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<li class="toctree-l2 current"><a class="current reference internal" href="#">Functions for Creating NumPy Arrays</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#Creating-Arrays-from-Python-Sequences">Creating Arrays from Python Sequences</a></li>
<li class="toctree-l3"><a class="reference internal" href="#Creating-Constant-Arrays:-zeros-and-ones">Creating Constant Arrays: <code class="docutils literal notranslate"><span class="pre">zeros</span></code> and <code class="docutils literal notranslate"><span class="pre">ones</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#Creating-Sequential-Arrays:-arange-and-linspace">Creating Sequential Arrays: <code class="docutils literal notranslate"><span class="pre">arange</span></code> and <code class="docutils literal notranslate"><span class="pre">linspace</span></code></a></li>
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<div class="section" id="Functions-for-Creating-NumPy-Arrays">
<h1>Functions for Creating NumPy Arrays<a class="headerlink" href="#Functions-for-Creating-NumPy-Arrays" title="Permalink to this headline">¶</a></h1>
<p>This section presents standard methods for creating NumPy arrays of varying shapes and contents. NumPy provides a laundry list of functions for creating arrays:</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="kn">as</span> <span class="nn">np</span>
<span class="go"># creating an array from a Python sequence</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)])</span>
<span class="go">array([ 0, 1, 4, 9, 16])</span>
<span class="go"># creating an array filled with ones</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="go">array([[ 1., 1., 1., 1.],</span>
<span class="go"> [ 1., 1., 1., 1.]])</span>
<span class="go"># creating an array of evenly-spaced points</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="go">array([ 0. , 2.5, 5. , 7.5, 10. ])</span>
<span class="go"># creating an array by sampling 10 numbers</span>
<span class="go"># randomly from a mean-1, std-dev-5 normal</span>
<span class="go"># distribution</span>
<span class="gp">>>> </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">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="go">array([ 2.549537 , 2.75144951, 0.60031823, 3.75185732, 4.65543858,</span>
<span class="go"> 0.55779525, 1.15574987, -1.98461337, 5.39771083, -7.81395192])</span>
<span class="go"># creating an array of a specified datatype</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">3.20</span><span class="p">,</span> <span class="mf">5.78</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
<span class="go">array([1, 3, 5])</span>
</pre></div>
</div>
<div class="section" id="Creating-Arrays-from-Python-Sequences">
<h2>Creating Arrays from Python Sequences<a class="headerlink" href="#Creating-Arrays-from-Python-Sequences" title="Permalink to this headline">¶</a></h2>
<p>You can create an array from a Python <code class="docutils literal notranslate"><span class="pre">list</span></code> or <code class="docutils literal notranslate"><span class="pre">tuple</span></code> by using NumPy’s <code class="docutils literal notranslate"><span class="pre">array</span></code> function. NumPy will interpret the structure of the data it receives to determine the dimensionality and shape of the array. For example, a single list of numbers will be used to create a 1-dimensional array:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># a list of numbers will become a 1D-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="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">])</span> <span class="c1"># shape: (3,)</span>
<span class="n">array</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="mf">3.</span><span class="p">])</span>
</pre></div>
</div>
<p>Nested lists/tuples will be used to construct multidimensional arrays. For example, a “list of equal-length lists of numbers” will lead to a 2-dimensional array; each of the inner-lists comprises a row of the array. Thus a list of two, length-three lists will produce a (2,3)-shaped array:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># a list of lists of numbers will produce a 2D-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="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]])</span> <span class="c1"># shape: (2, 3)</span>
<span class="n">array</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="mf">3.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]])</span>
</pre></div>
</div>
<p>A “list of equal-length lists, of equal-length lists of numbers” creates a 3D-array, and so on. Recall that using repeated concatenation, <code class="docutils literal notranslate"><span class="pre">[0]*3</span></code> will produce <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">0]</span></code>. Using this, let’s create two lists, each containing three lists, each containing four zeros; feeding this to <code class="docutils literal notranslate"><span class="pre">np.array</span></code> thus produces a 2x3x4 array of zeros:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># A list of lists of lists of zeros creates a 3D-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="o">*</span><span class="mi">4</span><span class="p">]</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
<span class="n">array</span><span class="p">([[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]],</span>
<span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]])</span>
</pre></div>
</div>
<p>You will seldom use lists to form high-dimensional arrays like this. Instead, there are other array-creation functions that are more amendable to generating high-dimensional data, which we will introduce next. For example, we will see that the <code class="docutils literal notranslate"><span class="pre">np.zeros</span></code> function is a much more civilized way to create a high-dimensional array of zeros.</p>
<div class="admonition warning">
<p class="admonition-title fa fa-exclamation-circle"><strong>Warning!</strong></p>
