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2018-11-12-panda使用.html
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<h2 id="数据结构之Series"><a href="#数据结构之Series" class="headerlink" title="数据结构之Series"></a>数据结构之Series</h2><figure class="highlight clean"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">from</span> <span class="keyword">import</span> Series,DataFrame </span><br><span class="line">Series是类似于一维数组的对象，由一组数据以及索引组成</span><br><span class="line"></span><br><span class="line">#Series <span class="number">1</span>-给定了索引</span><br><span class="line">a= Series([<span class="number">4</span>,<span class="number">7</span>,<span class="number">-5</span>,<span class="number">3</span>],index=[<span class="string">'d'</span>,<span class="string">'b'</span>,<span class="string">'a'</span>,<span class="string">'c'</span>])</span><br><span class="line">b= pd.Series([<span class="number">4</span>,<span class="number">7</span>,<span class="number">-5</span>,<span class="number">3</span>],index=[<span class="string">'d'</span>,<span class="string">'b'</span>,<span class="string">'a'</span>,<span class="string">'c'</span>])</span><br><span class="line">#<span class="number">2</span>-未给定索引，索引为[<span class="number">0</span>,N<span class="number">-1</span>]</span><br><span class="line">ser1 = Series([<span class="number">1</span>,<span class="number">2</span>,<span class="number">3</span>,<span class="number">4</span>])</span><br><span class="line">#给index指定一个list</span><br><span class="line">ser2 = Series(range(<span class="number">4</span>),index = [<span class="string">"a"</span>,<span class="string">"b"</span>,<span class="string">"c"</span>,<span class="string">"d"</span>])</span><br><span class="line">#<span class="number">3</span>-也可以通过字典来创建Series对象</span><br><span class="line">sdata = {<span class="string">'Ohio'</span>: <span class="number">35000</span>, <span class="string">'Texas'</span>: <span class="number">71000</span>, <span class="string">'Oregon'</span>: <span class="number">16000</span>, <span class="string">'Utah'</span>: <span class="number">5000</span>}</span><br><span class="line">ser3 = Series(sdata)</span><br><span class="line">#可以发现，用字典创建的Series是按index有序的</span><br><span class="line">#在用字典生成Series的时候，也可以指定索引，</span><br><span class="line">#当索引中值对应的字典中的值不存在的时候，则此索引的值标记为Missing，NA，</span><br><span class="line">#并且可以通过函数（pandas.isnull，pandas.notnull）来确定哪些索引对应的值是没有的。</span><br><span class="line">states = [<span class="string">'California'</span>, <span class="string">'Ohio'</span>, <span class="string">'Oregon'</span>, <span class="string">'Texas'</span>]</span><br><span class="line">ser3 = Series(sdata,index = states)</span><br><span class="line">In [<span class="number">50</span>]: ser3</span><br><span class="line">Out[<span class="number">50</span>]: </span><br><span class="line">California NaN</span><br><span class="line">Ohio <span class="number">35000.0</span></span><br><span class="line">Oregon <span class="number">16000.0</span></span><br><span class="line">Texas <span class="number">71000.0</span></span><br><span class="line">dtype: float64</span><br><span class="line"># 判断哪些值为空</span><br><span class="line">In [<span class="number">51</span>]: pd.isnull(ser3)</span><br><span class="line">Out[<span class="number">51</span>]: </span><br><span class="line">California <span class="literal">True</span></span><br><span class="line">Ohio <span class="literal">False</span></span><br><span class="line">Oregon <span class="literal">False</span></span><br><span class="line">Texas <span class="literal">False</span></span><br><span class="line">dtype: bool</span><br><span class="line">In [<span class="number">52</span>]: pd.notnull(ser3)</span><br><span class="line">Out[<span class="number">52</span>]: </span><br><span class="line">California <span class="literal">False</span></span><br><span class="line">Ohio <span class="literal">True</span></span><br><span class="line">Oregon <span class="literal">True</span></span><br><span class="line">Texas <span class="literal">True</span></span><br><span class="line">dtype: bool</span><br></pre></td></tr></table></figure>
