Update documentation

4-viz/plotting.html

@@ -32,7 +32,7 @@
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+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
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@@ -667,7 +667,7 @@ <h2>Learning Objectives<a class="headerlink" href="#learning-objectives" title="
 <li><p>Customize basic features of a plot, such as axis labels, titles, colors, and line styles</p></li>
 </ul>
 <hr class="docutils" />
-<img src='https://matplotlib.org/stable/_static/logo2_compressed.svg' alt='Matplotlib' width=225>
+<img src='./images/matplotlib.svg' alt='Matplotlib' width=225>
 <p><a class="reference external" href="https://matplotlib.org/">Matplotlib</a> is, effectively, the core plotting and data visualization package in Python. Many other packages use Matplotlib for data visualization, including pandas, NumPy, and SciPy. Matplotlib is not the only visualization package in Python, by any means. There are many others, including <a class="reference external" href="https://seaborn.pydata.org">seaborn</a>, <a class="reference external" href="https://altair-viz.github.io">Altair</a>, <a class="reference external" href="http://yhat.github.io/ggpy/">ggpy</a>, <a class="reference external" href="https://docs.bokeh.org/en/latest/index.html">Bokeh</a>, and <a class="reference external" href="https://plot.ly">plot.ly</a>. Some of the others are actually built on top of Matplotlib, but simply the syntax for creating specific, complex types of graphics relative to what’s required in Matplotlib (these are called <strong>wrappers</strong> for Matplotlib). Others are entirely independent. Regardless, Matplotlib is the most widely-used and flexible package for data visualization in Python, and so it’s valuable to learn it first, and then build out your skills from there.</p>
 <p>Matplotlib is also a very mature Python package, having been first released in 2003 and continuously updated since then. It has a strong development community, a detailed website with extensive documentation and many examples, and there is copious third party documentation in the form of blog posts, books, and more — much of which is freely available.</p>
 </section>
@@ -709,7 +709,7 @@ <h2>Generating a Plot<a class="headerlink" href="#generating-a-plot" title="Link
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x116631ee0&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x12c575c10&gt;]
 </pre></div>
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_sources/4-viz/plotting.ipynb

@@ -32,7 +32,7 @@
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    "metadata": {},
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-    "<img src='https://matplotlib.org/stable/_static/logo2_compressed.svg' alt='Matplotlib' width=225>\n",
+    "<img src='./images/matplotlib.svg' alt='Matplotlib' width=225>\n",
     "\n",
     "[Matplotlib](https://matplotlib.org/) is, effectively, the core plotting and data visualization package in Python. Many other packages use Matplotlib for data visualization, including pandas, NumPy, and SciPy. Matplotlib is not the only visualization package in Python, by any means. There are many others, including [seaborn](https://seaborn.pydata.org), [Altair](https://altair-viz.github.io), [ggpy](http://yhat.github.io/ggpy/), [Bokeh](https://docs.bokeh.org/en/latest/index.html), and [plot.ly](https://plot.ly). Some of the others are actually built on top of Matplotlib, but simply the syntax for creating specific, complex types of graphics relative to what's required in Matplotlib (these are called **wrappers** for Matplotlib). Others are entirely independent. Regardless, Matplotlib is the most widely-used and flexible package for data visualization in Python, and so it's valuable to learn it first, and then build out your skills from there. \n",
     "\n",