 0.995761 </span>
<span class="go">2 -1.048089 -0.025747 </span>
<span class="go">3 -0.964980 -0.845696 </span>
<span class="go">4 -0.693921 1.613616 </span>
<span class="go">5 -1.226970 0.040403 </span>
<span class="go">6 0.049307 -0.521493 </span>
<span class="go">.. ... ... </span>
<span class="go">23 -0.469503 1.142702 </span>
<span class="go">24 -0.486078 0.433042 </span>
<span class="go">25 0.571599 -0.000676 </span>
<span class="go">26 -0.143550 0.289401 </span>
<span class="go">27 -0.192862 1.979055 </span>
<span class="go">28 -0.657647 -0.952699 </span>
<span class="go">29 0.313335 -0.399709 </span>
<span class="go">[30 rows x 16 columns]</span>
</pre></div>
</div>
</div>
<div class="section" id="aggregate">
<h3><span class="yiyi-st" id="yiyi-140"><a class="reference external" href="http://finzi.psych.upenn.edu/R/library/stats/html/aggregate.html"><code class="docutils literal"><span class="pre">aggregate</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-141">在R中，您可能需要将数据拆分为子集并计算每个子集的平均值。</span><span class="yiyi-st" id="yiyi-142">使用名为<code class="docutils literal"><span class="pre">df</span></code>的数据框，并将其拆分为<code class="docutils literal"><span class="pre">by1</span></code>和<code class="docutils literal"><span class="pre">by2</span></code>组：</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>df <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span>
v1 <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">3</span><span class="p">,</span><span class="m">5</span><span class="p">,</span><span class="m">7</span><span class="p">,</span><span class="m">8</span><span class="p">,</span><span class="m">3</span><span class="p">,</span><span class="m">5</span><span class="p">,</span><span class="kc">NA</span><span class="p">,</span><span class="m">4</span><span class="p">,</span><span class="m">5</span><span class="p">,</span><span class="m">7</span><span class="p">,</span><span class="m">9</span><span class="p">),</span>
v2 <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="m">11</span><span class="p">,</span><span class="m">33</span><span class="p">,</span><span class="m">55</span><span class="p">,</span><span class="m">77</span><span class="p">,</span><span class="m">88</span><span class="p">,</span><span class="m">33</span><span class="p">,</span><span class="m">55</span><span class="p">,</span><span class="kc">NA</span><span class="p">,</span><span class="m">44</span><span class="p">,</span><span class="m">55</span><span class="p">,</span><span class="m">77</span><span class="p">,</span><span class="m">99</span><span class="p">),</span>
by1 <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">"red"</span><span class="p">,</span> <span class="s">"blue"</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="m">2</span><span class="p">,</span> <span class="kc">NA</span><span class="p">,</span> <span class="s">"big"</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="m">2</span><span class="p">,</span> <span class="s">"red"</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="kc">NA</span><span class="p">,</span> <span class="m">12</span><span class="p">),</span>
by2 <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">"wet"</span><span class="p">,</span> <span class="s">"dry"</span><span class="p">,</span> <span class="m">99</span><span class="p">,</span> <span class="m">95</span><span class="p">,</span> <span class="kc">NA</span><span class="p">,</span> <span class="s">"damp"</span><span class="p">,</span> <span class="m">95</span><span class="p">,</span> <span class="m">99</span><span class="p">,</span> <span class="s">"red"</span><span class="p">,</span> <span class="m">99</span><span class="p">,</span> <span class="kc">NA</span><span class="p">,</span> <span class="kc">NA</span><span class="p">))</span>
aggregate<span class="p">(</span>x<span class="o">=</span>df<span class="p">[,</span> <span class="kt">c</span><span class="p">(</span><span class="s">"v1"</span><span class="p">,</span> <span class="s">"v2"</span><span class="p">)],</span> by<span class="o">=</span><span class="kt">list</span><span class="p">(</span>mydf2<span class="o">$</span>by1<span class="p">,</span> mydf2<span class="o">$</span>by2<span class="p">),</span> FUN <span class="o">=</span> <span class="kp">mean</span><span class="p">)</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-143"><a class="reference internal" href="generated/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby" title="pandas.DataFrame.groupby"><code class="xref py py-meth docutils literal"><span class="pre">groupby()</span></code></a>方法类似于基本R <code class="docutils literal"><span class="pre">aggregate</span></code>函数。</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="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="gp"> ...:</span> <span class="s1">'v1'</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="mi">5</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</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="n">np</span><span class="o">.</span><span class="n">nan</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">7</span><span class="p">,</span><span class="mi">9</span><span class="p">],</span>
<span class="gp"> ...:</span> <span class="s1">'v2'</span><span class="p">:</span> <span class="p">[</span><span class="mi">11</span><span class="p">,</span><span class="mi">33</span><span class="p">,</span><span class="mi">55</span><span class="p">,</span><span class="mi">77</span><span class="p">,</span><span class="mi">88</span><span class="p">,</span><span class="mi">33</span><span class="p">,</span><span class="mi">55</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="mi">44</span><span class="p">,</span><span class="mi">55</span><span class="p">,</span><span class="mi">77</span><span class="p">,</span><span class="mi">99</span><span class="p">],</span>
<span class="gp"> ...:</span> <span class="s1">'by1'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"red"</span><span class="p">,</span> <span class="s2">"blue"</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="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s2">"big"</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">"red"</span><span class="p">,</span> <span class="mi">1</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="mi">12</span><span class="p">],</span>
