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cookbook.html
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<span id="id1"></span><h1><span class="yiyi-st" id="yiyi-84">Cookbook</span></h1>
<blockquote>
<p>原文：<a href="http://pandas.pydata.org/pandas-docs/stable/cookbook.html">http://pandas.pydata.org/pandas-docs/stable/cookbook.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-85">这里有大量的例程和链接，就像食谱中 <em>简单而可口</em> 示例一样，希望能对您学习pandas库有用。</span><span class="yiyi-st" id="yiyi-86">我们鼓励用户为此文档添加内容。</span></p>
<p><span class="yiyi-st" id="yiyi-87">如果您向此部分中添加链接或者例子，我们会把它当做 <em>First Pull Request</em> 处理。</span></p>
<p><span class="yiyi-st" id="yiyi-88">我们贴在此处的示例大多都是经过简化、凝练的，对新用户友好。这些例子可能已经在Stack-Overflow进行了讨论或者已经出现在了GitHub链接中。</span><span class="yiyi-st" id="yiyi-89">那些链接包含有更多的信息，这里的例程并没有扩展讲解。</span></p>
<p><span class="yiyi-st" id="yiyi-90">为了照顾初学者，在导入的模块中只有Pandas（pd）和Numpy（np）进行了缩写，</span><span class="yiyi-st" id="yiyi-91">其余的模块导入时都保留了原来的名称。</span></p>
<p><span class="yiyi-st" id="yiyi-92">这些例子是为python 3.4编写的。</span><span class="yiyi-st" id="yiyi-93">较早的python版本或许需要修改一下。</span></p>
<div class="section" id="idioms">
<h2><span class="yiyi-st" id="yiyi-94">Idioms</span></h2>
<p id="cookbook-idioms"><span class="yiyi-st" id="yiyi-95">这里有一些整齐优雅的pandas<code class="docutils literal"><span class="pre">惯用写法</span></code></span></p>
<p><span class="yiyi-st" id="yiyi-96"><a class="reference external" href="http://stackoverflow.com/questions/17128302/python-pandas-idiom-for-if-then-else">使用if-then / if-then-else选定某一列中某些值，但是却修改这些值所在行的其他一列或若干列中的值：</a></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="gp"> ...:</span> <span class="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ...:</span>
<span class="gr">Out[1]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 20 50</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
</pre></div>
</div>
<div class="section" id="if-then">
<h3><span class="yiyi-st" id="yiyi-97">if-then...</span></h3>
<p><span class="yiyi-st" id="yiyi-98">if-then 选中修改一列的值</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [2]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">AAA</span> <span class="o">>=</span> <span class="mi">5</span><span class="p">,</span><span class="s1">'BBB'</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span> <span class="n">df</span>
<span class="gr">Out[2]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 -1 50</span>
<span class="go">2 6 -1 -30</span>
<span class="go">3 7 -1 -50</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-99">if-then 选中修改2列的值</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [3]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">AAA</span> <span class="o">>=</span> <span class="mi">5</span><span class="p">,[</span><span class="s1">'BBB'</span><span class="p">,</span><span class="s1">'CCC'</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">555</span><span class="p">;</span> <span class="n">df</span>
<span class="gr">Out[3]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 555 555</span>
<span class="go">2 6 555 555</span>
<span class="go">3 7 555 555</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-100">此例与上一例逻辑相反，用来实现 else</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">AAA</span> <span class="o"><</span> <span class="mi">5</span><span class="p">,[</span><span class="s1">'BBB'</span><span class="p">,</span><span class="s1">'CCC'</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">2000</span><span class="p">;</span> <span class="n">df</span>
<span class="gr">Out[4]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 2000 2000</span>
<span class="go">1 5 555 555</span>
<span class="go">2 6 555 555</span>
<span class="go">3 7 555 555</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-101">或者你也可以使用mask</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="n">df_mask</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">'AAA'</span> <span class="p">:</span> <span class="p">[</span><span class="bp">True</span><span class="p">]</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="bp">False</span><span class="p">]</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="bp">True</span><span class="p">,</span><span class="bp">False</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span><span class="p">})</span>
<span class="gp">In [6]: </span><span class="n">df</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">df_mask</span><span class="p">,</span><span class="o">-</span><span class="mi">1000</span><span class="p">)</span>
<span class="gr">Out[6]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 -1000 2000</span>
<span class="go">1 5 -1000 -1000</span>
<span class="go">2 6 -1000 555</span>
<span class="go">3 7 -1000 -1000</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-102"><a class="reference external" href="http://stackoverflow.com/questions/19913659/pandas-conditional-creation-of-a-series-dataframe-column">通过numpy's where()，实现if-then-else的逻辑</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></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="gp"> ...:</span> <span class="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ...:</span>
<span class="gr">Out[7]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 20 50</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
<span class="gp">In [8]: </span><span class="n">df</span><span class="p">[</span><span class="s1">'logic'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'AAA'</span><span class="p">]</span> <span class="o">></span> <span class="mi">5</span><span class="p">,</span><span class="s1">'high'</span><span class="p">,</span><span class="s1">'low'</span><span class="p">);</span> <span class="n">df</span>
<span class="gr">Out[8]: </span>
<span class="go"> AAA BBB CCC logic</span>
<span class="go">0 4 10 100 low</span>
<span class="go">1 5 20 50 low</span>
<span class="go">2 6 30 -30 high</span>
<span class="go">3 7 40 -50 high</span>
</pre></div>
</div>
</div>
<div class="section" id="splitting">
<h3><span class="yiyi-st" id="yiyi-103">Splitting</span></h3>
<p><span class="yiyi-st" id="yiyi-104"><a class="reference external" href="http://stackoverflow.com/questions/14957116/how-to-split-a-dataframe-according-to-a-boolean-criterion">使用布尔值标准选择数据</a></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="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ...:</span>
<span class="gr">Out[9]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 20 50</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
<span class="gp">In [10]: </span><span class="n">dflow</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">AAA</span> <span class="o"><=</span> <span class="mi">5</span><span class="p">]</span>
<span class="gp">In [11]: </span><span class="n">dfhigh</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">AAA</span> <span class="o">></span> <span class="mi">5</span><span class="p">]</span>
<span class="gp">In [12]: </span><span class="n">dflow</span><span class="p">;</span> <span class="n">dfhigh</span>
<span class="gr">Out[12]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
</pre></div>
</div>
</div>
<div class="section" id="building-criteria">
<h3><span class="yiyi-st" id="yiyi-105">Building Criteria</span></h3>
<p><span class="yiyi-st" id="yiyi-106"><a class="reference external" href="http://stackoverflow.com/questions/15315452/selecting-with-complex-criteria-from-pandas-dataframe">根据多列的值选择</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [13]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
<span class="gp"> ....:</span> <span class="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[13]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 20 50</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-107">...实现‘与’操作（没有更改原内容而是返回了一个新的Series）</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="n">newseries</span> <span class="o">=</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="p">[</span><span class="s1">'BBB'</span><span class="p">]</span> <span class="o"><</span> <span class="mi">25</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'CCC'</span><span class="p">]</span> <span class="o">>=</span> <span class="o">-</span><span class="mi">40</span><span class="p">),</span> <span class="s1">'AAA'</span><span class="p">];</span> <span class="n">newseries</span>
<span class="gr">Out[14]: </span>
<span class="go">0 4</span>
<span class="go">1 5</span>
<span class="go">Name: AAA, dtype: int64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-108">...实现“或”操作（没有更改原内容而是返回了一个新的Series）</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [15]: </span><span class="n">newseries</span> <span class="o">=</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="p">[</span><span class="s1">'BBB'</span><span class="p">]</span> <span class="o">></span> <span class="mi">25</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'CCC'</span><span class="p">]</span> <span class="o">>=</span> <span class="o">-</span><span class="mi">40</span><span class="p">),</span> <span class="s1">'AAA'</span><span class="p">];</span> <span class="n">newseries</span><span class="p">;</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-109">...实现“或”操作（直接修改了DataFrame的内容）</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [16]: </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="p">[</span><span class="s1">'BBB'</span><span class="p">]</span> <span class="o">></span> <span class="mi">25</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'CCC'</span><span class="p">]</span> <span class="o">>=</span> <span class="mi">75</span><span class="p">),</span> <span class="s1">'AAA'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">;</span> <span class="n">df</span>