<h2 id="访问Series中的元素和索引"><a href="#访问Series中的元素和索引" class="headerlink" title="访问Series中的元素和索引"></a>访问Series中的元素和索引</h2><figure class="highlight prolog"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br></pre></td><td class="code"><pre><span class="line">se2</span><br><span class="line"><span class="symbol">Out</span>[<span class="number">159</span>]: </span><br><span class="line">a <span class="number">0</span></span><br><span class="line">b <span class="number">1</span></span><br><span class="line">c <span class="number">2</span></span><br><span class="line">d <span class="number">3</span></span><br><span class="line">dtype: int64</span><br><span class="line"># 访问索引为<span class="string">"a"</span>,<span class="string">"c"</span>的元素</span><br><span class="line">se2[[<span class="string">"a"</span>,<span class="string">"c"</span>]]</span><br><span class="line"><span class="symbol">Out</span>[<span class="number">160</span>]: </span><br><span class="line">a <span class="number">0</span></span><br><span class="line">c <span class="number">2</span></span><br><span class="line">dtype: int64</span><br><span class="line"># 访问索引为<span class="string">"a"</span>的元素</span><br><span class="line">se2[<span class="string">"a"</span>]</span><br><span class="line"><span class="symbol">Out</span>[<span class="number">161</span>]: <span class="number">0</span></span><br><span class="line"># 获取所有的值</span><br><span class="line">se2.values</span><br><span class="line"><span class="symbol">Out</span>[<span class="number">162</span>]: array([<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>], dtype=int64)</span><br><span class="line"># 获取所有的索引</span><br><span class="line">se2.index</span><br><span class="line"><span class="symbol">Out</span>[<span class="number">163</span>]: <span class="symbol">Index</span>([<span class="string">'a'</span>, <span class="string">'b'</span>, <span class="string">'c'</span>, <span class="string">'d'</span>], dtype=<span class="string">'object'</span>)</span><br></pre></td></tr></table></figure>
<h2 id="简单运算"><a href="#简单运算" class="headerlink" title="简单运算"></a>简单运算</h2><p>在pandas的Series中，会保留NumPy的数组操作（用布尔数组过滤数据，标量乘法，以及使用数学函数），并同时保持引用的使用.<br><figure class="highlight css"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><span class="line"><span class="selector-tag">se2</span><span class="selector-attr">[se2>2]</span></span><br><span class="line"><span class="selector-tag">Out</span><span class="selector-attr">[165]</span>: </span><br><span class="line"><span class="selector-tag">d</span> 3</span><br><span class="line"><span class="selector-tag">dtype</span>: <span class="selector-tag">int64</span></span><br><span class="line"><span class="selector-tag">se2</span>*2</span><br><span class="line"><span class="selector-tag">Out</span><span class="selector-attr">[166]</span>: </span><br><span class="line"><span class="selector-tag">a</span> 0</span><br><span class="line"><span class="selector-tag">b</span> 2</span><br><span class="line"><span class="selector-tag">c</span> 4</span><br><span class="line"><span class="selector-tag">d</span> 6</span><br><span class="line"><span class="selector-tag">dtype</span>: <span class="selector-tag">int64</span></span><br><span class="line"></span><br><span class="line"><span class="selector-tag">np</span><span class="selector-class">.exp</span>(<span class="selector-tag">se2</span>)</span><br><span class="line"><span class="selector-tag">Out</span><span class="selector-attr">[167]</span>: </span><br><span class="line"><span class="selector-tag">a</span> 1<span class="selector-class">.000000</span></span><br><span class="line"><span class="selector-tag">b</span> 2<span class="selector-class">.718282</span></span><br><span class="line"><span class="selector-tag">c</span> 7<span class="selector-class">.389056</span></span><br><span class="line"><span class="selector-tag">d</span> 20<span class="selector-class">.085537</span></span><br><span class="line"><span class="selector-tag">dtype</span>: <span class="selector-tag">float64</span></span><br></pre></td></tr></table></figure></p>