<span class="gp"> ...:</span> <span class="s1">'by2'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"wet"</span><span class="p">,</span> <span class="s2">"dry"</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="mi">95</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="s2">"damp"</span><span class="p">,</span> <span class="mi">95</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="s2">"red"</span><span class="p">,</span> <span class="mi">99</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"> ...:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span>
<span class="gp"> ...:</span> <span class="p">})</span>
<span class="gp"> ...:</span>
<span class="gp">In [10]: </span><span class="n">g</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'by1'</span><span class="p">,</span><span class="s1">'by2'</span><span class="p">])</span>
<span class="gp">In [11]: </span><span class="n">g</span><span class="p">[[</span><span class="s1">'v1'</span><span class="p">,</span><span class="s1">'v2'</span><span class="p">]]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="gr">Out[11]: </span>
<span class="go"> v1 v2</span>
<span class="go">by1 by2 </span>
<span class="go">1 95 5.0 55.0</span>
<span class="go"> 99 5.0 55.0</span>
<span class="go">2 95 7.0 77.0</span>
<span class="go"> 99 NaN NaN</span>
<span class="go">big damp 3.0 33.0</span>
<span class="go">blue dry 3.0 33.0</span>
<span class="go">red red 4.0 44.0</span>
<span class="go"> wet 1.0 11.0</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-144">有关更多详细信息和示例，请参阅<a class="reference internal" href="groupby.html#groupby-split"><span class="std std-ref">the groupby documentation</span></a>。</span></p>
</div>
<div class="section" id="match">
<h3><span class="yiyi-st" id="yiyi-145"><a class="reference external" href="http://finzi.psych.upenn.edu/R/library/base/html/match.html"><code class="docutils literal"><span class="pre">match</span></code> / <code class="docutils literal"><span class="pre">%in%</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-146">在R中选择数据的常用方法是使用<code class="docutils literal"><span class="pre">%in%</span></code>中，其使用<code class="docutils literal"><span class="pre">match</span></code>函数定义。</span><span class="yiyi-st" id="yiyi-147">在％中的运算符<code class="docutils literal"><span class="pre">%in%</span></code></span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>s <span class="o"><-</span> <span class="m">0</span><span class="o">:</span><span class="m">4</span>
s <span class="o">%in%</span> <span class="kt">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">4</span><span class="p">)</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-148"><a class="reference internal" href="generated/pandas.DataFrame.isin.html#pandas.DataFrame.isin" title="pandas.DataFrame.isin"><code class="xref py py-meth docutils literal"><span class="pre">isin()</span></code></a>方法类似于R <code class="docutils literal"><span class="pre">%in%</span></code>运算符：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [12]: </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">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">In [13]: </span><span class="n">s</span><span class="o">.</span><span class="n">isin</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[13]: </span>
<span class="go">0 False</span>
<span class="go">1 False</span>
<span class="go">2 True</span>
<span class="go">3 False</span>
<span class="go">4 True</span>
<span class="go">dtype: bool</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-149"><code class="docutils literal"><span class="pre">match</span></code>函数返回其第二个参数的第一个参数的匹配位置的向量：</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>s <span class="o"><-</span> <span class="m">0</span><span class="o">:</span><span class="m">4</span>
<span class="kp">match</span><span class="p">(</span>s<span class="p">,</span> <span class="kt">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">4</span><span class="p">))</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-150"><a class="reference internal" href="generated/pandas.core.groupby.GroupBy.apply.html#pandas.core.groupby.GroupBy.apply" title="pandas.core.groupby.GroupBy.apply"><code class="xref py py-meth docutils literal"><span class="pre">apply()</span></code></a>方法可用于复制此：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [14]: </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">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">In [15]: </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">match</span><span class="p">(</span><span class="n">s</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="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">))</span>
<span class="gr">Out[15]: </span>
<span class="go">0 NaN</span>
<span class="go">1 NaN</span>
<span class="go">2 0.0</span>
<span class="go">3 NaN</span>
<span class="go">4 1.0</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-151">有关详细信息和示例，请参阅<a class="reference internal" href="indexing.html#indexing-basics-indexing-isin"><span class="std std-ref">the reshaping documentation</span></a>。</span></p>
</div>
<div class="section" id="tapply">
<h3><span class="yiyi-st" id="yiyi-152"><a class="reference external" href="http://finzi.psych.upenn.edu/R/library/base/html/tapply.html"><code class="docutils literal"><span class="pre">tapply</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-153"><code class="docutils literal"><span class="pre">tapply</span></code>类似于<code class="docutils literal"><span class="pre">aggregate</span></code>，但数据可能位于粗糙的数组中，因为子类大小可能不规则。</span><span class="yiyi-st" id="yiyi-154">使用名为<code class="docutils literal"><span class="pre">baseball</span></code>的数据框架，并基于数组<code class="docutils literal"><span class="pre">team</span></code>检索信息：</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>baseball <span class="o"><-</span>
<span class="kt">data.frame</span><span class="p">(</span>team <span class="o">=</span> <span class="kp">gl</span><span class="p">(</span><span class="m">5</span><span class="p">,</span> <span class="m">5</span><span class="p">,</span>
labels <span class="o">=</span> <span class="kp">paste</span><span class="p">(</span><span class="s">"Team"</span><span class="p">,</span> <span class="kc">LETTERS</span><span class="p">[</span><span class="m">1</span><span class="o">:</span><span class="m">5</span><span class="p">])),</span>
player <span class="o">=</span> <span class="kp">sample</span><span class="p">(</span><span class="kc">letters</span><span class="p">,</span> <span class="m">25</span><span class="p">),</span>