<span class="gr">Out[16]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 0.1 10 100</span>
<span class="go">1 5.0 20 50</span>
<span class="go">2 0.1 30 -30</span>
<span class="go">3 0.1 40 -50</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-110"><a class="reference external" href="http://stackoverflow.com/questions/17758023/return-rows-in-a-dataframe-closest-to-a-user-defined-number">使用argsort</a>选择数据最接近某个值的行</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [17]: </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="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[17]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 20 50</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
<span class="gp">In [18]: </span><span class="n">aValue</span> <span class="o">=</span> <span class="mf">43.0</span>
<span class="gp">In [19]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">CCC</span><span class="o">-</span><span class="n">aValue</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">argsort</span><span class="p">()]</span>
<span class="gr">Out[19]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">1 5 20 50</span>
<span class="go">0 4 10 100</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-111"><a class="reference external" href="http://stackoverflow.com/questions/21058254/pandas-boolean-operation-in-a-python-list/21058331">使用二元运算符</a>动态地减少条件列表</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="gp"> ....:</span> <span class="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[20]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 20 50</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
<span class="gp">In [21]: </span><span class="n">Crit1</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">AAA</span> <span class="o"><=</span> <span class="mf">5.5</span>
<span class="gp">In [22]: </span><span class="n">Crit2</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">BBB</span> <span class="o">==</span> <span class="mf">10.0</span>
<span class="gp">In [23]: </span><span class="n">Crit3</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">CCC</span> <span class="o">></span> <span class="o">-</span><span class="mf">40.0</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-112">可以硬编码：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [24]: </span><span class="n">AllCrit</span> <span class="o">=</span> <span class="n">Crit1</span> <span class="o">&</span> <span class="n">Crit2</span> <span class="o">&</span> <span class="n">Crit3</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-113">...或者可以使用动态构建的标准列表</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [25]: </span><span class="n">CritList</span> <span class="o">=</span> <span class="p">[</span><span class="n">Crit1</span><span class="p">,</span><span class="n">Crit2</span><span class="p">,</span><span class="n">Crit3</span><span class="p">]</span>
<span class="gp">In [26]: </span><span class="n">AllCrit</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">&</span> <span class="n">y</span><span class="p">,</span> <span class="n">CritList</span><span class="p">)</span>
<span class="gp">In [27]: </span><span class="n">df</span><span class="p">[</span><span class="n">AllCrit</span><span class="p">]</span>
<span class="gr">Out[27]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="selection">
<span id="cookbook-selection"></span><h2><span class="yiyi-st" id="yiyi-114">Selection</span></h2>
<div class="section" id="dataframes">
<h3><span class="yiyi-st" id="yiyi-115">DataFrames</span></h3>
<p><span class="yiyi-st" id="yiyi-116"><a class="reference internal" href="indexing.html#indexing"><span class="std std-ref">indexing</span></a>文档。</span></p>
<p><span class="yiyi-st" id="yiyi-117"><a class="reference external" href="http://stackoverflow.com/questions/14725068/pandas-using-row-labels-in-boolean-indexing">使用行标签和值条件</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [28]: </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="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[28]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 20 50</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
<span class="gp">In [29]: </span><span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">AAA</span> <span class="o"><=</span> <span class="mi">6</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">]))]</span>
<span class="gr">Out[29]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">2 6 30 -30</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-118"><a class="reference external" href="https://github.com/pandas-dev/pandas/issues/2904">使用loc进行面向标签切片和iloc位置切片</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [30]: </span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span><span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]}</span>
<span class="gp">In [31]: </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">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'foo'</span><span class="p">,</span><span class="s1">'bar'</span><span class="p">,</span><span class="s1">'boo'</span><span class="p">,</span><span class="s1">'kar'</span><span class="p">]);</span> <span class="n">df</span>
<span class="gr">Out[31]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">foo 4 10 100</span>
<span class="go">bar 5 20 50</span>
<span class="go">boo 6 30 -30</span>
<span class="go">kar 7 40 -50</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-119">有2个显式切片方法，第三个一般情况</span></p>
<ol class="arabic simple">
<li><span class="yiyi-st" id="yiyi-120">位置导向（Python切片风格：不包括end）</span></li>
<li><span class="yiyi-st" id="yiyi-121">面向标签（非Python切片样式：包括end）</span></li>
<li><span class="yiyi-st" id="yiyi-122">一般（切片样式：取决于切片是否包含标签或位置）</span></li>
</ol>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [32]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'bar'</span><span class="p">:</span><span class="s1">'kar'</span><span class="p">]</span> <span class="c1">#Label</span>
<span class="gr">Out[32]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">bar 5 20 50</span>
<span class="go">boo 6 30 -30</span>
<span class="go">kar 7 40 -50</span>
<span class="c">#Generic</span>
<span class="gp">In [33]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> <span class="c1">#Same as .iloc[0:3]</span>
<span class="gr">Out[33]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">foo 4 10 100</span>
<span class="go">bar 5 20 50</span>
<span class="go">boo 6 30 -30</span>
<span class="gp">In [34]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="s1">'bar'</span><span class="p">:</span><span class="s1">'kar'</span><span class="p">]</span> <span class="c1">#Same as .loc['bar':'kar']</span>
<span class="gr">Out[34]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">bar 5 20 50</span>
<span class="go">boo 6 30 -30</span>
<span class="go">kar 7 40 -50</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-123">当索引由具有非零开始或非单位增量的整数组成时，会出现模糊性。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [35]: </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="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]);</span> <span class="c1">#Note index starts at 1.</span>
<span class="gp">In [36]: </span><span class="n">df2</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="c1">#Position-oriented</span>
<span class="gr">Out[36]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">2 5 20 50</span>
<span class="go">3 6 30 -30</span>
<span class="gp">In [37]: </span><span class="n">df2</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> <span class="c1">#Label-oriented</span>
<span class="gr">Out[37]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">1 4 10 100</span>
<span class="go">2 5 20 50</span>
<span class="go">3 6 30 -30</span>
<span class="gp">In [38]: </span><span class="n">df2</span><span class="o">.</span><span class="n">ix</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="c1">#General, will mimic loc (label-oriented)</span>
<span class="gr">Out[38]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">1 4 10 100</span>
<span class="go">2 5 20 50</span>
<span class="go">3 6 30 -30</span>
<span class="gp">In [39]: </span><span class="n">df2</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> <span class="c1">#General, will mimic iloc (position-oriented), as loc[0:3] would raise a KeyError</span>
<span class="gr">Out[39]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">1 4 10 100</span>
<span class="go">2 5 20 50</span>
