update

Author: GYHHAHA

build/doctrees/environment.pickle

@@ -60,6 +60,17 @@
     
     s['c': 'b': -2]
 
+如果前后端点的值存在重复，即非唯一值，那么需要经过排序才能使用切片：
+
+.. ipython:: python
+
+    try:
+        s['a': 'b']
+    except Exception as e:
+        Err_Msg = e
+    Err_Msg
+    s.sort_index()['a': 'b']
+
 【b】以整数为索引的 ``Series``
 
 在使用数据的读入函数时，如果不特别指定所对应的列作为索引，那么会生成从0开始的整数索引作为默认索引。当然，任意一组符合长度要求的整数都可以作为索引。
@@ -377,21 +388,46 @@
     df_multi = df.set_index(['School', 'Grade'])
     df_multi.head()
 
-由于多级索引中的单个元素以元组为单位，因此之前在第一节介绍的 ``loc`` 和 ``iloc`` 方法完全可以照搬，只需把标量的位置替换成对应的元组，不过在索引前最好对 ``MultiIndex`` 进行排序以避免性能警告：
+由于多级索引中的单个元素以元组为单位，因此之前在第一节介绍的 ``loc`` 和 ``iloc`` 方法完全可以照搬，只需把标量的位置替换成对应的元组。
+
+当传入元组列表或单个元组或返回前二者的函数时，需要先进行索引排序以避免性能警告：
 
 .. ipython:: python
 
-    df_multi = df_multi.sort_index()
-    df_multi.loc[('Fudan University', 'Junior')].head()
-    df_multi.loc[[('Fudan University', 'Senior'),
+    with warnings.catch_warnings():
+        warnings.filterwarnings('error')
+        try:
+            df_multi.loc[('Fudan University', 'Junior')].head()
+        except Warning as w:
+            Warning_Msg = w
+    Warning_Msg
+    df_sorted = df_multi.sort_index()
+    df_sorted.loc[('Fudan University', 'Junior')].head()
+    df_sorted.loc[[('Fudan University', 'Senior'),
                   ('Shanghai Jiao Tong University', 'Freshman')]].head()
-    df_multi.loc[df_multi.Weight > 70].head() # 布尔列表也是可用的
-    df_multi.loc[lambda x:('Fudan University','Junior')].head()
+    df_sorted.loc[df_sorted.Weight > 70].head() # 布尔列表也是可用的
+    df_sorted.loc[lambda x:('Fudan University','Junior')].head()
 
-.. admonition:: 练一练
-   :class: hint
+当使用切片时需要注意，在单级别索引中只要切片端点元素是唯一的，那么就可以进行切片，但在多级索引中，无论元组在索引中是否重复出现，都必须经过排序才能使用切片，否则报错：
+
+.. ipython:: python
+
+    try:
+        df_multi.loc[('Fudan University', 'Senior'):].head()
+    except Exception as e:
+        Err_Msg = e
+    Err_Msg
+    df_sorted.loc[('Fudan University', 'Senior'):].head()
+    df_unique = df.drop_duplicates(subset=['School','Grade']
+                                  ).set_index(['School', 'Grade'])
+    df_unique.head()
 
-    与单层索引类似，若存在重复元素，则不能使用切片，请去除重复索引后给出一个元素切片的例子。
+    try:
+        df_unique.loc[('Fudan University', 'Senior'):].head()
+    except Exception as e:
+        Err_Msg = e
+    Err_Msg
+    df_unique.sort_index().loc[('Fudan University', 'Senior'):].head()
 