batting.average <span class="o">=</span> runif<span class="p">(</span><span class="m">25</span><span class="p">,</span> <span class="m">.200</span><span class="p">,</span> <span class="m">.400</span><span class="p">))</span>
<span class="kp">tapply</span><span class="p">(</span>baseball<span class="o">$</span>batting.average<span class="p">,</span> baseball.example<span class="o">$</span>team<span class="p">,</span>
<span class="kp">max</span><span class="p">)</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-155">在<code class="docutils literal"><span class="pre">pandas</span></code>中，我们可以使用<a class="reference internal" href="generated/pandas.pivot_table.html#pandas.pivot_table" title="pandas.pivot_table"><code class="xref py py-meth docutils literal"><span class="pre">pivot_table()</span></code></a>方法来处理：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="kn">import</span> <span class="nn">random</span>
<span class="gp">In [17]: </span><span class="kn">import</span> <span class="nn">string</span>
<span class="gp">In [18]: </span><span class="n">baseball</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="gp"> ....:</span> <span class="s1">'team'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"team </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">x</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)]</span><span class="o">*</span><span class="mi">5</span><span class="p">,</span>
<span class="gp"> ....:</span> <span class="s1">'player'</span><span class="p">:</span> <span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">string</span><span class="o">.</span><span class="n">ascii_lowercase</span><span class="p">),</span><span class="mi">25</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="s1">'batting avg'</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">uniform</span><span class="p">(</span><span class="o">.</span><span class="mi">200</span><span class="p">,</span> <span class="o">.</span><span class="mi">400</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
<span class="gp"> ....:</span> <span class="p">})</span>
<span class="gp"> ....:</span>
<span class="gp">In [19]: </span><span class="n">baseball</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="s1">'batting avg'</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s1">'team'</span><span class="p">,</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
<span class="gr">Out[19]: </span>
<span class="go">team</span>
<span class="go">team 1 0.394457</span>
<span class="go">team 2 0.395730</span>
<span class="go">team 3 0.343015</span>
<span class="go">team 4 0.388863</span>
<span class="go">team 5 0.377379</span>
<span class="go">Name: batting avg, dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-156">有关详细信息和示例，请参阅<a class="reference internal" href="reshaping.html#reshaping-pivot"><span class="std std-ref">the reshaping documentation</span></a>。</span></p>
</div>
<div class="section" id="subset">
<h3><span class="yiyi-st" id="yiyi-157"><a class="reference external" href="http://finzi.psych.upenn.edu/R/library/base/html/subset.html"><code class="docutils literal"><span class="pre">subset</span></code></a></span></h3>
<div class="versionadded">
<p><span class="yiyi-st" id="yiyi-158"><span class="versionmodified">版本0.13中的新功能。</span></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-159"><a class="reference internal" href="generated/pandas.DataFrame.query.html#pandas.DataFrame.query" title="pandas.DataFrame.query"><code class="xref py py-meth docutils literal"><span class="pre">query()</span></code></a>方法类似于基本R <code class="docutils literal"><span class="pre">subset</span></code>函数。</span><span class="yiyi-st" id="yiyi-160">在R中，您可能想要获取<code class="docutils literal"><span class="pre">data.frame</span></code>的行，其中一列的值小于另一列的值：</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>df <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span>a<span class="o">=</span>rnorm<span class="p">(</span><span class="m">10</span><span class="p">),</span> b<span class="o">=</span>rnorm<span class="p">(</span><span class="m">10</span><span class="p">))</span>
<span class="kp">subset</span><span class="p">(</span>df<span class="p">,</span> a <span class="o"><=</span> b<span class="p">)</span>
df<span class="p">[</span>df<span class="o">$</span>a <span class="o"><=</span> df<span class="o">$</span>b<span class="p">,]</span> <span class="c1"># note the comma</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-161">在<code class="docutils literal"><span class="pre">pandas</span></code>中，有几种方法可以执行子集。</span><span class="yiyi-st" id="yiyi-162">您可以使用<a class="reference internal" href="generated/pandas.DataFrame.query.html#pandas.DataFrame.query" title="pandas.DataFrame.query"><code class="xref py py-meth docutils literal"><span class="pre">query()</span></code></a>或传递表达式，就像它是索引/切片以及标准布尔索引：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [20]: </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">'a'</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">10</span><span class="p">),</span> <span class="s1">'b'</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">10</span><span class="p">)})</span>
<span class="gp">In [21]: </span><span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">'a <= b'</span><span class="p">)</span>
<span class="gr">Out[21]: </span>
<span class="go"> a b</span>
<span class="go">0 -1.003455 -0.990738</span>
<span class="go">1 0.083515 0.548796</span>
<span class="go">3 -0.524392 0.904400</span>
<span class="go">4 -0.837804 0.746374</span>
<span class="go">8 -0.507219 0.245479</span>
<span class="gp">In [22]: </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="n">df</span><span class="o">.</span><span class="n">b</span><span class="p">]</span>
<span class="gr">Out[22]: </span>
<span class="go"> a b</span>
<span class="go">0 -1.003455 -0.990738</span>
<span class="go">1 0.083515 0.548796</span>
<span class="go">3 -0.524392 0.904400</span>
<span class="go">4 -0.837804 0.746374</span>
<span class="go">8 -0.507219 0.245479</span>
<span class="gp">In [23]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</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="n">df</span><span class="o">.</span><span class="n">b</span><span class="p">]</span>
<span class="gr">Out[23]: </span>
<span class="go"> a b</span>
<span class="go">0 -1.003455 -0.990738</span>
<span class="go">1 0.083515 0.548796</span>
<span class="go">3 -0.524392 0.904400</span>
