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<div class="section" id="Basic-Array-Attributes">
<h1>Basic Array Attributes<a class="headerlink" href="#Basic-Array-Attributes" title="Permalink to this headline">¶</a></h1>
<p>Armed with our understanding of multidimensional NumPy arrays, we now look at methods for programmatically inspecting an array’s attributes (e.g. its dimensionality). It is especially important to understand what an array’s “shape” is.</p>
<p>We will use the following array to provide context for our discussion:</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">example_array</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="gp">... </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="gp">...</span>
<span class="gp">... </span> <span class="p">[[</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</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="gp">... </span> <span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">15</span><span class="p">]],</span>
<span class="gp">...</span>
<span class="gp">... </span> <span class="p">[[</span><span class="mi">16</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="mi">19</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">22</span><span class="p">,</span> <span class="mi">23</span><span class="p">]]])</span>
</pre></div>
</div>
<p>According to the preceding discussion, it is a 3-dimensional array structured such that:</p>
<ul class="simple">
<li><p>axis-0 discerns which of the <strong>3 sheets</strong> to select from.</p></li>
<li><p>axis-1 discerns which of the <strong>2 rows</strong>, in any sheet, to select from.</p></li>
<li><p>axis-2 discerns which of the <strong>4 columns</strong>, in any sheet and row, to select from.</p></li>
</ul>
<p><strong>ndarray.ndim</strong>:</p>
<p>The number of axes (dimensions) of the array.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># dimensionality of the array</span>
<span class="o">>>></span> <span class="n">example_array</span><span class="o">.</span><span class="n">ndim</span>
<span class="mi">3</span>
</pre></div>
</div>
<p><strong>ndarray.shape</strong>:</p>
<p>A tuple of integers indicating the number of elements that are stored along each dimension of the array. For a 2D-array with <span class="math notranslate nohighlight">\(N\)</span> rows and <span class="math notranslate nohighlight">\(M\)</span> columns, shape will be <span class="math notranslate nohighlight">\((N, M)\)</span>. The length of this shape-tuple is therefore equal to the number of dimensions of the array.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># shape of the array</span>
<span class="o">>>></span> <span class="n">example_array</span><span class="o">.</span><span class="n">shape</span>
<span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>ndarray.size</strong>:</p>
<p>The total number of elements of the array. This is equal to the product of the elements of the array’s shape.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># size of the array: the number of elements it stores</span>
<span class="o">>>></span> <span class="n">example_array</span><span class="o">.</span><span class="n">size</span>
<span class="mi">24</span>
</pre></div>
</div>
<p><strong>ndarray.dtype</strong>:</p>
<p>An object describing the data type of the elements in the array. Recall that NumPy’s ND-arrays are <em>homogeneous</em>: they can only posses numbers of a uniform data type.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># `example_array` contains integers, each of which are stored using 32 bits of memory</span>
<span class="o">>>></span> <span class="n">example_array</span><span class="o">.</span><span class="n">dtype</span>
<span class="n">dtype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>ndarray.itemsize</strong>:</p>
<p>The size, in bytes (8 bits is 1 byte), of each element of the array. For example, an array of elements of type <code class="docutils literal notranslate"><span class="pre">float64</span></code> has itemsize 8 <span class="math notranslate nohighlight">\((= \frac{64}{8})\)</span>, while an array of type <code class="docutils literal notranslate"><span class="pre">complex32</span></code> has itemsize 4 <span class="math notranslate nohighlight">\((= \frac{32}{8})\)</span>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># each integer in `example_array` is represented using 4 bytes (32 bits) of memory</span>
<span class="o">>>></span> <span class="n">example_array</span><span class="o">.</span><span class="n">itemsize</span>
<span class="mi">4</span>
</pre></div>
</div>
<div class="section" id="Links-to-Official-Documentation">
<h2>Links to Official Documentation<a class="headerlink" href="#Links-to-Official-Documentation" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.ndarray.html#array-attributes">Array attributes</a></p></li>
</ul>
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