<span class="go">3 6 30 -30</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-124"><a class="reference external" href="http://stackoverflow.com/questions/14986510/picking-out-elements-based-on-complement-of-indices-in-python-pandas">使用反向运算符（〜）来取掩码的补码</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="p">{</span><span class="s1">'AAA'</span> <span class="p">:</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">7</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">40</span><span class="p">],</span> <span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[40]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 4 10 100</span>
<span class="go">1 5 20 50</span>
<span class="go">2 6 30 -30</span>
<span class="go">3 7 40 -50</span>
<span class="gp">In [41]: </span><span class="n">df</span><span class="p">[</span><span class="o">~</span><span class="p">((</span><span class="n">df</span><span class="o">.</span><span class="n">AAA</span> <span class="o"><=</span> <span class="mi">6</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">])))]</span>
<span class="gr">Out[41]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">1 5 20 50</span>
<span class="go">3 7 40 -50</span>
</pre></div>
</div>
</div>
<div class="section" id="panels">
<h3><span class="yiyi-st" id="yiyi-125">Panels</span></h3>
<p><span class="yiyi-st" id="yiyi-126"><a class="reference external" href="http://stackoverflow.com/questions/15364050/extending-a-pandas-panel-frame-along-the-minor-axis">通过移调，添加新尺寸并转换回原始尺寸来扩展面板框架</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [42]: </span><span class="n">rng</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">'1/1/2013'</span><span class="p">,</span><span class="n">periods</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span><span class="n">freq</span><span class="o">=</span><span class="s1">'D'</span><span class="p">)</span>
<span class="gp">In [43]: </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="gp">In [44]: </span><span class="n">cols</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 [45]: </span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</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="n">data</span><span class="p">,</span> <span class="n">rng</span><span class="p">,</span> <span class="n">cols</span><span class="p">),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">rng</span><span class="p">,</span> <span class="n">cols</span><span class="p">),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">rng</span><span class="p">,</span> <span class="n">cols</span><span class="p">)</span>
<span class="gp">In [46]: </span><span class="n">pf</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">'df1'</span><span class="p">:</span><span class="n">df1</span><span class="p">,</span><span class="s1">'df2'</span><span class="p">:</span><span class="n">df2</span><span class="p">,</span><span class="s1">'df3'</span><span class="p">:</span><span class="n">df3</span><span class="p">});</span><span class="n">pf</span>
<span class="gr">Out[46]: </span>
<span class="go"><class 'pandas.core.panel.Panel'></span>
<span class="go">Dimensions: 3 (items) x 100 (major_axis) x 4 (minor_axis)</span>
<span class="go">Items axis: df1 to df3</span>
<span class="go">Major_axis axis: 2013-01-01 00:00:00 to 2013-04-10 00:00:00</span>
<span class="go">Minor_axis axis: A to D</span>
<span class="c">#Assignment using Transpose (pandas < 0.15)</span>
<span class="gp">In [47]: </span><span class="n">pf</span> <span class="o">=</span> <span class="n">pf</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</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="gp">In [48]: </span><span class="n">pf</span><span class="p">[</span><span class="s1">'E'</span><span class="p">]</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">data</span><span class="p">,</span> <span class="n">rng</span><span class="p">,</span> <span class="n">cols</span><span class="p">)</span>
<span class="gp">In [49]: </span><span class="n">pf</span> <span class="o">=</span> <span class="n">pf</span><span class="o">.</span><span class="n">transpose</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">0</span><span class="p">);</span><span class="n">pf</span>
<span class="gr">Out[49]: </span>
<span class="go"><class 'pandas.core.panel.Panel'></span>
<span class="go">Dimensions: 3 (items) x 100 (major_axis) x 5 (minor_axis)</span>
<span class="go">Items axis: df1 to df3</span>
<span class="go">Major_axis axis: 2013-01-01 00:00:00 to 2013-04-10 00:00:00</span>
<span class="go">Minor_axis axis: A to E</span>
<span class="c">#Direct assignment (pandas > 0.15)</span>
<span class="gp">In [50]: </span><span class="n">pf</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,:,</span><span class="s1">'F'</span><span class="p">]</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">data</span><span class="p">,</span> <span class="n">rng</span><span class="p">,</span> <span class="n">cols</span><span class="p">);</span><span class="n">pf</span>
<span class="gr">Out[50]: </span>
<span class="go"><class 'pandas.core.panel.Panel'></span>
<span class="go">Dimensions: 3 (items) x 100 (major_axis) x 6 (minor_axis)</span>
<span class="go">Items axis: df1 to df3</span>
<span class="go">Major_axis axis: 2013-01-01 00:00:00 to 2013-04-10 00:00:00</span>
<span class="go">Minor_axis axis: A to F</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-127"><a class="reference external" href="http://stackoverflow.com/questions/14650341/boolean-mask-in-pandas-panel">使用np.where屏蔽面板，然后使用新的屏蔽值</a>重建面板</span></p>
</div>
<div class="section" id="new-columns">
<h3><span class="yiyi-st" id="yiyi-128">New Columns</span></h3>
<p><span class="yiyi-st" id="yiyi-129"><a class="reference external" href="http://stackoverflow.com/questions/16575868/efficiently-creating-additional-columns-in-a-pandas-dataframe-using-map">使用applymap</a>有效且动态地创建新列</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [51]: </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="p">{</span><span class="s1">'AAA'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="s1">'CCC'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[51]: </span>
<span class="go"> AAA BBB CCC</span>
<span class="go">0 1 1 2</span>
<span class="go">1 2 1 1</span>
<span class="go">2 1 2 3</span>
<span class="go">3 3 2 1</span>
<span class="gp">In [52]: </span><span class="n">source_cols</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="c1"># or some subset would work too.</span>
<span class="gp">In [53]: </span><span class="n">new_cols</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="s2">"_cat"</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">source_cols</span><span class="p">]</span>
<span class="gp">In [54]: </span><span class="n">categories</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span> <span class="p">:</span> <span class="s1">'Alpha'</span><span class="p">,</span> <span class="mi">2</span> <span class="p">:</span> <span class="s1">'Beta'</span><span class="p">,</span> <span class="mi">3</span> <span class="p">:</span> <span class="s1">'Charlie'</span> <span class="p">}</span>
<span class="gp">In [55]: </span><span class="n">df</span><span class="p">[</span><span class="n">new_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">source_cols</span><span class="p">]</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">categories</span><span class="o">.</span><span class="n">get</span><span class="p">);</span><span class="n">df</span>
<span class="gr">Out[55]: </span>
<span class="go"> AAA BBB CCC AAA_cat BBB_cat CCC_cat</span>
<span class="go">0 1 1 2 Alpha Alpha Beta</span>
<span class="go">1 2 1 1 Beta Alpha Alpha</span>
<span class="go">2 1 2 3 Alpha Beta Charlie</span>
<span class="go">3 3 2 1 Charlie Beta Alpha</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-130"><a class="reference external" href="http://stackoverflow.com/questions/23394476/keep-other-columns-when-using-min-with-groupby">在groupby</a>中使用min()时保留其他列</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [56]: </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="p">{</span><span class="s1">'AAA'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="s1">'BBB'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</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">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[56]: </span>
<span class="go"> AAA BBB</span>
<span class="go">0 1 2</span>
<span class="go">1 1 1</span>
<span class="go">2 1 3</span>
<span class="go">3 2 4</span>
<span class="go">4 2 5</span>
<span class="go">5 2 1</span>
<span class="go">6 3 2</span>
<span class="go">7 3 3</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-131">方法1：idxmin()获取最小的索引的完整数据原格式</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [57]: </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">groupby</span><span class="p">(</span><span class="s2">"AAA"</span><span class="p">)[</span><span class="s2">"BBB"</span><span class="p">]</span><span class="o">.</span><span class="n">idxmin</span><span class="p">()]</span>
<span class="gr">Out[57]: </span>
<span class="go"> AAA BBB</span>
<span class="go">1 1 1</span>
<span class="go">5 2 1</span>