<h2 id="DataFrame"><a href="#DataFrame" class="headerlink" title="DataFrame"></a>DataFrame</h2><p>DataFrame是一个表格型的数据结构 它含有一组有序的列，每列可以是不同的值类型（数值/字符串/布尔型值） 它有行/列索引，可看做由Series组成的字典（共同用一个索引）<br><figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">#DataFrame</span><br><span class="line">d = {<span class="string">'state'</span>:[<span class="string">'1'</span>,<span class="string">'2'</span>],<span class="string">'year'</span>:[<span class="string">'a'</span>,<span class="string">'b'</span>],<span class="string">'pop'</span>:[<span class="string">'x'</span>,<span class="string">'y'</span>]}</span><br><span class="line">frame = pd.DataFrame(d)</span><br><span class="line">frame</span><br><span class="line"> state year pop</span><br><span class="line"><span class="number">0</span> <span class="number">1</span> <span class="selector-tag">a</span> x</span><br><span class="line"><span class="number">1</span> <span class="number">2</span> <span class="selector-tag">b</span> y</span><br></pre></td></tr></table></figure></p>
<h2 id="DataFrame基本用法"><a href="#DataFrame基本用法" class="headerlink" title="DataFrame基本用法"></a>DataFrame基本用法</h2><h3 id="追加数据"><a href="#追加数据" class="headerlink" title="追加数据"></a>追加数据</h3><figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">frame2 =pd.DataFrame([[<span class="string">'z'</span>,<span class="string">'3'</span>,<span class="string">'c'</span>],[<span class="string">'x'</span>,<span class="string">'4'</span>,<span class="string">'d'</span>]],<span class="attribute">columns</span>=[<span class="string">'pop'</span>,<span class="string">'state'</span>,<span class="string">'year'</span>])</span><br><span class="line">frame</span><br><span class="line"> pop state year</span><br><span class="line"><span class="number">0</span> x <span class="number">1</span> a</span><br><span class="line"><span class="number">1</span> y <span class="number">2</span> b</span><br><span class="line"><span class="number">2</span> z <span class="number">3</span> c</span><br><span class="line"><span class="number">3</span> x <span class="number">4</span> d</span><br></pre></td></tr></table></figure>
<h3 id="拼接数据"><a href="#拼接数据" class="headerlink" title="拼接数据"></a>拼接数据</h3><figure class="highlight lsl"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">pd.concat([frame,frame2])</span><br><span class="line">frame</span><br><span class="line"> pop <span class="section">state</span> year</span><br><span class="line"><span class="number">0</span> x <span class="number">1</span> a</span><br><span class="line"><span class="number">1</span> y <span class="number">2</span> b</span><br><span class="line"><span class="number">0</span> z <span class="number">3</span> c</span><br><span class="line"><span class="number">1</span> x <span class="number">4</span> d</span><br></pre></td></tr></table></figure>
<h3 id="从csv导入数据"><a href="#从csv导入数据" class="headerlink" title="从csv导入数据"></a>从csv导入数据</h3><figure class="highlight haskell"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line"><span class="class"><span class="keyword">data</span> = pd.read_excel('<span class="type">D</span>:\\<span class="type">Users</span>\\<span class="title">zyb</span>\\<span class="type">Desktop</span>\\<span class="title">plan</span>.<span class="title">xlsx'</span>,<span class="title">header</span>=5)</span></span><br><span class="line"><span class="class"><span class="keyword">data</span></span></span><br><span class="line"><span class="type">Out</span>[<span class="number">189</span>]: </span><br><span class="line"> <span class="number">5</span> <span class="type">FR</span>接入数据库测试 <span class="number">1</span> <span class="type">Unnamed</span>: <span class="number">3</span></span><br><span class="line"><span class="number">0</span> <span class="number">6.0</span> 整体测试 <span class="number">1</span> <span class="type">NaN</span></span><br><span class="line"><span class="number">1</span> <span class="type">NaN</span> 总计 <span class="number">8</span> <span class="type">NaN</span></span><br><span class="line"></span><br><span class="line"><span class="class"><span class="keyword">type</span>(<span class="title">data</span>)</span></span><br><span class="line"><span class="type">Out</span>[<span class="number">190</span>]: pandas.core.frame.<span class="type">DataFrame</span></span><br></pre></td></tr></table></figure>