<span class="go">4 -0.837804 0.746374</span>
<span class="go">8 -0.507219 0.245479</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-163">有关详细信息和示例，请参见<a class="reference internal" href="indexing.html#indexing-query"><span class="std std-ref">the query documentation</span></a>。</span></p>
</div>
<div class="section" id="with">
<h3><span class="yiyi-st" id="yiyi-164"><a class="reference external" href="http://finzi.psych.upenn.edu/R/library/base/html/with.html"><code class="docutils literal"><span class="pre">with</span></code></a></span></h3>
<div class="versionadded">
<p><span class="yiyi-st" id="yiyi-165"><span class="versionmodified">版本0.13中的新功能。</span></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-166">An expression using a data.frame called <code class="docutils literal"><span class="pre">df</span></code> in R with the columns <code class="docutils literal"><span class="pre">a</span></code> and <code class="docutils literal"><span class="pre">b</span></code> would be evaluated using <code class="docutils literal"><span class="pre">with</span></code> like so:</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>df <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span>a<span class="o">=</span>rnorm<span class="p">(</span><span class="m">10</span><span class="p">),</span> b<span class="o">=</span>rnorm<span class="p">(</span><span class="m">10</span><span class="p">))</span>
<span class="kp">with</span><span class="p">(</span>df<span class="p">,</span> a <span class="o">+</span> b<span class="p">)</span>
df<span class="o">$</span>a <span class="o">+</span> df<span class="o">$</span>b <span class="c1"># same as the previous expression</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-167">在<code class="docutils literal"><span class="pre">pandas</span></code>中，使用<a class="reference internal" href="generated/pandas.DataFrame.eval.html#pandas.DataFrame.eval" title="pandas.DataFrame.eval"><code class="xref py py-meth docutils literal"><span class="pre">eval()</span></code></a>方法的等效表达式为：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [24]: </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">'a'</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">10</span><span class="p">),</span> <span class="s1">'b'</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">10</span><span class="p">)})</span>
<span class="gp">In [25]: </span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'a + b'</span><span class="p">)</span>
<span class="gr">Out[25]: </span>
<span class="go">0 -0.920205</span>
<span class="go">1 -0.860236</span>
<span class="go">2 1.154370</span>
<span class="go">3 0.188140</span>
<span class="go">4 -1.163718</span>
<span class="go">5 0.001397</span>
<span class="go">6 -0.825694</span>
<span class="go">7 -1.138198</span>
<span class="go">8 -1.708034</span>
<span class="go">9 1.148616</span>
<span class="go">dtype: float64</span>
<span class="gp">In [26]: </span><span class="n">df</span><span class="o">.</span><span class="n">a</span> <span class="o">+</span> <span class="n">df</span><span class="o">.</span><span class="n">b</span> <span class="c1"># same as the previous expression</span>
<span class="gr">Out[26]: </span>
<span class="go">0 -0.920205</span>
<span class="go">1 -0.860236</span>
<span class="go">2 1.154370</span>
<span class="go">3 0.188140</span>
<span class="go">4 -1.163718</span>
<span class="go">5 0.001397</span>
<span class="go">6 -0.825694</span>
<span class="go">7 -1.138198</span>
<span class="go">8 -1.708034</span>
<span class="go">9 1.148616</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-168">在某些情况下，<a class="reference internal" href="generated/pandas.DataFrame.eval.html#pandas.DataFrame.eval" title="pandas.DataFrame.eval"><code class="xref py py-meth docutils literal"><span class="pre">eval()</span></code></a>将比纯Python中的求值快得多。</span><span class="yiyi-st" id="yiyi-169">有关详细信息和示例，请参见<a class="reference internal" href="enhancingperf.html#enhancingperf-eval"><span class="std std-ref">the eval documentation</span></a>。</span></p>
</div>
</div>
<div class="section" id="plyr">
<h2><span class="yiyi-st" id="yiyi-170">plyr</span></h2>
<p><span class="yiyi-st" id="yiyi-171"><code class="docutils literal"><span class="pre">plyr</span></code>是用于数据分析的拆分应用组合策略的R库。</span><span class="yiyi-st" id="yiyi-172">The functions revolve around three data structures in R, <code class="docutils literal"><span class="pre">a</span></code> for <code class="docutils literal"><span class="pre">arrays</span></code>, <code class="docutils literal"><span class="pre">l</span></code> for <code class="docutils literal"><span class="pre">lists</span></code>, and <code class="docutils literal"><span class="pre">d</span></code> for <code class="docutils literal"><span class="pre">data.frame</span></code>. </span><span class="yiyi-st" id="yiyi-173">下表显示了如何在Python中映射这些数据结构。</span></p>
<table border="1" class="docutils">
<colgroup>
<col width="28%">
<col width="72%">
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"><span class="yiyi-st" id="yiyi-174">R</span></th>
<th class="head"><span class="yiyi-st" id="yiyi-175">蟒蛇</span></th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-176">数组</span></td>
<td><span class="yiyi-st" id="yiyi-177">列表</span></td>
</tr>
<tr class="row-odd"><td><span class="yiyi-st" id="yiyi-178">列表</span></td>
<td><span class="yiyi-st" id="yiyi-179">字典或对象列表</span></td>
</tr>
<tr class="row-even"><td><span class="yiyi-st" id="yiyi-180">data.frame</span></td>
<td><span class="yiyi-st" id="yiyi-181">数据帧</span></td>
</tr>
</tbody>
</table>
<div class="section" id="ddply">
<h3><span class="yiyi-st" id="yiyi-182"><a class="reference external" href="http://www.inside-r.org/packages/cran/plyr/docs/ddply"><code class="docutils literal"><span class="pre">ddply</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-183">在R中要使用<code class="docutils literal"><span class="pre">month</span></code>汇总<code class="docutils literal"><span class="pre">x</span></code>的数据框架 span>：<code class="docutils literal"><span class="pre">df</span></code></span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kn">require</span><span class="p">(</span>plyr<span class="p">)</span>
df <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span>
x <span class="o">=</span> runif<span class="p">(</span><span class="m">120</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="m">168</span><span class="p">),</span>