<span class="go">6 3 2</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-132">方法2：排序，然后取第一</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [58]: </span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">"BBB"</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"AAA"</span><span class="p">,</span> <span class="n">as_index</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span><span class="o">.</span><span class="n">first</span><span class="p">()</span>
<span class="gr">Out[58]: </span>
<span class="go"> AAA BBB</span>
<span class="go">0 1 1</span>
<span class="go">1 2 1</span>
<span class="go">2 3 2</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-133">注意相同的结果，除了索引。</span></p>
</div>
</div>
<div class="section" id="multiindexing">
<span id="cookbook-multi-index"></span><h2><span class="yiyi-st" id="yiyi-134">MultiIndexing</span></h2>
<p><span class="yiyi-st" id="yiyi-135"><a class="reference internal" href="advanced.html#advanced-hierarchical"><span class="std std-ref">multindexing</span></a>。</span></p>
<p><span class="yiyi-st" id="yiyi-136"><a class="reference external" href="http://stackoverflow.com/questions/14916358/reshaping-dataframes-in-pandas-based-on-column-labels">从带标签的DataFrame创建多索引</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [59]: </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">'row'</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="mi">2</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'One_X'</span> <span class="p">:</span> <span class="p">[</span><span class="mf">1.1</span><span class="p">,</span><span class="mf">1.1</span><span class="p">,</span><span class="mf">1.1</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'One_Y'</span> <span class="p">:</span> <span class="p">[</span><span class="mf">1.2</span><span class="p">,</span><span class="mf">1.2</span><span class="p">,</span><span class="mf">1.2</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'Two_X'</span> <span class="p">:</span> <span class="p">[</span><span class="mf">1.11</span><span class="p">,</span><span class="mf">1.11</span><span class="p">,</span><span class="mf">1.11</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'Two_Y'</span> <span class="p">:</span> <span class="p">[</span><span class="mf">1.22</span><span class="p">,</span><span class="mf">1.22</span><span class="p">,</span><span class="mf">1.22</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[59]: </span>
<span class="go"> One_X One_Y Two_X Two_Y row</span>
<span class="go">0 1.1 1.2 1.11 1.22 0</span>
<span class="go">1 1.1 1.2 1.11 1.22 1</span>
<span class="go">2 1.1 1.2 1.11 1.22 2</span>
<span class="c"># As Labelled Index</span>
<span class="gp">In [60]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">'row'</span><span class="p">);</span><span class="n">df</span>
<span class="gr">Out[60]: </span>
<span class="go"> One_X One_Y Two_X Two_Y</span>
<span class="go">row </span>
<span class="go">0 1.1 1.2 1.11 1.22</span>
<span class="go">1 1.1 1.2 1.11 1.22</span>
<span class="go">2 1.1 1.2 1.11 1.22</span>
<span class="c"># With Hierarchical Columns</span>
<span class="gp">In [61]: </span><span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">([</span><span class="nb">tuple</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'_'</span><span class="p">))</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">]);</span><span class="n">df</span>
<span class="gr">Out[61]: </span>
<span class="go"> One Two </span>
<span class="go"> X Y X Y</span>
<span class="go">row </span>
<span class="go">0 1.1 1.2 1.11 1.22</span>
<span class="go">1 1.1 1.2 1.11 1.22</span>
<span class="go">2 1.1 1.2 1.11 1.22</span>
<span class="c"># Now stack & Reset</span>
<span class="gp">In [62]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span><span class="n">df</span>
<span class="gr">Out[62]: </span>
<span class="go"> level_1 X Y</span>
<span class="go">row </span>
<span class="go">0 One 1.10 1.20</span>
<span class="go">0 Two 1.11 1.22</span>
<span class="go">1 One 1.10 1.20</span>
<span class="go">1 Two 1.11 1.22</span>
<span class="go">2 One 1.10 1.20</span>
<span class="go">2 Two 1.11 1.22</span>
<span class="c"># And fix the labels (Notice the label 'level_1' got added automatically)</span>
<span class="gp">In [63]: </span><span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Sample'</span><span class="p">,</span><span class="s1">'All_X'</span><span class="p">,</span><span class="s1">'All_Y'</span><span class="p">];</span><span class="n">df</span>
<span class="gr">Out[63]: </span>
<span class="go"> Sample All_X All_Y</span>
<span class="go">row </span>
<span class="go">0 One 1.10 1.20</span>
<span class="go">0 Two 1.11 1.22</span>
<span class="go">1 One 1.10 1.20</span>
<span class="go">1 Two 1.11 1.22</span>
<span class="go">2 One 1.10 1.20</span>
<span class="go">2 Two 1.11 1.22</span>
</pre></div>
</div>
<div class="section" id="arithmetic">
<h3><span class="yiyi-st" id="yiyi-137">Arithmetic</span></h3>
<p><span class="yiyi-st" id="yiyi-138"><a class="reference external" href="http://stackoverflow.com/questions/19501510/divide-entire-pandas-multiindex-dataframe-by-dataframe-variable/19502176#19502176">使用需要广播的多索引进行算术</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [64]: </span><span class="n">cols</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">([</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</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="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'O'</span><span class="p">,</span><span class="s1">'I'</span><span class="p">]])</span>
<span class="gp">In [65]: </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">2</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="p">[</span><span class="s1">'n'</span><span class="p">,</span><span class="s1">'m'</span><span class="p">],</span><span class="n">columns</span><span class="o">=</span><span class="n">cols</span><span class="p">);</span> <span class="n">df</span>
<span class="gr">Out[65]: </span>
<span class="go"> A B C </span>
<span class="go"> O I O I O I</span>
<span class="go">n 1.920906 -0.388231 -2.314394 0.665508 0.402562 0.399555</span>
<span class="go">m -1.765956 0.850423 0.388054 0.992312 0.744086 -0.739776</span>
<span class="gp">In [66]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'C'</span><span class="p">],</span><span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">);</span> <span class="n">df</span>
<span class="gr">Out[66]: </span>
<span class="go"> A B C </span>
<span class="go"> O I O I O I</span>
<span class="go">n 4.771702 -0.971660 -5.749162 1.665625 1.0 1.0</span>
<span class="go">m -2.373321 -1.149568 0.521518 -1.341367 1.0 1.0</span>
</pre></div>
</div>
</div>
<div class="section" id="slicing">
<h3><span class="yiyi-st" id="yiyi-139">Slicing</span></h3>
<p><span class="yiyi-st" id="yiyi-140"><a class="reference external" href="http://stackoverflow.com/questions/12590131/how-to-slice-multindex-columns-in-pandas-dataframes">使用xs</a>切片多索引</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [67]: </span><span class="n">coords</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">'AA'</span><span class="p">,</span><span class="s1">'one'</span><span class="p">),(</span><span class="s1">'AA'</span><span class="p">,</span><span class="s1">'six'</span><span class="p">),(</span><span class="s1">'BB'</span><span class="p">,</span><span class="s1">'one'</span><span class="p">),(</span><span class="s1">'BB'</span><span class="p">,</span><span class="s1">'two'</span><span class="p">),(</span><span class="s1">'BB'</span><span class="p">,</span><span class="s1">'six'</span><span class="p">)]</span>
<span class="gp">In [68]: </span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="n">coords</span><span class="p">)</span>
<span class="gp">In [69]: </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="mi">11</span><span class="p">,</span><span class="mi">22</span><span class="p">,</span><span class="mi">33</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="n">index</span><span class="p">,[</span><span class="s1">'MyData'</span><span class="p">]);</span> <span class="n">df</span>
<span class="gr">Out[69]: </span>
<span class="go"> MyData</span>
<span class="go">AA one 11</span>
<span class="go"> six 22</span>
<span class="go">BB one 33</span>
<span class="go"> two 44</span>
<span class="go"> six 55</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-141">要取第1级和第1轴的横截面索引：</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [70]: </span><span class="n">df</span><span class="o">.</span><span class="n">xs</span><span class="p">(</span><span class="s1">'BB'</span><span class="p">,</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1">#Note : level and axis are optional, and default to zero</span>
<span class="gr">Out[70]: </span>
<span class="go"> MyData</span>
<span class="go">one 33</span>
<span class="go">two 44</span>
<span class="go">six 55</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-142">...现在是第1轴的第2级。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [71]: </span><span class="n">df</span><span class="o">.</span><span class="n">xs</span><span class="p">(</span><span class="s1">'six'</span><span class="p">,</span><span class="n">level</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">0</span><span class="p">)</span>
<span class="gr">Out[71]: </span>
