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<divclass="section" id="Basic-Array-Attributes">
<h1>Basic Array Attributes<aclass="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>
<divclass="highlight-python notranslate"><divclass="highlight"><pre><span></span><spanclass="gp">>>> </span><spanclass="kn">import</span><spanclass="nn">numpy</span><spanclass="k">as</span><spanclass="nn">np</span>
<spanclass="gp">>>> </span><spanclass="n">example_array</span><spanclass="o">=</span><spanclass="n">np</span><spanclass="o">.</span><spanclass="n">array</span><spanclass="p">([[[</span><spanclass="mi">0</span><spanclass="p">,</span><spanclass="mi">1</span><spanclass="p">,</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">],</span>
<spanclass="gp">... </span><spanclass="p">[</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">,</span><spanclass="mi">6</span><spanclass="p">,</span><spanclass="mi">7</span><spanclass="p">]],</span>
<spanclass="gp">...</span>
<spanclass="gp">... </span><spanclass="p">[[</span><spanclass="mi">8</span><spanclass="p">,</span><spanclass="mi">9</span><spanclass="p">,</span><spanclass="mi">10</span><spanclass="p">,</span><spanclass="mi">11</span><spanclass="p">],</span>
<spanclass="gp">... </span><spanclass="p">[</span><spanclass="mi">12</span><spanclass="p">,</span><spanclass="mi">13</span><spanclass="p">,</span><spanclass="mi">14</span><spanclass="p">,</span><spanclass="mi">15</span><spanclass="p">]],</span>
<spanclass="gp">...</span>
<spanclass="gp">... </span><spanclass="p">[[</span><spanclass="mi">16</span><spanclass="p">,</span><spanclass="mi">17</span><spanclass="p">,</span><spanclass="mi">18</span><spanclass="p">,</span><spanclass="mi">19</span><spanclass="p">],</span>
<spanclass="gp">... </span><spanclass="p">[</span><spanclass="mi">20</span><spanclass="p">,</span><spanclass="mi">21</span><spanclass="p">,</span><spanclass="mi">22</span><spanclass="p">,</span><spanclass="mi">23</span><spanclass="p">]]])</span>
</pre></div>
</div>
<p>According to the preceding discussion, it is a 3-dimensional array structured such that:</p>
<ulclass="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>
<divclass="highlight-python notranslate"><divclass="highlight"><pre><span></span><spanclass="c1"># dimensionality of the array</span>
<spanclass="o">>>></span><spanclass="n">example_array</span><spanclass="o">.</span><spanclass="n">ndim</span>
<spanclass="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 <spanclass="math notranslate nohighlight">\(N\)</span> rows and <spanclass="math notranslate nohighlight">\(M\)</span> columns, shape will be <spanclass="math notranslate nohighlight">\((N, M)\)</span>. The length of this shape-tuple is therefore equal to the number of dimensions of the array.</p>
<divclass="highlight-python notranslate"><divclass="highlight"><pre><span></span><spanclass="c1"># shape of the array</span>
<spanclass="o">>>></span><spanclass="n">example_array</span><spanclass="o">.</span><spanclass="n">shape</span>
<spanclass="p">(</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="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>
<divclass="highlight-python notranslate"><divclass="highlight"><pre><span></span><spanclass="c1"># size of the array: the number of elements it stores</span>
<spanclass="o">>>></span><spanclass="n">example_array</span><spanclass="o">.</span><spanclass="n">size</span>
<spanclass="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>
<divclass="highlight-python notranslate"><divclass="highlight"><pre><span></span><spanclass="c1"># `example_array` contains integers, each of which are stored using 32 bits of memory</span>
<spanclass="o">>>></span><spanclass="n">example_array</span><spanclass="o">.</span><spanclass="n">dtype</span>
<spanclass="n">dtype</span><spanclass="p">(</span><spanclass="s1">'int32'</span><spanclass="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 <codeclass="docutils literal notranslate"><spanclass="pre">float64</span></code> has itemsize 8 <spanclass="math notranslate nohighlight">\((= \frac{64}{8})\)</span>, while an array of type <codeclass="docutils literal notranslate"><spanclass="pre">complex32</span></code> has itemsize 4 <spanclass="math notranslate nohighlight">\((= \frac{32}{8})\)</span>.</p>
<divclass="highlight-python notranslate"><divclass="highlight"><pre><span></span><spanclass="c1"># each integer in `example_array` is represented using 4 bytes (32 bits) of memory</span>
<spanclass="o">>>></span><spanclass="n">example_array</span><spanclass="o">.</span><spanclass="n">itemsize</span>
<spanclass="mi">4</span>
</pre></div>
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<divclass="section" id="Links-to-Official-Documentation">
<h2>Links to Official Documentation<aclass="headerlink" href="#Links-to-Official-Documentation" title="Permalink to this headline"></a></h2>
<ulclass="simple">
<li><p><aclass="reference external" href="https://numpy.org/doc/stable/reference/arrays.ndarray.html#array-attributes">Array attributes</a></p></li>
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