update

Author: GYHHAHA

build/doctrees/environment.pickle

@@ -233,7 +233,7 @@
 2. 组合
 ------------
 
-``combine`` 函数能够让两张表按照一定的规则进行组合，在进行规则比较时会自动进行列索引的对齐。对于传入的函数而言，每一次操作中输入的参数是来自两个表的同名 ``Series`` ，依次传入的列是两个表列名的并集，例如下面这个例子会依次传入 ``A,B,C,D`` 四组序列，每组为左右表的两个序列。同时，进行 ``A`` 列比较的时候， ``s1`` 指代的就是一个全空的序列，因为它在被调用的表中并不存在，并且来自第一个表的序列索引会被 ``reindex`` 成两个索引的并集。具体的过程可以通过在传入的函数中插入适当的 ``print`` 方法查看。
+``combine`` 函数能够让两张表按照一定的规则进行组合，在进行规则比较时会自动进行列索引的对齐。对于传入的函数而言，每一次操作中输入的参数是来自两个表的同名 ``Series`` ，依次传入的列是两个表列名的并集，例如下面这个例子会依次传入 ``A,B,C,D`` 四组序列，每组为左右表的两个序列。同时，进行 ``A`` 列比较的时候， ``s2`` 指代的就是一个全空的序列，因为它在被调用的表中并不存在，并且来自第一个表的序列索引会被 ``reindex`` 成两个索引的并集。具体的过程可以通过在传入的函数中插入适当的 ``print`` 方法查看。
 
 下面的例子表示选出对应索引位置较小的元素：
 

build/doctrees/目录/ch1.doctree

@@ -503,56 +503,56 @@ <h3>1. np数组的构造<a class="headerlink" href="#np" title="Permalink to thi
 <p>【c】随机矩阵： <code class="docutils literal notranslate"><span class="pre">np.random</span></code></p>
 <p>最常用的随机生成函数为 <code class="docutils literal notranslate"><span class="pre">rand,</span> <span class="pre">randn,</span> <span class="pre">randint,</span> <span class="pre">choice</span></code> ，它们分别表示0-1均匀分布的随机数组、标准正态的随机数组、随机整数组和随机列表抽样：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [38]: </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="c1"># 生成服从0-1均匀分布的三个随机数</span>
-<span class="gh">Out[38]: </span><span class="go">array([0.19644779, 0.05829755, 0.1763729 ])</span>
+<span class="gh">Out[38]: </span><span class="go">array([0.31716937, 0.0257562 , 0.1757882 ])</span>
 
 <span class="gp">In [39]: </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</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="c1"># 注意这里传入的不是元组，每个维度大小分开输入</span>
 <span class="gh">Out[39]: </span><span class="go"></span>
-<span class="go">array([[0.81234435, 0.3204975 , 0.46507329],</span>
-<span class="go">       [0.15952536, 0.56821002, 0.90365527],</span>
-<span class="go">       [0.73249233, 0.00373533, 0.70851416]])</span>
+<span class="go">array([[0.09218596, 0.02981407, 0.78171316],</span>
+<span class="go">       [0.01116467, 0.79777235, 0.26795529],</span>
+<span class="go">       [0.47509305, 0.98702896, 0.63964399]])</span>
 </pre></div>
 </div>
 <p>对于服从区间 <span class="math notranslate nohighlight">\(a\)</span> 到 <span class="math notranslate nohighlight">\(b\)</span> 上的均匀分布可以如下生成：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [40]: </span><span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">15</span>
 
 <span class="gp">In [41]: </span><span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="n">a</span><span class="p">)</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">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">a</span>
-<span class="gh">Out[41]: </span><span class="go">array([6.68214573, 5.10422845, 9.83236843])</span>
+<span class="gh">Out[41]: </span><span class="go">array([ 5.84238915, 13.9787058 ,  5.43238762])</span>
 </pre></div>
 </div>
 <p>一般的，可以选择已有的库函数：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [42]: </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="gh">Out[42]: </span><span class="go">array([7.54869551, 9.99776418, 6.17509434])</span>
+<span class="gh">Out[42]: </span><span class="go">array([10.93549541, 14.21013578,  6.46852749])</span>
 </pre></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">randn</span></code> 生成了 <span class="math notranslate nohighlight">\(N\rm{(\mathbf{0}, \mathbf{I})}\)</span> 的标准正态分布：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [43]: </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">3</span><span class="p">)</span>
-<span class="gh">Out[43]: </span><span class="go">array([ 0.19822997, -0.42655702,  0.54141998])</span>
+<span class="gh">Out[43]: </span><span class="go">array([ 0.54801691, -0.46540686,  0.65733351])</span>
 