<span class="go"> MyData</span>
<span class="go">AA 22</span>
<span class="go">BB 55</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-143"><a class="reference external" href="http://stackoverflow.com/questions/14964493/multiindex-based-indexing-in-pandas">使用xs，方法＃2</a>来切割多索引</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [72]: </span><span class="n">index</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">([</span><span class="s1">'Ada'</span><span class="p">,</span><span class="s1">'Quinn'</span><span class="p">,</span><span class="s1">'Violet'</span><span class="p">],[</span><span class="s1">'Comp'</span><span class="p">,</span><span class="s1">'Math'</span><span class="p">,</span><span class="s1">'Sci'</span><span class="p">]))</span>
<span class="gp">In [73]: </span><span class="n">headr</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">([</span><span class="s1">'Exams'</span><span class="p">,</span><span class="s1">'Labs'</span><span class="p">],[</span><span class="s1">'I'</span><span class="p">,</span><span class="s1">'II'</span><span class="p">]))</span>
<span class="gp">In [74]: </span><span class="n">indx</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'Student'</span><span class="p">,</span><span class="s1">'Course'</span><span class="p">])</span>
<span class="gp">In [75]: </span><span class="n">cols</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="n">headr</span><span class="p">)</span> <span class="c1">#Notice these are un-named</span>
<span class="gp">In [76]: </span><span class="n">data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">70</span><span class="o">+</span><span class="n">x</span><span class="o">+</span><span class="n">y</span><span class="o">+</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="n">y</span><span class="p">)</span><span class="o">%</span><span class="mi">3</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">4</span><span class="p">)]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">9</span><span class="p">)]</span>
<span class="gp">In [77]: </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">data</span><span class="p">,</span><span class="n">indx</span><span class="p">,</span><span class="n">cols</span><span class="p">);</span> <span class="n">df</span>
<span class="gr">Out[77]: </span>
<span class="go"> Exams Labs </span>
<span class="go"> I II I II</span>
<span class="go">Student Course </span>
<span class="go">Ada Comp 70 71 72 73</span>
<span class="go"> Math 71 73 75 74</span>
<span class="go"> Sci 72 75 75 75</span>
<span class="go">Quinn Comp 73 74 75 76</span>
<span class="go"> Math 74 76 78 77</span>
<span class="go"> Sci 75 78 78 78</span>
<span class="go">Violet Comp 76 77 78 79</span>
<span class="go"> Math 77 79 81 80</span>
<span class="go"> Sci 78 81 81 81</span>
<span class="gp">In [78]: </span><span class="n">All</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="bp">None</span><span class="p">)</span>
<span class="gp">In [79]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'Violet'</span><span class="p">]</span>
<span class="gr">Out[79]: </span>
<span class="go"> Exams Labs </span>
<span class="go"> I II I II</span>
<span class="go">Course </span>
<span class="go">Comp 76 77 78 79</span>
<span class="go">Math 77 79 81 80</span>
<span class="go">Sci 78 81 81 81</span>
<span class="gp">In [80]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">All</span><span class="p">,</span><span class="s1">'Math'</span><span class="p">),</span><span class="n">All</span><span class="p">]</span>
<span class="gr">Out[80]: </span>
<span class="go"> Exams Labs </span>
<span class="go"> I II I II</span>
<span class="go">Student Course </span>
<span class="go">Ada Math 71 73 75 74</span>
<span class="go">Quinn Math 74 76 78 77</span>
<span class="go">Violet Math 77 79 81 80</span>
<span class="gp">In [81]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="nb">slice</span><span class="p">(</span><span class="s1">'Ada'</span><span class="p">,</span><span class="s1">'Quinn'</span><span class="p">),</span><span class="s1">'Math'</span><span class="p">),</span><span class="n">All</span><span class="p">]</span>
<span class="gr">Out[81]: </span>
<span class="go"> Exams Labs </span>
<span class="go"> I II I II</span>
<span class="go">Student Course </span>
<span class="go">Ada Math 71 73 75 74</span>
<span class="go">Quinn Math 74 76 78 77</span>
<span class="gp">In [82]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">All</span><span class="p">,</span><span class="s1">'Math'</span><span class="p">),(</span><span class="s1">'Exams'</span><span class="p">)]</span>
<span class="gr">Out[82]: </span>
<span class="go"> I II</span>
<span class="go">Student Course </span>
<span class="go">Ada Math 71 73</span>
<span class="go">Quinn Math 74 76</span>
<span class="go">Violet Math 77 79</span>
<span class="gp">In [83]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">All</span><span class="p">,</span><span class="s1">'Math'</span><span class="p">),(</span><span class="n">All</span><span class="p">,</span><span class="s1">'II'</span><span class="p">)]</span>
<span class="gr">Out[83]: </span>
<span class="go"> Exams Labs</span>
<span class="go"> II II</span>
<span class="go">Student Course </span>
<span class="go">Ada Math 73 74</span>
<span class="go">Quinn Math 76 77</span>
<span class="go">Violet Math 79 80</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-144"><a class="reference external" href="http://stackoverflow.com/questions/19319432/pandas-selecting-a-lower-level-in-a-dataframe-to-do-a-ffill">使用xs</a>设置多索引的部分</span></p>
</div>
<div class="section" id="sorting">
<h3><span class="yiyi-st" id="yiyi-145">Sorting</span></h3>
<p><span class="yiyi-st" id="yiyi-146"><a class="reference external" href="http://stackoverflow.com/questions/14733871/mutli-index-sorting-in-pandas">按特定列或使用多索引</a>的列的有序列表排序</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [84]: </span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">(</span><span class="s1">'Labs'</span><span class="p">,</span> <span class="s1">'II'</span><span class="p">),</span> <span class="n">ascending</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="gr">Out[84]: </span>
<span class="go"> Exams Labs </span>
<span class="go"> I II I II</span>
<span class="go">Student Course </span>
<span class="go">Violet Sci 78 81 81 81</span>
<span class="go"> Math 77 79 81 80</span>
<span class="go"> Comp 76 77 78 79</span>
<span class="go">Quinn Sci 75 78 78 78</span>
<span class="go"> Math 74 76 78 77</span>
<span class="go"> Comp 73 74 75 76</span>
<span class="go">Ada Sci 72 75 75 75</span>
<span class="go"> Math 71 73 75 74</span>
<span class="go"> Comp 70 71 72 73</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-147"><a class="reference external" href="https://github.com/pandas-dev/pandas/issues/2995">部分选择，需要排序；</a></span></p>
</div>
<div class="section" id="levels">
<h3><span class="yiyi-st" id="yiyi-148">Levels</span></h3>
<p><span class="yiyi-st" id="yiyi-149"><a class="reference external" href="http://stackoverflow.com/questions/14744068/prepend-a-level-to-a-pandas-multiindex">将关卡预置到多索引</a></span></p>
<p><span class="yiyi-st" id="yiyi-150"><a class="reference external" href="http://stackoverflow.com/questions/14507794/python-pandas-how-to-flatten-a-hierarchical-index-in-columns">展平分层列</a></span></p>
</div>
<div class="section" id="panelnd">
<h3><span class="yiyi-st" id="yiyi-151">panelnd</span></h3>
<p><span class="yiyi-st" id="yiyi-152"><a class="reference internal" href="dsintro.html#dsintro-panelnd"><span class="std std-ref">panelnd</span></a>文档。</span></p>
<p><span class="yiyi-st" id="yiyi-153"><a class="reference external" href="http://stackoverflow.com/questions/18748598/why-my-panelnd-factory-throwing-a-keyerror">构建5D面板</a></span></p>
</div>
</div>
<div class="section" id="missing-data">
<span id="cookbook-missing-data"></span><h2><span class="yiyi-st" id="yiyi-154">Missing Data</span></h2>
<p><span class="yiyi-st" id="yiyi-155"><a class="reference internal" href="missing_data.html#missing-data"><span class="std std-ref">missing data</span></a>文档。</span></p>
<p><span class="yiyi-st" id="yiyi-156">填充反向时间序列</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [85]: </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">6</span><span class="p">,</span><span class="mi">1</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">'2013-08-01'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'B'</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">'A'</span><span class="p">))</span>
<span class="gp">In [86]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="gp">In [87]: </span><span class="n">df</span>
<span class="gr">Out[87]: </span>
<span class="go"> A</span>
<span class="go">2013-08-01 -1.054874</span>
<span class="go">2013-08-02 -0.179642</span>
<span class="go">2013-08-05 0.639589</span>
<span class="go">2013-08-06 NaN</span>
<span class="go">2013-08-07 1.906684</span>
<span class="go">2013-08-08 0.104050</span>
<span class="gp">In [88]: </span><span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">ffill</span><span class="p">()</span>
<span class="gr">Out[88]: </span>