y <span class="o">=</span> runif<span class="p">(</span><span class="m">120</span><span class="p">,</span> <span class="m">7</span><span class="p">,</span> <span class="m">334</span><span class="p">),</span>
z <span class="o">=</span> runif<span class="p">(</span><span class="m">120</span><span class="p">,</span> <span class="m">1.7</span><span class="p">,</span> <span class="m">20.7</span><span class="p">),</span>
month <span class="o">=</span> <span class="kp">rep</span><span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="m">5</span><span class="p">,</span><span class="m">6</span><span class="p">,</span><span class="m">7</span><span class="p">,</span><span class="m">8</span><span class="p">),</span><span class="m">30</span><span class="p">),</span>
week <span class="o">=</span> <span class="kp">sample</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">4</span><span class="p">,</span> <span class="m">120</span><span class="p">,</span> <span class="kc">TRUE</span><span class="p">)</span>
<span class="p">)</span>
ddply<span class="p">(</span>df<span class="p">,</span> <span class="m">.</span><span class="p">(</span>month<span class="p">,</span> week<span class="p">),</span> summarize<span class="p">,</span>
mean <span class="o">=</span> <span class="kp">round</span><span class="p">(</span><span class="kp">mean</span><span class="p">(</span>x<span class="p">),</span> <span class="m">2</span><span class="p">),</span>
sd <span class="o">=</span> <span class="kp">round</span><span class="p">(</span>sd<span class="p">(</span>x<span class="p">),</span> <span class="m">2</span><span class="p">))</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-184">在<code class="docutils literal"><span class="pre">pandas</span></code>中，使用<a class="reference internal" href="generated/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby" title="pandas.DataFrame.groupby"><code class="xref py py-meth docutils literal"><span class="pre">groupby()</span></code></a>方法的等效表达式将是：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [27]: </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="gp"> ....:</span> <span class="s1">'x'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">168.</span><span class="p">,</span> <span class="mi">120</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="s1">'y'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">7.</span><span class="p">,</span> <span class="mf">334.</span><span class="p">,</span> <span class="mi">120</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="s1">'z'</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">uniform</span><span class="p">(</span><span class="mf">1.7</span><span class="p">,</span> <span class="mf">20.7</span><span class="p">,</span> <span class="mi">120</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="s1">'month'</span><span class="p">:</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="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span><span class="o">*</span><span class="mi">30</span><span class="p">,</span>
<span class="gp"> ....:</span> <span class="s1">'week'</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">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
<span class="gp"> ....:</span> <span class="p">})</span>
<span class="gp"> ....:</span>
<span class="gp">In [28]: </span><span class="n">grouped</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'month'</span><span class="p">,</span><span class="s1">'week'</span><span class="p">])</span>
<span class="gp">In [29]: </span><span class="n">grouped</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">])</span>
<span class="gr">Out[29]: </span>
<span class="go"> mean std</span>
<span class="go">month week </span>
<span class="go">5 1 71.840596 52.886392</span>
<span class="go"> 2 71.904794 55.786805</span>
<span class="go"> 3 89.845632 49.892367</span>
<span class="go">6 1 97.730877 52.442172</span>
<span class="go"> 2 93.369836 47.178389</span>
<span class="go"> 3 96.592088 58.773744</span>
<span class="go">7 1 59.255715 43.442336</span>
<span class="go"> 2 69.634012 28.607369</span>
<span class="go"> 3 84.510992 59.761096</span>
<span class="go">8 1 104.787666 31.745437</span>
<span class="go"> 2 69.717872 53.747188</span>
<span class="go"> 3 79.892221 52.950459</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-185">有关更多详细信息和示例，请参阅<a class="reference internal" href="groupby.html#groupby-aggregate"><span class="std std-ref">the groupby documentation</span></a>。</span></p>
</div>
</div>
<div class="section" id="reshape-reshape2">
<h2><span class="yiyi-st" id="yiyi-186">reshape / reshape2</span></h2>
<div class="section" id="meltarray">
<h3><span class="yiyi-st" id="yiyi-187"><a class="reference external" href="http://www.inside-r.org/packages/cran/reshape2/docs/melt.array"><code class="docutils literal"><span class="pre">melt.array</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-188">使用R中的一个名为<code class="docutils literal"><span class="pre">a</span></code>的三维数组的表达式，其中要将其融化为一个data.frame：</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>a <span class="o"><-</span> <span class="kt">array</span><span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">23</span><span class="p">,</span> <span class="kc">NA</span><span class="p">),</span> <span class="kt">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">,</span><span class="m">4</span><span class="p">))</span>
<span class="kt">data.frame</span><span class="p">(</span>melt<span class="p">(</span>a<span class="p">))</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-189">在Python中，由于<code class="docutils literal"><span class="pre">a</span></code>是一个列表，因此可以使用list comprehension。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [30]: </span><span class="n">a</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="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">24</span><span class="p">))</span><span class="o">+</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="o">.</span><span class="n">reshape</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 [31]: </span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">+</span><span class="p">[</span><span class="n">val</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">(</span><span class="n">a</span><span class="p">)])</span>
<span class="gr">Out[31]: </span>
<span class="go"> 0 1 2 3</span>
<span class="go">0 0 0 0 1.0</span>
<span class="go">1 0 0 1 2.0</span>