<p>You actually <em>can</em> create an array from lists of <em>unequal</em> lengths. The resulting array is <strong>not</strong> an ND-array as it has no well-defined dimensionality. Instead, something called an <em>object-array</em> is produced, which does not benefit from the majority of NumPy’s features. This is a relatively obscure feature of the NumPy library, and should be avoided unless you really know what you’re doing!</p>
</div>
</div>
<div class="section" id="Creating-Constant-Arrays:-zeros-and-ones">
<h2>Creating Constant Arrays: <code class="docutils literal notranslate"><span class="pre">zeros</span></code> and <code class="docutils literal notranslate"><span class="pre">ones</span></code><a class="headerlink" href="#Creating-Constant-Arrays:-zeros-and-ones" title="Permalink to this headline">¶</a></h2>
<p>NumPy provides the functions <code class="docutils literal notranslate"><span class="pre">zeros</span></code> and <code class="docutils literal notranslate"><span class="pre">ones</span></code>, which will fill an array of user-specified shape with 0s and 1s, respectively:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># create a 3x4 array of zeros</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</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="n">array</span><span class="p">([[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]])</span>
<span class="c1"># create a shape-(4,) array of ones</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">4</span><span class="p">,))</span>
<span class="n">array</span><span class="p">([</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])</span>
</pre></div>
</div>
<p>NumPy provides additional functions for creating constant-valued arrays. Please refer to <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#ones-and-zeros">the official documentation</a> for a complete listing.</p>
</div>
<div class="section" id="Creating-Sequential-Arrays:-arange-and-linspace">
<h2>Creating Sequential Arrays: <code class="docutils literal notranslate"><span class="pre">arange</span></code> and <code class="docutils literal notranslate"><span class="pre">linspace</span></code><a class="headerlink" href="#Creating-Sequential-Arrays:-arange-and-linspace" title="Permalink to this headline">¶</a></h2>
<p>The <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange">arange</a> function allows you to initialize a sequence of integers based on a starting point (inclusive), stopping point (exclusive), and step size. This is very similar to the <code class="docutils literal notranslate"><span class="pre">range</span></code> function; however <code class="docutils literal notranslate"><span class="pre">arange</span></code> immediately creates this sequence as an array, whereas <code class="docutils literal notranslate"><span class="pre">range</span></code> produces a generator.</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">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># start (included): 0, stop (excluded): 10, step:1</span>
<span class="go">array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</span>
<span class="go"># supplying one value to `arange` amounts to specifying the stop value</span>
<span class="go"># start=0 and step=1 are then used as defaults</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="c1"># equivalent to: start: 0, stop: 10, step:1</span>
<span class="go">array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># start (included): -5, stop (excluded): 6, step:2</span>
<span class="go">array([-5, -3, -1, 1, 3, 5])</span>
</pre></div>
</div>
<p>The <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html">linspace</a> function allows you to generate <span class="math notranslate nohighlight">\(N\)</span> <em>evenly-spaced</em> points within a user-specified interval <span class="math notranslate nohighlight">\([i, j]\)</span> (<span class="math notranslate nohighlight">\(i\)</span> and <span class="math notranslate nohighlight">\(j\)</span> are included in the interval). This is often used to generate a domain of values on which to evaluate a mathematical function (e.g. if you want to the sine function from <span class="math notranslate nohighlight">\(-\pi\)</span> to <span class="math notranslate nohighlight">\(\pi\)</span> on a finely-divided grid).</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># generate five evenly-spaced points on the interval [-1, 1]</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">1.</span> <span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.</span> <span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.</span> <span class="p">])</span>
<span class="c1"># generate two evenly-spaced points on the interval [3, 4]</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</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">2</span><span class="p">)</span>
<span class="n">array</span><span class="p">([</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">])</span>
<span class="c1"># generate 100 evenly-spaced points on the interval [-pi, pi]</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">3.14159265</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="mf">3.14159265</span><span class="p">])</span>
</pre></div>
</div>
<p>Numpy has other functions for creating sequential arrays, such as producing an array spaced evenly on a log-scaled interval. See the <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#numerical-ranges">official documentation</a> for a complete listing.</p>
</div>
<div class="section" id="Creating-Arrays-Using-Random-Sampling">
<h2>Creating Arrays Using Random Sampling<a class="headerlink" href="#Creating-Arrays-Using-Random-Sampling" title="Permalink to this headline">¶</a></h2>
<p>Several functions can be accessed from <code class="docutils literal notranslate"><span class="pre">np.random</span></code>, which populate arrays of a user-specified shape by drawing randomly from a specified statistical distribution:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># create a shape-(3,3) array by drawing its entries randomly</span>