<span class="go"> A</span>
<span class="go">2013-08-08 0.104050</span>
<span class="go">2013-08-07 1.906684</span>
<span class="go">2013-08-06 1.906684</span>
<span class="go">2013-08-05 0.639589</span>
<span class="go">2013-08-02 -0.179642</span>
<span class="go">2013-08-01 -1.054874</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-157"><a class="reference external" href="http://stackoverflow.com/questions/18196811/cumsum-reset-at-nan">在NaN值</a>的累计复位</span></p>
<div class="section" id="replace">
<h3><span class="yiyi-st" id="yiyi-158">Replace</span></h3>
<p><span class="yiyi-st" id="yiyi-159"><a class="reference external" href="http://stackoverflow.com/questions/16818871/extracting-value-and-creating-new-column-out-of-it">使用替换为backrefs</a></span></p>
</div>
</div>
<div class="section" id="grouping">
<span id="cookbook-grouping"></span><h2><span class="yiyi-st" id="yiyi-160">Grouping</span></h2>
<p><span class="yiyi-st" id="yiyi-161"><a class="reference internal" href="groupby.html#groupby"><span class="std std-ref">grouping</span></a>文档。</span></p>
<p><span class="yiyi-st" id="yiyi-162"><a class="reference external" href="http://stackoverflow.com/questions/15322632/python-pandas-df-groupy-agg-column-reference-in-agg">应用</a>的基本分组</span></p>
<p><span class="yiyi-st" id="yiyi-163">与agg不同，apply的callable是通过一个子DataFrame传递给你访问的所有的列</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [89]: </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">'animal'</span><span class="p">:</span> <span class="s1">'cat dog cat fish dog cat cat'</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span>
<span class="gp"> ....:</span> <span class="s1">'size'</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="s1">'SSMMMLL'</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">8</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">],</span>
<span class="gp"> ....:</span> <span class="s1">'adult'</span> <span class="p">:</span> <span class="p">[</span><span class="bp">False</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">+</span> <span class="p">[</span><span class="bp">True</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span><span class="p">});</span> <span class="n">df</span>
<span class="gp"> ....:</span>
<span class="gr">Out[89]: </span>
<span class="go"> adult animal size weight</span>
<span class="go">0 False cat S 8</span>
<span class="go">1 False dog S 10</span>
<span class="go">2 False cat M 11</span>
<span class="go">3 False fish M 1</span>
<span class="go">4 False dog M 20</span>
<span class="go">5 True cat L 12</span>
<span class="go">6 True cat L 12</span>
<span class="c">#List the size of the animals with the highest weight.</span>
<span class="gp">In [90]: </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="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">subf</span><span class="p">:</span> <span class="n">subf</span><span class="p">[</span><span class="s1">'size'</span><span class="p">][</span><span class="n">subf</span><span class="p">[</span><span class="s1">'weight'</span><span class="p">]</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()])</span>
<span class="gr">Out[90]: </span>
<span class="go">animal</span>
<span class="go">cat L</span>
<span class="go">dog M</span>
<span class="go">fish M</span>
<span class="go">dtype: object</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-164"><a class="reference external" href="http://stackoverflow.com/questions/14734533/how-to-access-pandas-groupby-dataframe-by-key">使用get_group</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [91]: </span><span class="n">gb</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">'animal'</span><span class="p">])</span>
<span class="gp">In [92]: </span><span class="n">gb</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="s1">'cat'</span><span class="p">)</span>
<span class="gr">Out[92]: </span>
<span class="go"> adult animal size weight</span>
<span class="go">0 False cat S 8</span>
<span class="go">2 False cat M 11</span>
<span class="go">5 True cat L 12</span>
<span class="go">6 True cat L 12</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-165"><a class="reference external" href="http://stackoverflow.com/questions/15262134/apply-different-functions-to-different-items-in-group-object-python-pandas">套用至群组中的其他项目</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [93]: </span><span class="k">def</span> <span class="nf">GrowUp</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp"> ....:</span> <span class="n">avg_weight</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="s1">'size'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'S'</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span> <span class="o">*</span> <span class="mf">1.5</span><span class="p">)</span>
<span class="gp"> ....:</span> <span class="n">avg_weight</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="s1">'size'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'M'</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span> <span class="o">*</span> <span class="mf">1.25</span><span class="p">)</span>
<span class="gp"> ....:</span> <span class="n">avg_weight</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="s1">'size'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'L'</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
<span class="gp"> ....:</span> <span class="n">avg_weight</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp"> ....:</span> <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">'L'</span><span class="p">,</span><span class="n">avg_weight</span><span class="p">,</span><span class="bp">True</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'size'</span><span class="p">,</span> <span class="s1">'weight'</span><span class="p">,</span> <span class="s1">'adult'</span><span class="p">])</span>
<span class="gp"> ....:</span>
<span class="gp">In [94]: </span><span class="n">expected_df</span> <span class="o">=</span> <span class="n">gb</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">GrowUp</span><span class="p">)</span>
<span class="gp">In [95]: </span><span class="n">expected_df</span>
<span class="gr">Out[95]: </span>
<span class="go"> size weight adult</span>
<span class="go">animal </span>
<span class="go">cat L 12.4375 True</span>
<span class="go">dog L 20.0000 True</span>
<span class="go">fish L 1.2500 True</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-166"><a class="reference external" href="http://stackoverflow.com/questions/14542145/reductions-down-a-column-in-pandas">扩展应用</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [96]: </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">i</span> <span class="o">/</span> <span class="mf">100.0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">11</span><span class="p">)])</span>
<span class="gp">In [97]: </span><span class="k">def</span> <span class="nf">CumRet</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
<span class="gp"> ....:</span> <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span>
<span class="gp"> ....:</span>
<span class="gp">In [98]: </span><span class="k">def</span> <span class="nf">Red</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp"> ....:</span> <span class="k">return</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">CumRet</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
<span class="gp"> ....:</span>
<span class="gp">In [99]: </span><span class="n">S</span><span class="o">.</span><span class="n">expanding</span><span class="p">()</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">Red</span><span class="p">)</span>
<span class="gr">Out[99]: </span>
<span class="go">0 1.010000</span>
<span class="go">1 1.030200</span>
<span class="go">2 1.061106</span>
<span class="go">3 1.103550</span>
<span class="go">4 1.158728</span>
<span class="go">5 1.228251</span>
<span class="go">6 1.314229</span>
<span class="go">7 1.419367</span>
<span class="go">8 1.547110</span>
<span class="go">9 1.701821</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-167"><a class="reference external" href="http://stackoverflow.com/questions/14760757/replacing-values-with-groupby-means">用组的其余部分的平均值替换某些值</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [100]: </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="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="s1">'B'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
<span class="gp">In [101]: </span><span class="n">gb</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">'A'</span><span class="p">)</span>
<span class="gp">In [102]: </span><span class="k">def</span> <span class="nf">replace</span><span class="p">(</span><span class="n">g</span><span class="p">):</span>
<span class="gp"> .....:</span> <span class="n">mask</span> <span class="o">=</span> <span class="n">g</span> <span class="o"><</span> <span class="mi">0</span>
<span class="gp"> .....:</span> <span class="n">g</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span> <span class="o">=</span> <span class="n">g</span><span class="p">[</span><span class="o">~</span><span class="n">mask</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="gp"> .....:</span> <span class="k">return</span> <span class="n">g</span>