<span class="go">2 0 0 2 3.0</span>
<span class="go">3 0 0 3 4.0</span>
<span class="go">4 0 1 0 5.0</span>
<span class="go">5 0 1 1 6.0</span>
<span class="go">6 0 1 2 7.0</span>
<span class="go">.. .. .. .. ...</span>
<span class="go">17 1 1 1 18.0</span>
<span class="go">18 1 1 2 19.0</span>
<span class="go">19 1 1 3 20.0</span>
<span class="go">20 1 2 0 21.0</span>
<span class="go">21 1 2 1 22.0</span>
<span class="go">22 1 2 2 23.0</span>
<span class="go">23 1 2 3 NaN</span>
<span class="go">[24 rows x 4 columns]</span>
</pre></div>
</div>
</div>
<div class="section" id="meltlist">
<h3><span class="yiyi-st" id="yiyi-190"><a class="reference internal" href="#meltlist"><code class="docutils literal"><span class="pre">melt.list</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-191">使用R中的列表<code class="docutils literal"><span class="pre">a</span></code>的表达式，您要将其融化为一个data.frame：</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>a <span class="o"><-</span> <span class="kp">as.list</span><span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">4</span><span class="p">,</span> <span class="kc">NA</span><span class="p">))</span>
<span class="kt">data.frame</span><span class="p">(</span>melt<span class="p">(</span>a<span class="p">))</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-192">在Python中，此列表将是一个元组列表，因此<a class="reference internal" href="generated/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame"><code class="xref py py-meth docutils literal"><span class="pre">DataFrame()</span></code></a>方法会将其转换为所需的数据帧。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [32]: </span><span class="n">a</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</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="o">+</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 [33]: </span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="gr">Out[33]: </span>
<span class="go"> 0 1</span>
<span class="go">0 0 1.0</span>
<span class="go">1 1 2.0</span>
<span class="go">2 2 3.0</span>
<span class="go">3 3 4.0</span>
<span class="go">4 4 NaN</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-193">有关详细信息和示例，请参阅<a class="reference internal" href="dsintro.html#basics-dataframe-from-items"><span class="std std-ref">the Into to Data Structures documentation</span></a>。</span></p>
</div>
<div class="section" id="meltdf">
<h3><span class="yiyi-st" id="yiyi-194"><a class="reference internal" href="#meltdf"><code class="docutils literal"><span class="pre">melt.data.frame</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-195">一个在R中使用名为<code class="docutils literal"><span class="pre">cheese</span></code>的data.frame的表达式，其中要重新整形data.frame：</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>cheese <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span>
first <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">'John'</span><span class="p">,</span> <span class="s">'Mary'</span><span class="p">),</span>
last <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">'Doe'</span><span class="p">,</span> <span class="s">'Bo'</span><span class="p">),</span>
height <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="m">5.5</span><span class="p">,</span> <span class="m">6.0</span><span class="p">),</span>
weight <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="m">130</span><span class="p">,</span> <span class="m">150</span><span class="p">)</span>
<span class="p">)</span>
melt<span class="p">(</span>cheese<span class="p">,</span> id<span class="o">=</span><span class="kt">c</span><span class="p">(</span><span class="s">"first"</span><span class="p">,</span> <span class="s">"last"</span><span class="p">))</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-196">在Python中，<a class="reference internal" href="generated/pandas.melt.html#pandas.melt" title="pandas.melt"><code class="xref py py-meth docutils literal"><span class="pre">melt()</span></code></a>方法是R等价：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [34]: </span><span class="n">cheese</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">'first'</span> <span class="p">:</span> <span class="p">[</span><span class="s1">'John'</span><span class="p">,</span> <span class="s1">'Mary'</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'last'</span> <span class="p">:</span> <span class="p">[</span><span class="s1">'Doe'</span><span class="p">,</span> <span class="s1">'Bo'</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'height'</span> <span class="p">:</span> <span class="p">[</span><span class="mf">5.5</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'weight'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">130</span><span class="p">,</span> <span class="mi">150</span><span class="p">]})</span>
<span class="gp"> ....:</span>
<span class="gp">In [35]: </span><span class="n">pd</span><span class="o">.</span><span class="n">melt</span><span class="p">(</span><span class="n">cheese</span><span class="p">,</span> <span class="n">id_vars</span><span class="o">=</span><span class="p">[</span><span class="s1">'first'</span><span class="p">,</span> <span class="s1">'last'</span><span class="p">])</span>
<span class="gr">Out[35]: </span>
<span class="go"> first last variable value</span>
<span class="go">0 John Doe height 5.5</span>
<span class="go">1 Mary Bo height 6.0</span>
<span class="go">2 John Doe weight 130.0</span>
<span class="go">3 Mary Bo weight 150.0</span>
<span class="gp">In [36]: </span><span class="n">cheese</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s1">'first'</span><span class="p">,</span> <span class="s1">'last'</span><span class="p">])</span><span class="o">.</span><span class="n">stack</span><span class="p">()</span> <span class="c1"># alternative way</span>
<span class="gr">Out[36]: </span>
<span class="go">first last </span>
<span class="go">John Doe height 5.5</span>
<span class="go"> weight 130.0</span>
<span class="go">Mary Bo height 6.0</span>
<span class="go"> weight 150.0</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-197">有关详细信息和示例，请参阅<a class="reference internal" href="reshaping.html#reshaping-melt"><span class="std std-ref">the reshaping documentation</span></a>。</span></p>
</div>
<div class="section" id="cast">
<h3><span class="yiyi-st" id="yiyi-198"><a class="reference internal" href="#cast"><code class="docutils literal"><span class="pre">cast</span></code></a></span></h3>
