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<div class="section" id="N维数组简介">
<h1>N维数组简介<a class="headerlink" href="#N维数组简介" title="永久链接至标题">¶</a></h1>
<p>是时候让我们了解NumPy——在Python中做数字工作的旗舰模组——了！以使用此模组，我们需要在代码中“导入”（import）NumPy模组：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
</div>
<p>你也可以运行 <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">numpy</span></code> 来代替以上语句，但是以上语句允许我们在代码中使用简写 ‘np’ 而不需要写出全称 ‘numpy’。这是一个常用的简写。</p>
<p>N维数组（ND-array）是NumPy模组的明星。它能够存储一序列的数字。像Python列表一样，你可以通过“索引”（indexing）来访问该数组的成员，也可以通过“切片”（slicing）来访问数组的子序列。那么NumPy的N维数组和Python列表有什么区别，且为什么有一整个围绕着这个数组的数字模组呢？N维数组特殊于以下两点。它可以：</p>
<ol class="arabic simple">
<li><p>提供在多个维度访问它内在数据的接口。</p></li>
<li><p>通过编译的C代码（而不是Python代码）高效地对其成员或有规律的子序列进行数学操作；这个过程叫做矢量化（vectorization）。</p></li>
</ol>
<p>让我们扫一眼这个模组提供的内容吧。以下代码创建了一个内容为数字0-8的N维数组：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="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="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
</pre></div>
</div>
<p>此对象属于NumPy定义的类 <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># N维数组属于类 `numpy.ndarray`</span>
<span class="o">>>></span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span>
<span class="o">>>></span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span>
<span class="kc">True</span>
</pre></div>
</div>
<p>我们可以重塑（reshape）此数组的形状，使其可以在2个维度中被访问：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</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="gp">>>> </span><span class="n">x</span>
<span class="go">array([[0, 1, 2],</span>
<span class="go"> [3, 4, 5],</span>
<span class="go"> [6, 7, 8]])</span>
</pre></div>
</div>
<p>我们将使用NumPy的一个“矢量化”过的函数来为数组中每一个数都求平方（而不需要编写for循环）：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># 也可以使用简写来计算：x**2</span>
<span class="go">array([[ 0, 1, 4],</span>
<span class="go"> [ 9, 16, 25],</span>
<span class="go"> [36, 49, 64]], dtype=int32)</span>
</pre></div>
</div>
<p>让我们求数据每一列的平均值：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([ 1., 4., 7.])</span>
</pre></div>
</div>
<p>我们可以使用广播来为 <code class="docutils literal notranslate"><span class="pre">x</span></code> 的每一列求多个不同的幂：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">**</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span>
<span class="go">array([[ 1., 1., 4.],</span>
<span class="go"> [ 1., 4., 25.],</span>
<span class="go"> [ 1., 7., 64.]])</span>
</pre></div>
</div>
<p>基本索引（basic indexing）允许我们获得 <code class="docutils literal notranslate"><span class="pre">x</span></code> 的多维度切片：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span><span class="p">[:</span><span class="mi">2</span><span class="p">,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span>
<span class="go">array([[0, 1, 2],</span>
<span class="go"> [3, 4, 5]])</span>
</pre></div>
</div>
<p>进阶索引可以用来获取 <code class="docutils literal notranslate"><span class="pre">x</span></code> 中所有的偶数；让我们更新 <code class="docutils literal notranslate"><span class="pre">x</span></code> 来让它所有的偶数都乘以-1吧：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="n">x</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">array([[ 0, 1, -2],</span>
<span class="go"> [ 3, -4, 5],</span>
<span class="go"> [-6, 7, -8]])</span>
</pre></div>
</div>
<p>在本模组结尾，这些代码片段都将会成为你理解范围内，且NumPy的强大功能也将得到充分的展示。</p>
<div class="section" id="官方说明文档链接">
<h2>官方说明文档链接<a class="headerlink" href="#官方说明文档链接" title="永久链接至标题">¶</a></h2>
<ul class="simple">
<li><p><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html">N维数组</a></p></li>
<li><p><a class="reference external" href="https://docs.scipy.org/doc/numpy/user/basics.html#numpy-basics">NumPy基础</a></p></li>
<li><p><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/index.html">NumPy参考</a></p></li>
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