 <span class="gp">In [44]: </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">2</span><span class="p">)</span>
 <span class="gh">Out[44]: </span><span class="go"></span>
-<span class="go">array([[-1.05118329,  1.16228097],</span>
-<span class="go">       [-1.20598525, -0.43466828]])</span>
+<span class="go">array([[-2.10673238, -0.85087355],</span>
+<span class="go">       [-0.45202665,  0.56211032]])</span>
 </pre></div>
 </div>
 <p>对于服从方差为 <span class="math notranslate nohighlight">\(\sigma^2\)</span> 均值为 <span class="math notranslate nohighlight">\(\mu\)</span> 的一元正态分布可以如下生成：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [45]: </span><span class="n">sigma</span><span class="p">,</span> <span class="n">mu</span> <span class="o">=</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mi">3</span>
 
 <span class="gp">In [46]: </span><span class="n">mu</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">3</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
-<span class="gh">Out[46]: </span><span class="go">array([7.03818638, 2.43763313, 3.28032122])</span>
+<span class="gh">Out[46]: </span><span class="go">array([ 2.7220844 , -0.71042638,  3.10171717])</span>
 </pre></div>
 </div>
 <p>同样的，也可选择从已有函数生成：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [47]: </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="gh">Out[47]: </span><span class="go">array([3.07728292, 3.20987623, 4.61403821])</span>
+<span class="gh">Out[47]: </span><span class="go">array([1.99030477, 6.59505467, 4.62269539])</span>
 </pre></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">randint</span></code> 可以指定生成随机整数的最小值最大值（不包含）和维度大小：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [48]: </span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</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="c1"># 生成5到14的随机整数</span>
 
 <span class="gp">In [49]: </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
 <span class="gh">Out[49]: </span><span class="go"></span>
-<span class="go">array([[11,  7],</span>
-<span class="go">       [ 5,  5]])</span>
+<span class="go">array([[14, 11],</span>
+<span class="go">       [ 5, 12]])</span>
 </pre></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">choice</span></code> 可以从给定的列表中，以一定概率和方式抽取结果，当不指定概率时为均匀采样，默认抽取方式为有放回抽样：</p>
@@ -563,14 +563,14 @@ <h3>1. np数组的构造<a class="headerlink" href="#np" title="Permalink to thi
 
 <span class="gp">In [52]: </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">my_list</span><span class="p">,</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="gh">Out[52]: </span><span class="go"></span>
-<span class="go">array([[&#39;c&#39;, &#39;c&#39;, &#39;d&#39;],</span>
-<span class="go">       [&#39;a&#39;, &#39;a&#39;, &#39;d&#39;],</span>
-<span class="go">       [&#39;a&#39;, &#39;c&#39;, &#39;b&#39;]], dtype=&#39;&lt;U1&#39;)</span>
+<span class="go">array([[&#39;c&#39;, &#39;b&#39;, &#39;c&#39;],</span>
+<span class="go">       [&#39;b&#39;, &#39;d&#39;, &#39;d&#39;],</span>
+<span class="go">       [&#39;a&#39;, &#39;a&#39;, &#39;d&#39;]], dtype=&#39;&lt;U1&#39;)</span>
 </pre></div>
 </div>
 <p>当返回的元素个数与原列表相同时，不放回抽样等价于使用 <code class="docutils literal notranslate"><span class="pre">permutation</span></code> 函数，即打散原列表：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [53]: </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">my_list</span><span class="p">)</span>
-<span class="gh">Out[53]: </span><span class="go">array([&#39;b&#39;, &#39;a&#39;, &#39;c&#39;, &#39;d&#39;], dtype=&#39;&lt;U1&#39;)</span>
+<span class="gh">Out[53]: </span><span class="go">array([&#39;d&#39;, &#39;c&#39;, &#39;b&#39;, &#39;a&#39;], dtype=&#39;&lt;U1&#39;)</span>
 </pre></div>
 </div>
 <p>最后，需要提到的是随机种子，它能够固定随机数的输出结果：</p>

build/doctrees/目录/ch10.doctree

@@ -1009,19 +1009,19 @@ <h3>1. 滑动窗口<a class="headerlink" href="#id10" title="Permalink to this h
 <span class="gp">In [107]: </span><span class="n">r</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="s1">&#39;30D&#39;</span><span class="p">)</span>
 
 <span class="gp">In [108]: </span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-<span class="gh">Out[108]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x28b082ee208&gt;]</span>
+<span class="gh">Out[108]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x196b0413488&gt;]</span>
 