<p><span class="yiyi-st" id="yiyi-199">在R <code class="docutils literal"><span class="pre">acast</span></code>是一个表达式，使用R中的一个名为<code class="docutils literal"><span class="pre">df</span></code>的数据框来转换为一个更高维数组：</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>df <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span>
x <span class="o">=</span> runif<span class="p">(</span><span class="m">12</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="m">168</span><span class="p">),</span>
y <span class="o">=</span> runif<span class="p">(</span><span class="m">12</span><span class="p">,</span> <span class="m">7</span><span class="p">,</span> <span class="m">334</span><span class="p">),</span>
z <span class="o">=</span> runif<span class="p">(</span><span class="m">12</span><span class="p">,</span> <span class="m">1.7</span><span class="p">,</span> <span class="m">20.7</span><span class="p">),</span>
month <span class="o">=</span> <span class="kp">rep</span><span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="m">5</span><span class="p">,</span><span class="m">6</span><span class="p">,</span><span class="m">7</span><span class="p">),</span><span class="m">4</span><span class="p">),</span>
week <span class="o">=</span> <span class="kp">rep</span><span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">2</span><span class="p">),</span> <span class="m">6</span><span class="p">)</span>
<span class="p">)</span>
mdf <span class="o"><-</span> melt<span class="p">(</span>df<span class="p">,</span> id<span class="o">=</span><span class="kt">c</span><span class="p">(</span><span class="s">"month"</span><span class="p">,</span> <span class="s">"week"</span><span class="p">))</span>
acast<span class="p">(</span>mdf<span class="p">,</span> week <span class="o">~</span> month <span class="o">~</span> variable<span class="p">,</span> <span class="kp">mean</span><span class="p">)</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-200">在Python中，最好的方法是使用<a class="reference internal" href="generated/pandas.pivot_table.html#pandas.pivot_table" title="pandas.pivot_table"><code class="xref py py-meth docutils literal"><span class="pre">pivot_table()</span></code></a>：</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="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="gp"> ....:</span> <span class="s1">'x'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">168.</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="s1">'y'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">7.</span><span class="p">,</span> <span class="mf">334.</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="s1">'z'</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">uniform</span><span class="p">(</span><span class="mf">1.7</span><span class="p">,</span> <span class="mf">20.7</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="gp"> ....:</span> <span class="s1">'month'</span><span class="p">:</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="mi">7</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span>
<span class="gp"> ....:</span> <span class="s1">'week'</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="o">*</span><span class="mi">6</span>
<span class="gp"> ....:</span> <span class="p">})</span>
<span class="gp"> ....:</span>
<span class="gp">In [38]: </span><span class="n">mdf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">melt</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">id_vars</span><span class="o">=</span><span class="p">[</span><span class="s1">'month'</span><span class="p">,</span> <span class="s1">'week'</span><span class="p">])</span>
<span class="gp">In [39]: </span><span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">mdf</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'variable'</span><span class="p">,</span><span class="s1">'week'</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'month'</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
<span class="gp"> ....:</span>
<span class="gr">Out[39]: </span>
<span class="go">month 5 6 7</span>
<span class="go">variable week </span>
<span class="go">x 1 114.001700 132.227290 65.808204</span>
<span class="go"> 2 124.669553 147.495706 82.882820</span>
<span class="go">y 1 225.636630 301.864228 91.706834</span>
<span class="go"> 2 57.692665 215.851669 218.004383</span>
<span class="go">z 1 17.793871 7.124644 17.679823</span>
<span class="go"> 2 15.068355 13.873974 9.394966</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-201">类似地，对于<code class="docutils literal"><span class="pre">dcast</span></code>，它使用R中的数据框架<code class="docutils literal"><span class="pre">df</span></code>，基于<code class="docutils literal"><span class="pre">Animal</span></code>和<code class="docutils literal"><span class="pre">FeedType</span></code></span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span>df <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span>
Animal <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">'Animal1'</span><span class="p">,</span> <span class="s">'Animal2'</span><span class="p">,</span> <span class="s">'Animal3'</span><span class="p">,</span> <span class="s">'Animal2'</span><span class="p">,</span> <span class="s">'Animal1'</span><span class="p">,</span>
<span class="s">'Animal2'</span><span class="p">,</span> <span class="s">'Animal3'</span><span class="p">),</span>
FeedType <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">'A'</span><span class="p">,</span> <span class="s">'B'</span><span class="p">,</span> <span class="s">'A'</span><span class="p">,</span> <span class="s">'A'</span><span class="p">,</span> <span class="s">'B'</span><span class="p">,</span> <span class="s">'B'</span><span class="p">,</span> <span class="s">'A'</span><span class="p">),</span>
Amount <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="m">10</span><span class="p">,</span> <span class="m">7</span><span class="p">,</span> <span class="m">4</span><span class="p">,</span> <span class="m">2</span><span class="p">,</span> <span class="m">5</span><span class="p">,</span> <span class="m">6</span><span class="p">,</span> <span class="m">2</span><span class="p">)</span>
<span class="p">)</span>
dcast<span class="p">(</span>df<span class="p">,</span> Animal <span class="o">~</span> FeedType<span class="p">,</span> <span class="kp">sum</span><span class="p">,</span> fill<span class="o">=</span><span class="kc">NaN</span><span class="p">)</span>
<span class="c1"># Alternative method using base R</span>