<h3 id="显示头尾几行"><a href="#显示头尾几行" class="headerlink" title="显示头尾几行"></a>显示头尾几行</h3><figure class="highlight haskell"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="class"><span class="keyword">data</span>.head()</span></span><br><span class="line"><span class="class"><span class="keyword">data</span>.tail()</span></span><br></pre></td></tr></table></figure>
<h3 id="显示列名-值"><a href="#显示列名-值" class="headerlink" title="显示列名/值"></a>显示列名/值</h3><figure class="highlight prolog"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">data.columns</span><br><span class="line"><span class="symbol">Out</span>[<span class="number">6</span>]: <span class="symbol">Index</span>([<span class="number">5</span>, <span class="string">'FR接入数据库测试'</span>, <span class="number">1</span>, <span class="string">'Unnamed: 3'</span>], dtype=<span class="string">'object'</span>)</span><br><span class="line"></span><br><span class="line">data.values</span><br><span class="line"><span class="symbol">Out</span>[<span class="number">7</span>]: </span><br><span class="line">array([[<span class="number">6.0</span>, <span class="string">'整体测试'</span>, <span class="number">1</span>, nan],</span><br><span class="line"> [nan, <span class="string">'总计'</span>, <span class="number">8</span>, nan]], dtype=object)</span><br></pre></td></tr></table></figure>
<h3 id="筛选、缺失值处理"><a href="#筛选、缺失值处理" class="headerlink" title="筛选、缺失值处理"></a>筛选、缺失值处理</h3><figure class="highlight kotlin"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line">#筛选行/列</span><br><span class="line"><span class="keyword">data</span>.iloc[<span class="number">3</span>:<span class="number">6</span>]</span><br><span class="line"><span class="keyword">data</span>.iloc[:,<span class="number">3</span>:<span class="number">6</span>]</span><br><span class="line"><span class="keyword">data</span>.loc[:,[<span class="string">"school"</span>, <span class="string">"age"</span>]]</span><br><span class="line"></span><br><span class="line">#条件筛选</span><br><span class="line"><span class="keyword">data</span>[<span class="keyword">data</span>[<span class="string">"G1"</span>]<<span class="number">10</span>]</span><br><span class="line"></span><br><span class="line">#缺失值处理</span><br><span class="line"><span class="keyword">data</span>.fillna(value=<span class="number">0</span>)</span><br><span class="line"><span class="keyword">data</span>.dropna(how=<span class="string">"any"</span>)</span><br><span class="line"><span class="keyword">data</span>.isnull()</span><br></pre></td></tr></table></figure>
<h3 id="排序"><a href="#排序" class="headerlink" title="排序"></a><strong>排序</strong></h3><figure class="highlight routeros"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#排序</span></span><br><span class="line">data.sort_values(<span class="string">"G1"</span>, <span class="attribute">ascending</span>=<span class="literal">False</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment">#统计并排序</span></span><br><span class="line"><span class="attribute">s</span>=pd.Series(data2.loc[:,<span class="string">"Medu"</span>])</span><br><span class="line"><span class="attribute">s</span>=s.value_counts()</span><br><span class="line"><span class="attribute">s</span>=s.sort_index(axis=0)</span><br></pre></td></tr></table></figure>
<h3 id="算术运算"><a href="#算术运算" class="headerlink" title="算术运算"></a><strong>算术运算</strong></h3><figure class="highlight applescript"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">count</span></span><br></pre></td></tr></table></figure>
<p>非NA值的数量</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">describe</span></span><br></pre></td></tr></table></figure>
<p>针对Series或各DataFrame列计算汇总统计</p>