<span class="gp"> .....:</span>
<span class="gp">In [103]: </span><span class="n">gb</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">replace</span><span class="p">)</span>
<span class="gr">Out[103]: </span>
<span class="go"> B</span>
<span class="go">0 1.0</span>
<span class="go">1 1.0</span>
<span class="go">2 1.0</span>
<span class="go">3 2.0</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-168"><a class="reference external" href="http://stackoverflow.com/questions/14941366/pandas-sort-by-group-aggregate-and-column">按聚合数据对组排序</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [104]: </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">'code'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'foo'</span><span class="p">,</span> <span class="s1">'bar'</span><span class="p">,</span> <span class="s1">'baz'</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span>
<span class="gp"> .....:</span> <span class="s1">'data'</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.16</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.21</span><span class="p">,</span> <span class="mf">0.33</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.59</span><span class="p">,</span> <span class="mf">0.62</span><span class="p">],</span>
<span class="gp"> .....:</span> <span class="s1">'flag'</span><span class="p">:</span> <span class="p">[</span><span class="bp">False</span><span class="p">,</span> <span class="bp">True</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">})</span>
<span class="gp"> .....:</span>
<span class="gp">In [105]: </span><span class="n">code_groups</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">'code'</span><span class="p">)</span>
<span class="gp">In [106]: </span><span class="n">agg_n_sort_order</span> <span class="o">=</span> <span class="n">code_groups</span><span class="p">[[</span><span class="s1">'data'</span><span class="p">]]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="nb">sum</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">In [107]: </span><span class="n">sorted_df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">agg_n_sort_order</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
<span class="gp">In [108]: </span><span class="n">sorted_df</span>
<span class="gr">Out[108]: </span>
<span class="go"> code data flag</span>
<span class="go">1 bar -0.21 True</span>
<span class="go">4 bar -0.59 False</span>
<span class="go">0 foo 0.16 False</span>
<span class="go">3 foo 0.45 True</span>
<span class="go">2 baz 0.33 False</span>
<span class="go">5 baz 0.62 True</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-169"><a class="reference external" href="http://stackoverflow.com/questions/14897100/create-multiple-columns-in-pandas-aggregation-function">创建多个聚合列</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [109]: </span><span class="n">rng</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="n">start</span><span class="o">=</span><span class="s2">"2014-10-07"</span><span class="p">,</span><span class="n">periods</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span><span class="n">freq</span><span class="o">=</span><span class="s1">'2min'</span><span class="p">)</span>
<span class="gp">In [110]: </span><span class="n">ts</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">data</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">10</span><span class="p">)),</span> <span class="n">index</span> <span class="o">=</span> <span class="n">rng</span><span class="p">)</span>
<span class="gp">In [111]: </span><span class="k">def</span> <span class="nf">MyCust</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp"> .....:</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span>
<span class="gp"> .....:</span> <span class="k">return</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mf">1.234</span>
<span class="gp"> .....:</span> <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">NaT</span>
<span class="gp"> .....:</span>
<span class="gp">In [112]: </span><span class="n">mhc</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Mean'</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="s1">'Max'</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">,</span> <span class="s1">'Custom'</span> <span class="p">:</span> <span class="n">MyCust</span><span class="p">}</span>
<span class="gp">In [113]: </span><span class="n">ts</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s2">"5min"</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">mhc</span><span class="p">)</span>
<span class="gr">Out[113]: </span>
<span class="go"> Max Custom Mean</span>
<span class="go">2014-10-07 00:00:00 2 1.234 1.0</span>
<span class="go">2014-10-07 00:05:00 4 NaT 3.5</span>
<span class="go">2014-10-07 00:10:00 7 7.404 6.0</span>
<span class="go">2014-10-07 00:15:00 9 NaT 8.5</span>
<span class="gp">In [114]: </span><span class="n">ts</span>
<span class="gr">Out[114]: </span>
<span class="go">2014-10-07 00:00:00 0</span>
<span class="go">2014-10-07 00:02:00 1</span>
<span class="go">2014-10-07 00:04:00 2</span>
<span class="go">2014-10-07 00:06:00 3</span>
<span class="go">2014-10-07 00:08:00 4</span>
<span class="go">2014-10-07 00:10:00 5</span>
<span class="go">2014-10-07 00:12:00 6</span>
<span class="go">2014-10-07 00:14:00 7</span>
<span class="go">2014-10-07 00:16:00 8</span>
<span class="go">2014-10-07 00:18:00 9</span>
<span class="go">Freq: 2T, dtype: int64</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-170"><a class="reference external" href="http://stackoverflow.com/questions/17709270/i-want-to-create-a-column-of-value-counts-in-my-pandas-dataframe">创建值计数列，并重新分配回DataFrame</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [115]: </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">'Color'</span><span class="p">:</span> <span class="s1">'Red Red Red Blue'</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span>
<span class="gp"> .....:</span> <span class="s1">'Value'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">]});</span> <span class="n">df</span>
<span class="gp"> .....:</span>
<span class="gr">Out[115]: </span>
<span class="go"> Color Value</span>
<span class="go">0 Red 100</span>
<span class="go">1 Red 150</span>
<span class="go">2 Red 50</span>
<span class="go">3 Blue 50</span>
<span class="gp">In [116]: </span><span class="n">df</span><span class="p">[</span><span class="s1">'Counts'</span><span class="p">]</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">'Color'</span><span class="p">])</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="nb">len</span><span class="p">)</span>
<span class="gp">In [117]: </span><span class="n">df</span>
<span class="gr">Out[117]: </span>
<span class="go"> Color Value Counts</span>
<span class="go">0 Red 100 3</span>
<span class="go">1 Red 150 3</span>
<span class="go">2 Red 50 3</span>
<span class="go">3 Blue 50 1</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-171"><a class="reference external" href="http://stackoverflow.com/q/23198053/190597">根据索引</a>对列中的值进行移位</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [118]: </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="p">{</span><span class="s1">u'line_race'</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</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="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span>
<span class="gp"> .....:</span> <span class="s1">u'beyer'</span><span class="p">:</span> <span class="p">[</span><span class="mi">99</span><span class="p">,</span> <span class="mi">102</span><span class="p">,</span> <span class="mi">103</span><span class="p">,</span> <span class="mi">103</span><span class="p">,</span> <span class="mi">88</span><span class="p">,</span> <span class="mi">100</span><span class="p">]},</span>
<span class="gp"> .....:</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">u'Last Gunfighter'</span><span class="p">,</span> <span class="s1">u'Last Gunfighter'</span><span class="p">,</span> <span class="s1">u'Last Gunfighter'</span><span class="p">,</span>
<span class="gp"> .....:</span> <span class="s1">u'Paynter'</span><span class="p">,</span> <span class="s1">u'Paynter'</span><span class="p">,</span> <span class="s1">u'Paynter'</span><span class="p">]);</span> <span class="n">df</span>
<span class="gp"> .....:</span>
<span class="gr">Out[118]: </span>
<span class="go"> beyer line_race</span>
<span class="go">Last Gunfighter 99 10</span>
<span class="go">Last Gunfighter 102 10</span>
<span class="go">Last Gunfighter 103 8</span>
<span class="go">Paynter 103 10</span>
<span class="go">Paynter 88 10</span>
<span class="go">Paynter 100 8</span>
<span class="gp">In [119]: </span><span class="n">df</span><span class="p">[</span><span class="s1">'beyer_shifted'</span><span class="p">]</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="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="s1">'beyer'</span><span class="p">]</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">In [120]: </span><span class="n">df</span>
<span class="gr">Out[120]: </span>
<span class="go"> beyer line_race beyer_shifted</span>