 此外，在多级索引中的元组有一种特殊的用法，可以对多层的元素进行交叉组合后索引，但同时需要指定 ``loc`` 的列，全选则用 ``:`` 表示。其中，每一层需要选中的元素用列表存放，传入 ``loc`` 的形式为 ``[(level_0_list, level_1_list), cols]`` 。例如，想要得到所有北大和复旦的大二大三学生，可以如下写出：
 

build/doctrees/目录/ch1.doctree

@@ -503,74 +503,74 @@ <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([0.04459277, 0.70822814, 0.72825652])</span>
+<span class="gh">Out[38]: </span><span class="go">array([0.6153947 , 0.88273666, 0.00543514])</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([[0.7086854 , 0.55065805, 0.24684969],</span>
-<span class="go">       [0.01502467, 0.12753788, 0.67763982],</span>
-<span class="go">       [0.61491281, 0.72367285, 0.35903761]])</span>
+<span class="go">array([[0.98101125, 0.02326537, 0.65705324],</span>
+<span class="go">       [0.03397158, 0.07922305, 0.55944787],</span>
+<span class="go">       [0.03126215, 0.85214473, 0.85844943]])</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([10.85277559,  5.42718874,  6.6127651 ])</span>
+<span class="gh">Out[41]: </span><span class="go">array([11.2770425 , 12.46941527,  8.75814737])</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([9.24865468, 9.50059861, 5.08912649])</span>
+<span class="gh">Out[42]: </span><span class="go">array([13.85234451,  9.1684829 ,  7.30636836])</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([-0.55853584, -0.13854145, -0.70530864])</span>
+<span class="gh">Out[43]: </span><span class="go">array([-0.12094655,  1.66356431, -0.90127077])</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([[ 1.30718089,  1.19663584],</span>
-<span class="go">       [ 0.47448249, -1.10435937]])</span>
+<span class="go">array([[-0.25825946,  0.10824804],</span>
+<span class="go">       [ 0.65975602,  0.50712147]])</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([ 4.00657339,  4.56976171, -0.29177403])</span>
+<span class="gh">Out[46]: </span><span class="go">array([5.91026277, 3.02197811, 7.51099581])</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([4.15032959, 5.37686641, 5.8810344 ])</span>
+<span class="gh">Out[47]: </span><span class="go">array([ 6.06441723, -0.82517133, -1.47681533])</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([[10,  9],</span>
-<span class="go">       [13,  5]])</span>
+<span class="go">array([[ 7, 13],</span>
+<span class="go">       [ 7, 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>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [50]: </span><span class="n">my_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">]</span>
 
 <span class="gp">In [51]: </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="mi">2</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.1</span> <span class="p">,</span><span class="mf">0.1</span><span class="p">])</span>
-<span class="gh">Out[51]: </span><span class="go">array([&#39;b&#39;, &#39;d&#39;], dtype=&#39;&lt;U1&#39;)</span>
+<span class="gh">Out[51]: </span><span class="go">array([&#39;b&#39;, &#39;a&#39;], dtype=&#39;&lt;U1&#39;)</span>
 
 <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;a&#39;, &#39;d&#39;, &#39;b&#39;],</span>
-<span class="go">       [&#39;a&#39;, &#39;c&#39;, &#39;a&#39;],</span>
-<span class="go">       [&#39;b&#39;, &#39;d&#39;, &#39;d&#39;]], dtype=&#39;&lt;U1&#39;)</span>
+<span class="go">array([[&#39;a&#39;, &#39;c&#39;, &#39;c&#39;],</span>
+<span class="go">       [&#39;d&#39;, &#39;c&#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;d&#39;, &#39;c&#39;], dtype=&#39;&lt;U1&#39;)</span>
+<span class="gh">Out[53]: </span><span class="go">array([&#39;b&#39;, &#39;c&#39;, &#39;d&#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 0x1daaf67d088&gt;]</span>
+<span class="gh">Out[108]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x263b80c8748&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 0x1daaf64f148&gt;]</span>
+<span class="gh">Out[110]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x263b80b8f88&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 0x1daaf62e848&gt;]</span>
+<span class="gh">Out[111]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x263b80a3288&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 0x1daaf5fd9c8&gt;]</span>
+<span class="gh">Out[112]: </span><span class="go">[&lt;matplotlib.lines.Line2D at 0x263b73c3f88&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>