<figure class="highlight arduino"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">min</span>,<span class="built_in">max</span></span><br></pre></td></tr></table></figure>
<p>计算最小值、最大值</p>
<figure class="highlight autohotkey"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">argmin,</span> argmax</span><br></pre></td></tr></table></figure>
<p>计算能够获取到最小值和最大值的索引位置（整数)</p>
<figure class="highlight autohotkey"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">idxmin,</span> idxmax</span><br></pre></td></tr></table></figure>
<p>计算能够获取到最小值和最大值的索引值</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">quantile</span></span><br></pre></td></tr></table></figure>
<p>计算样本的分位数（0到1）</p>
<figure class="highlight axapta"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">sum</span></span><br></pre></td></tr></table></figure>
<p>值的总和</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">mean</span></span><br></pre></td></tr></table></figure>
<p>值的平均数</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">media</span></span><br></pre></td></tr></table></figure>
<p>值的算术中位数（50%分位数)</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">mad</span></span><br></pre></td></tr></table></figure>
<p>根据平均值计算平均绝对离差</p>
<figure class="highlight maxima"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">var</span>, <span class="built_in">std</span></span><br></pre></td></tr></table></figure>
<p>样本值的方差、标准差</p>
<figure class="highlight excel"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">skew</span>, <span class="built_in">kurt</span></span><br></pre></td></tr></table></figure>
<p>样本值的偏度（三阶矩）、峰度（四阶矩）</p>
<figure class="highlight autohotkey"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">cumsum,</span> cumprod</span><br></pre></td></tr></table></figure>
<p>样本值的累计和/累计积</p>
<figure class="highlight autohotkey"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">cummin,</span> cummax</span><br></pre></td></tr></table></figure>
<p>样本值的累计最小、最大值</p>
<figure class="highlight ebnf"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attribute">diff</span></span><br></pre></td></tr></table></figure>
<p>计算一阶差分（对时间序列很有用)</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pct_change</span><br></pre></td></tr></table></figure>
<p>计算百分数变化</p>
<h3 id="groupby-统计、数据透视表"><a href="#groupby-统计、数据透视表" class="headerlink" title="groupby**统计、数据透视表**"></a><strong>groupby**</strong>统计、数据透视表**</h3><figure class="highlight prolog"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line">#groupby</span><br><span class="line">data.groupby([<span class="string">'sex'</span>, <span class="string">'studytime'</span>])[<span class="string">'G1'</span>].mean()</span><br><span class="line"></span><br><span class="line">group1 = data.groupby(<span class="string">'sex'</span>)</span><br><span class="line">group1[<span class="string">'G1'</span>,<span class="string">'G2'</span>].agg([<span class="string">'mean'</span>,<span class="string">'sum'</span>])</span><br><span class="line"></span><br><span class="line">#数据透视表</span><br><span class="line">pd.pivot_table(data, values=<span class="string">'G1'</span>, index=[<span class="string">'sex'</span>],columns=[<span class="string">'age'</span>], aggfunc=np.mean)</span><br></pre></td></tr></table></figure>
<h3 id="类别转换"><a href="#类别转换" class="headerlink" title="类别转换"></a><strong>类别转换</strong></h3><figure class="highlight makefile"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#类别转换</span></span><br><span class="line">medu=data[<span class="string">"Medu"</span>].astype(<span class="string">"category"</span>)</span><br><span class="line">medu.cat.categories=[<span class="string">"None"</span>,<span class="string">"<4th grade"</span>,<span class="string">"5th to 9th grade"</span>,<span class="string">"secondary education"</span>,<span class="string">"higher education"</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment">#转换成哑元</span></span><br><span class="line">sex_dummies = pd.get_dummies(X_train['sex'], prefix='sex')</span><br><span class="line">X_train=X_train.join(sex_dummies)</span><br></pre></td></tr></table></figure>
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