<span class="go">Last Gunfighter 99 10 NaN</span>
<span class="go">Last Gunfighter 102 10 99.0</span>
<span class="go">Last Gunfighter 103 8 102.0</span>
<span class="go">Paynter 103 10 NaN</span>
<span class="go">Paynter 88 10 103.0</span>
<span class="go">Paynter 100 8 88.0</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-172"><a class="reference external" href="http://stackoverflow.com/q/26701849/190597">从每个组</a>中选择具有最大值的行</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [121]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'host'</span><span class="p">:[</span><span class="s1">'other'</span><span class="p">,</span><span class="s1">'other'</span><span class="p">,</span><span class="s1">'that'</span><span class="p">,</span><span class="s1">'this'</span><span class="p">,</span><span class="s1">'this'</span><span class="p">],</span>
<span class="gp"> .....:</span> <span class="s1">'service'</span><span class="p">:[</span><span class="s1">'mail'</span><span class="p">,</span><span class="s1">'web'</span><span class="p">,</span><span class="s1">'mail'</span><span class="p">,</span><span class="s1">'mail'</span><span class="p">,</span><span class="s1">'web'</span><span class="p">],</span>
<span class="gp"> .....:</span> <span class="s1">'no'</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]})</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s1">'host'</span><span class="p">,</span> <span class="s1">'service'</span><span class="p">])</span>
<span class="gp"> .....:</span>
<span class="gp">In [122]: </span><span class="n">mask</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="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="s1">'idxmax'</span><span class="p">)</span>
<span class="gp">In [123]: </span><span class="n">df_count</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">mask</span><span class="p">[</span><span class="s1">'no'</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="gp">In [124]: </span><span class="n">df_count</span>
<span class="gr">Out[124]: </span>
<span class="go"> host service no</span>
<span class="go">0 other web 2</span>
<span class="go">1 that mail 1</span>
<span class="go">2 this mail 2</span>
</pre></div>
</div>
<p><span class="yiyi-st" id="yiyi-173"><a class="reference external" href="http://stackoverflow.com/q/29142487/846892">像Python的itertools.groupby</a>分组</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [125]: </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="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</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="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">])</span>
<span class="gp">In [126]: </span><span class="n">df</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">groupby</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">A</span><span class="o">.</span><span class="n">shift</span><span class="p">())</span><span class="o">.</span><span class="n">cumsum</span><span class="p">())</span><span class="o">.</span><span class="n">groups</span>
<span class="gr">Out[126]: </span>
<span class="go">{1: Int64Index([0], dtype='int64'),</span>
<span class="go"> 2: Int64Index([1], dtype='int64'),</span>
<span class="go"> 3: Int64Index([2], dtype='int64'),</span>
<span class="go"> 4: Int64Index([3, 4, 5], dtype='int64'),</span>
<span class="go"> 5: Int64Index([6], dtype='int64'),</span>
<span class="go"> 6: Int64Index([7, 8], dtype='int64')}</span>
<span class="gp">In [127]: </span><span class="n">df</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">groupby</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">A</span><span class="o">.</span><span class="n">shift</span><span class="p">())</span><span class="o">.</span><span class="n">cumsum</span><span class="p">())</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
<span class="gr">Out[127]: </span>
<span class="go">0 0</span>
<span class="go">1 1</span>
<span class="go">2 0</span>
<span class="go">3 1</span>
<span class="go">4 2</span>
<span class="go">5 3</span>
<span class="go">6 0</span>
<span class="go">7 1</span>
<span class="go">8 2</span>
<span class="go">Name: A, dtype: int64</span>
</pre></div>
</div>
<div class="section" id="expanding-data">
<h3><span class="yiyi-st" id="yiyi-174">Expanding Data</span></h3>
<p><span class="yiyi-st" id="yiyi-175"><a class="reference external" href="http://stackoverflow.com/questions/15489011/python-time-series-alignment-and-to-date-functions">对齐和最新</a></span></p>
<p><span class="yiyi-st" id="yiyi-176"><a class="reference external" href="http://stackoverflow.com/questions/14300768/pandas-rolling-computation-with-window-based-on-values-instead-of-counts">基于值而不是计数</a>滚动计算窗口</span></p>
<p><span class="yiyi-st" id="yiyi-177"><a class="reference external" href="http://stackoverflow.com/questions/15771472/pandas-rolling-mean-by-time-interval">按时间间隔滚动平均值</a></span></p>
</div>
<div class="section" id="id2">
<h3><span class="yiyi-st" id="yiyi-178">Splitting</span></h3>
<p><span class="yiyi-st" id="yiyi-179"><a class="reference external" href="http://stackoverflow.com/questions/13353233/best-way-to-split-a-dataframe-given-an-edge/15449992#15449992">拆分帧</a></span></p>
<p><span class="yiyi-st" id="yiyi-180">创建数据框列表，使用基于行中包含的逻辑的划分进行拆分。</span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [128]: </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">data</span><span class="o">=</span><span class="p">{</span><span class="s1">'Case'</span> <span class="p">:</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">'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">'A'</span><span class="p">,</span><span class="s1">'A'</span><span class="p">],</span>
<span class="gp"> .....:</span> <span class="s1">'Data'</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">9</span><span class="p">)})</span>
<span class="gp"> .....:</span>
<span class="gp">In [129]: </span><span class="n">dfs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</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="mi">1</span><span class="o">*</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Case'</span><span class="p">]</span><span class="o">==</span><span class="s1">'B'</span><span class="p">))</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span><span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">median</span><span class="p">())))[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="gp">In [130]: </span><span class="n">dfs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="gr">Out[130]: </span>
<span class="go"> Case Data</span>
<span class="go">0 A 0.174068</span>
<span class="go">1 A -0.439461</span>
<span class="go">2 A -0.741343</span>
<span class="go">3 B -0.079673</span>
<span class="gp">In [131]: </span><span class="n">dfs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="gr">Out[131]: </span>
<span class="go"> Case Data</span>
<span class="go">4 A -0.922875</span>
<span class="go">5 A 0.303638</span>
<span class="go">6 B -0.917368</span>
<span class="gp">In [132]: </span><span class="n">dfs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="gr">Out[132]: </span>
<span class="go"> Case Data</span>
<span class="go">7 A -1.624062</span>
<span class="go">8 A -0.758514</span>
</pre></div>
</div>
</div>
<div class="section" id="pivot">
<span id="cookbook-pivot"></span><h3><span class="yiyi-st" id="yiyi-181">Pivot</span></h3>
<p><span class="yiyi-st" id="yiyi-182"><a class="reference internal" href="reshaping.html#reshaping-pivot"><span class="std std-ref">Pivot</span></a>文档。</span></p>
<p><span class="yiyi-st" id="yiyi-183"><a class="reference external" href="http://stackoverflow.com/questions/15570099/pandas-pivot-tables-row-subtotals/15574875#15574875">部分和和小计</a></span></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [133]: </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">data</span><span class="o">=</span><span class="p">{</span><span class="s1">'Province'</span> <span class="p">:</span> <span class="p">[</span><span class="s1">'ON'</span><span class="p">,</span><span class="s1">'QC'</span><span class="p">,</span><span class="s1">'BC'</span><span class="p">,</span><span class="s1">'AL'</span><span class="p">,</span><span class="s1">'AL'</span><span class="p">,</span><span class="s1">'MN'</span><span class="p">,</span><span class="s1">'ON'</span><span class="p">],</span>
<span class="gp"> .....:</span> <span class="s1">'City'</span> <span class="p">:</span> <span class="p">[</span><span class="s1">'Toronto'</span><span class="p">,</span><span class="s1">'Montreal'</span><span class="p">,</span><span class="s1">'Vancouver'</span><span class="p">,</span><span class="s1">'Calgary'</span><span class="p">,</span><span class="s1">'Edmonton'</span><span class="p">,</span><span class="s1">'Winnipeg'</span><span class="p">,</span><span class="s1">'Windsor'</span><span class="p">],</span>
<span class="gp"> .....:</span> <span class="s1">'Sales'</span> <span class="p">:</span> <span class="p">[</span><span class="mi">13</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">16</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="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">]})</span>
<span class="gp"> .....:</span>