<span class="kp">with</span><span class="p">(</span>df<span class="p">,</span> <span class="kp">tapply</span><span class="p">(</span>Amount<span class="p">,</span> <span class="kt">list</span><span class="p">(</span>Animal<span class="p">,</span> FeedType<span class="p">),</span> <span class="kp">sum</span><span class="p">))</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-202">Python可以通过两种不同的方式来实现。</span><span class="yiyi-st" id="yiyi-203">首先，类似于上面使用<a class="reference internal" href="generated/pandas.pivot_table.html#pandas.pivot_table" title="pandas.pivot_table"><code class="xref py py-meth docutils literal"><span class="pre">pivot_table()</span></code></a>：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [40]: </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="gp"> ....:</span> <span class="s1">'Animal'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'Animal1'</span><span class="p">,</span> <span class="s1">'Animal2'</span><span class="p">,</span> <span class="s1">'Animal3'</span><span class="p">,</span> <span class="s1">'Animal2'</span><span class="p">,</span> <span class="s1">'Animal1'</span><span class="p">,</span>
<span class="gp"> ....:</span> <span class="s1">'Animal2'</span><span class="p">,</span> <span class="s1">'Animal3'</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'FeedType'</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="s1">'A'</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">'B'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'Amount'</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</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="mi">2</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="p">})</span>
<span class="gp"> ....:</span>
<span class="gp">In [41]: </span><span class="n">df</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="s1">'Amount'</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="s1">'Animal'</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s1">'FeedType'</span><span class="p">,</span> <span class="n">aggfunc</span><span class="o">=</span><span class="s1">'sum'</span><span class="p">)</span>
<span class="gr">Out[41]: </span>
<span class="go">FeedType A B</span>
<span class="go">Animal </span>
<span class="go">Animal1 10.0 5.0</span>
<span class="go">Animal2 2.0 13.0</span>
<span class="go">Animal3 6.0 NaN</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-204">第二种方法是使用<a class="reference internal" href="generated/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby" title="pandas.DataFrame.groupby"><code class="xref py py-meth docutils literal"><span class="pre">groupby()</span></code></a>方法：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [42]: </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Animal'</span><span class="p">,</span><span class="s1">'FeedType'</span><span class="p">])[</span><span class="s1">'Amount'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="gr">Out[42]: </span>
<span class="go">Animal FeedType</span>
<span class="go">Animal1 A 10</span>
<span class="go"> B 5</span>
<span class="go">Animal2 A 2</span>
<span class="go"> B 13</span>
<span class="go">Animal3 A 6</span>
<span class="go">Name: Amount, dtype: int64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-205">有关详细信息和示例，请参阅<a class="reference internal" href="reshaping.html#reshaping-pivot"><span class="std std-ref">the reshaping documentation</span></a>或<a class="reference internal" href="groupby.html#groupby-split"><span class="std std-ref">the groupby documentation</span></a>。</span></p>
</div>
<div class="section" id="factor">
<h3><span class="yiyi-st" id="yiyi-206"><a class="reference external" href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/factor.html"><code class="docutils literal"><span class="pre">factor</span></code></a></span></h3>
<div class="versionadded">
<p><span class="yiyi-st" id="yiyi-207"><span class="versionmodified">版本0.15中的新功能。</span></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-208">pandas具有用于分类数据的数据类型。</span></p>
<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kp">cut</span><span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">,</span><span class="m">4</span><span class="p">,</span><span class="m">5</span><span class="p">,</span><span class="m">6</span><span class="p">),</span> <span class="m">3</span><span class="p">)</span>
<span class="kp">factor</span><span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">,</span><span class="m">2</span><span class="p">,</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">))</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-209">在pandas中，这是通过<code class="docutils literal"><span class="pre">pd.cut</span></code>和<code class="docutils literal"><span class="pre">astype("category")</span></code>完成的：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [43]: </span><span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</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">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="mi">3</span><span class="p">)</span>
<span class="gr">Out[43]: </span>
<span class="go">0 (0.995, 2.667]</span>
<span class="go">1 (0.995, 2.667]</span>
<span class="go">2 (2.667, 4.333]</span>
<span class="go">3 (2.667, 4.333]</span>
<span class="go">4 (4.333, 6]</span>
<span class="go">5 (4.333, 6]</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, object): [(0.995, 2.667] < (2.667, 4.333] < (4.333, 6]]</span>
<span class="gp">In [44]: </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">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="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"category"</span><span class="p">)</span>
<span class="gr">Out[44]: </span>
<span class="go">0 1</span>
<span class="go">1 2</span>
<span class="go">2 3</span>
<span class="go">3 2</span>
<span class="go">4 2</span>
<span class="go">5 3</span>
<span class="go">dtype: category</span>
<span class="go">Categories (3, int64): [1, 2, 3]</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-210">有关详细信息和示例，请参见<a class="reference internal" href="categorical.html#categorical"><span class="std std-ref">categorical introduction</span></a>和<a class="reference internal" href="api.html#api-categorical"><span class="std std-ref">API documentation</span></a>。</span><span class="yiyi-st" id="yiyi-211">还有关于<a class="reference internal" href="categorical.html#categorical-rfactor"><span class="std std-ref">differences to R’s factor</span></a>的差异的文档。</span></p>
</div>
</div>