 <span class="gp">In [109]: </span><span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;BOLL LINES&#39;</span><span class="p">)</span>
 <span class="gh">Out[109]: </span><span class="go">Text(0.5, 1.0, &#39;BOLL LINES&#39;)</span>
 
 <span class="gp">In [110]: </span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
-<span class="gh">Out[110]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x28b0834ee48&gt;]</span>
+<span class="gh">Out[110]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x196b0468988&gt;]</span>
 
 <span class="gp">In [111]: </span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">+</span><span class="n">r</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
-<span class="gh">Out[111]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x28b0834ee88&gt;]</span>
+<span class="gh">Out[111]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x196b0413848&gt;]</span>
 
 <span class="gp">In [112]: </span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">-</span><span class="n">r</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
-<span class="gh">Out[112]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x28b08359a08&gt;]</span>
+<span class="gh">Out[112]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x196b0475cc8&gt;]</span>
 </pre></div>
 </div>
 <a class="reference internal image-reference" href="../_images/ch10.png"><img alt="../_images/ch10.png" src="../_images/ch10.png" style="width: 400px;" /></a>

build/doctrees/目录/ch4.doctree

@@ -442,7 +442,7 @@ <h3>3. Groupby对象<a class="headerlink" href="#groupby" title="Permalink to th
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [13]: </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">&#39;School&#39;</span><span class="p">,</span> <span class="s1">&#39;Grade&#39;</span><span class="p">])</span>
 
 <span class="gp">In [14]: </span><span class="n">gb</span>
-<span class="gh">Out[14]: </span><span class="go">&lt;pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000028B0A133F48&gt;</span>
+<span class="gh">Out[14]: </span><span class="go">&lt;pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000196B233ED48&gt;</span>
 </pre></div>
 </div>
 <p>通过 <code class="docutils literal notranslate"><span class="pre">ngroups</span></code> 属性，可以得到分组个数：</p>

build/doctrees/目录/ch6.doctree

@@ -626,7 +626,7 @@ <h3>1. 比较<a class="headerlink" href="#id9" title="Permalink to this headline
 </div>
 <div class="section" id="id10">
 <h3>2. 组合<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal notranslate"><span class="pre">combine</span></code> 函数能够让两张表按照一定的规则进行组合，在进行规则比较时会自动进行列索引的对齐。对于传入的函数而言，每一次操作中输入的参数是来自两个表的同名 <code class="docutils literal notranslate"><span class="pre">Series</span></code> ，依次传入的列是两个表列名的并集，例如下面这个例子会依次传入 <code class="docutils literal notranslate"><span class="pre">A,B,C,D</span></code> 四组序列，每组为左右表的两个序列。同时，进行 <code class="docutils literal notranslate"><span class="pre">A</span></code> 列比较的时候， <code class="docutils literal notranslate"><span class="pre">s1</span></code> 指代的就是一个全空的序列，因为它在被调用的表中并不存在，并且来自第一个表的序列索引会被 <code class="docutils literal notranslate"><span class="pre">reindex</span></code> 成两个索引的并集。具体的过程可以通过在传入的函数中插入适当的 <code class="docutils literal notranslate"><span class="pre">print</span></code> 方法查看。</p>
+<p><code class="docutils literal notranslate"><span class="pre">combine</span></code> 函数能够让两张表按照一定的规则进行组合，在进行规则比较时会自动进行列索引的对齐。对于传入的函数而言，每一次操作中输入的参数是来自两个表的同名 <code class="docutils literal notranslate"><span class="pre">Series</span></code> ，依次传入的列是两个表列名的并集，例如下面这个例子会依次传入 <code class="docutils literal notranslate"><span class="pre">A,B,C,D</span></code> 四组序列，每组为左右表的两个序列。同时，进行 <code class="docutils literal notranslate"><span class="pre">A</span></code> 列比较的时候， <code class="docutils literal notranslate"><span class="pre">s2</span></code> 指代的就是一个全空的序列，因为它在被调用的表中并不存在，并且来自第一个表的序列索引会被 <code class="docutils literal notranslate"><span class="pre">reindex</span></code> 成两个索引的并集。具体的过程可以通过在传入的函数中插入适当的 <code class="docutils literal notranslate"><span class="pre">print</span></code> 方法查看。</p>
 <p>下面的例子表示选出对应索引位置较小的元素：</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [53]: </span><span class="k">def</span> <span class="nf">choose_min</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">):</span>
 <span class="gp">   ....: </span>    <span class="n">s2</span> <span class="o">=</span> <span class="n">s2</span><span class="o">.</span><span class="n">reindex_like</span><span class="p">(</span><span class="n">s1</span><span class="p">)</span>