<story><title>Seaborn: a high-level Python interface for drawing statistical graphics</title><url>https://github.com/mwaskom/seaborn</url></story><parent_chain></parent_chain><comment><author>jordigh</author><text>While we&amp;#x27;re talking about stats and Python could I convince someone here to implement a fast medcouple for statsmodels? I can&amp;#x27;t do it myself because I read R&amp;#x27;s GPL&amp;#x27;ed code in order to understand the algorithm. Using my understanding, I wrote the following high-level description of it:&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Medcouple&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Medcouple&lt;/a&gt;&lt;p&gt;This should be taken as the design spec of a clean-room reverse engineering, so that we can have a free, fast and non-copylefted implementation. It&amp;#x27;s not that I have a problem with copyleft (in fact, I prefer it), but I really want statsmodels to fix their implementation, and they&amp;#x27;re GPL-phobic.&lt;p&gt;Since Seaborn has boxplots, implementing an adjusted boxplot seems relevant.&lt;p&gt;edit: Oh, one more thing. I&amp;#x27;d love any feedback on how to improve the &amp;quot;design spec&amp;quot;, in case I wasn&amp;#x27;t able to make it clear enough.</text></comment>
<story><title>Seaborn: a high-level Python interface for drawing statistical graphics</title><url>https://github.com/mwaskom/seaborn</url></story><parent_chain></parent_chain><comment><author>danso</author><text>tl;dr:&lt;p&gt;I&amp;#x27;m new to Seaborn and matplotlib in general, but Seaborn is a wrapper on top of matplotlib, and from what I can tell, was borne partly out of frustration with how hard it is to get matplotlib graphics to look decent out-of-the-box. Which makes it, in one sense, kind of like what ggplot2 was to R&amp;#x27;s standard plotting tools.&lt;p&gt;However, Seaborn has a more object-oriented API, among other things:&lt;p&gt;&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;stanford.edu&amp;#x2F;~mwaskom&amp;#x2F;software&amp;#x2F;seaborn&amp;#x2F;introduction.html&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;stanford.edu&amp;#x2F;~mwaskom&amp;#x2F;software&amp;#x2F;seaborn&amp;#x2F;introduction.h...&lt;/a&gt;&lt;p&gt;&amp;gt;  Seaborn’s goals are similar to those of R’s ggplot, but it takes a different approach with an imperative and object-oriented style that tries to make it straightforward to construct sophisticated plots. If matplotlib “tries to make easy things easy and hard things possible”, seaborn aims to make a well-defined set of hard things easy too.&lt;p&gt;There already is an attempt to port ggplot over to Python, and its authors&amp;#x27; opinion is that its API should look like R&amp;#x27;s ggplot2, which means the syntax is not Pythonic: &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;ggplot.yhathq.com&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;ggplot.yhathq.com&amp;#x2F;&lt;/a&gt;</text></comment>