<story><title>Visualizing distributions of data</title><url>http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/plotting_distributions.ipynb</url><text></text></story><parent_chain></parent_chain><comment><author>skeletonjelly</author><text>Really loving these ipython visualisation articles!&lt;p&gt;There was one about detecting bubbles in liquid for the purposes of reducing spilling that was very interesting&lt;p&gt;&lt;a href=&quot;http://nbviewer.ipython.org/github/soft-matter/trackpy-examples/blob/master/notebooks/custom_feature_detection.ipynb&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;nbviewer.ipython.org&amp;#x2F;github&amp;#x2F;soft-matter&amp;#x2F;trackpy-examp...&lt;/a&gt;</text></comment>
<story><title>Visualizing distributions of data</title><url>http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/plotting_distributions.ipynb</url><text></text></story><parent_chain></parent_chain><comment><author>stared</author><text>I find it nicer than bare-bone matplotlib for plotting (one line of code per standard plot + very pleasant graphically).&lt;p&gt;I knew that it exists for some time (and I had it already installed) but only today I rediscovered it as a really useful tool for default plots in my data analysis &amp;#x2F; data exploration.</text></comment>