<h2>首先:</h2>
<p>按照惯例,在Python世界中,<code>numpy</code>的快捷方式是<code>np</code>,因此:</p>
<pre><code>In [1]: import numpy as np
In [2]: a = np.array([[1,2],[3,4]])
</code></pre>
<h2>第二:</h2>
<p>在Numpy中,<strong>维度</strong>,<strong>轴/轴</strong>,<strong>形状</strong>是相关的,有时是相似的概念:</p>
<h3>尺寸</h3>
<p>在<em>数学/物理</em>中,维度或维度非正式地定义为指定空间内任何点所需的最小坐标数。但在Numpy中,根据<a href="https://docs.scipy.org/doc/numpy/user/quickstart.html#the-basics" rel="noreferrer">numpy doc</a>,它与轴/轴相同:</p>
<blockquote>
<p>In Numpy dimensions are called axes. The number of axes is rank.</p>
</blockquote>
<pre><code>In [3]: a.ndim # num of dimensions/axes, *Mathematics definition of dimension*
Out[3]: 2
</code></pre>
<h3>轴/轴</h3>
<p>在Numpy中,<em>nth</em>坐标表示一个<code>array</code>。多维数组每个轴可以有一个索引。</p>
<pre><code>In [4]: a[1,0] # to index `a`, we specific 1 at the first axis and 0 at the second axis.
Out[4]: 3 # which results in 3 (locate at the row 1 and column 0, 0-based index)
</code></pre>
<h3>形状</h3>
<p>描述每个可用轴上有多少数据(或范围)。</p>
<pre><code>In [5]: a.shape
Out[5]: (2, 2) # both the first and second axis have 2 (columns/rows/pages/blocks/...) data
</code></pre>