<span class="c1"># from the uniform distribution [0, 1)</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="mi">3</span><span class="p">)</span>
<span class="n">array</span><span class="p">([[</span> <span class="mf">0.09542611</span><span class="p">,</span> <span class="mf">0.13183498</span><span class="p">,</span> <span class="mf">0.39836068</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.7358235</span> <span class="p">,</span> <span class="mf">0.77640024</span><span class="p">,</span> <span class="mf">0.74913595</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.37702688</span><span class="p">,</span> <span class="mf">0.86617624</span><span class="p">,</span> <span class="mf">0.39846429</span><span class="p">]])</span>
<span class="c1"># create a shape-(5,) array by drawing its entries randomly</span>
<span class="c1"># from a mean-0, variance-1 normal (a.k.a. Gaussian) distribution</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">5</span><span class="p">)</span>
<span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">1.11262121</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.35392007</span><span class="p">,</span> <span class="mf">0.4245215</span> <span class="p">,</span> <span class="o">-</span><span class="mf">0.81995588</span><span class="p">,</span> <span class="mf">0.65412323</span><span class="p">])</span>
</pre></div>
</div>
<p>There are <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.random.html#distributions">many more functions</a> to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.</p>
</div>
<div class="section" id="Creating-an-Array-with-a-Specified-Data-Type">
<h2>Creating an Array with a Specified Data Type<a class="headerlink" href="#Creating-an-Array-with-a-Specified-Data-Type" title="Permalink to this headline">¶</a></h2>
<p>Each of the preceding functions used to create an array can be passed a so-called ‘keyword’ argument, <code class="docutils literal notranslate"><span class="pre">dtype</span></code>, which instructs NumPy to use a specified data type when producing the contents of the array.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># populate an array using 32-bit floating point numbers</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">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="n">dtype</span><span class="o">=</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">array</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="mf">3.</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
<span class="c1"># default data type produced by `arange` is 32-bit integers</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</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>
<span class="c1"># the data type produced by `arange` can be specified otherwise</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"float16"</span><span class="p">)</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="mf">3.</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float16</span><span class="p">)</span>
<span class="c1"># generate shape-(4,4) array of 64-bit complex-valued 0s</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"complex64"</span><span class="p">)</span>
<span class="n">array</span><span class="p">([[</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">,</span> <span class="mf">0.</span><span class="o">+</span><span class="mf">0.j</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">complex64</span><span class="p">)</span>
</pre></div>
</div>
<p>Refer to <a class="reference external" href="https://docs.scipy.org/doc/numpy/user/basics.types.html#array-types-and-conversions-between-types">the official NumPy documentation</a> for the complete list of available array datatypes.</p>
</div>
<div class="section" id="Joining-Arrays-Together">
<h2>Joining Arrays Together<a class="headerlink" href="#Joining-Arrays-Together" title="Permalink to this headline">¶</a></h2>
<p>Similar to Python lists and tuples, NumPy arrays can be concatenated together. However, because NumPy’s arrays can be multi-dimensional, we can choose the dimension along which arrays are joined.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># demonstrating methods for joining arrays</span>
<span class="o">>>></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">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="o">>>></span> <span class="n">y</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="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">])</span>
<span class="c1"># stack `x` and `y` "vertically"</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span>
<span class="n">array</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="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">]])</span>
<span class="c1"># stack `x` and `y` "horizontally"</span>
<span class="o">>>></span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span>
<span class="n">array</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="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">])</span>
</pre></div>
</div>
<p>A complete listing of functions for joining arrays can be <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#joining-arrays">found in the official NumPy documentation</a>. There are also corresponding functions for splitting an array into independent arrays.</p>
</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><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#ones-and-zeros">Constant arrays</a></li>
<li><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html#numpy-array">numpy.array</a></li>
<li><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#numerical-ranges">Sequential arrays</a></li>
<li><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.random.html#distributions">Random distributions</a></li>
<li><a class="reference external" href="https://docs.scipy.org/doc/numpy/user/basics.types.html#array-types-and-conversions-between-types">Array types</a></li>
<li><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#joining-arrays">Joining arrays</a></li>
</ul>
</div>
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