<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>trextrex</author><text>You should check out &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;IGITUGraz&amp;#x2F;live-plotter&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;IGITUGraz&amp;#x2F;live-plotter&lt;/a&gt;  (I am the author of the package)&lt;p&gt;It is designed exactly for this use-case -- visualizing results of an ongoing simulation. It does best effort plotting, but doesn&amp;#x27;t block computation for plotting. I would be happy to receive feedback on the package.</text><parent_chain><item><author>radarsat1</author><text>Does anyone know of a good &lt;i&gt;non-blocking&lt;/i&gt; plotting library for Python?&lt;p&gt;I find that for some applications matplotlib really slows down my loops, but there are times that I really need to visualize the in-progress computations. While some other libraries might be faster, what I really want is simply to send instructions and data to a separate process that is performing the plotting.  I&amp;#x27;d like it to completely render a frame and simply skip instructions for following frames until it&amp;#x27;s done rendering, avoid buffering up every single frame that is sent to it, and have it plot the next available frame when it&amp;#x27;s done.  I have yet to see a library that functions this way.  Basically I want to be able to easily visualize an on-going computation without &lt;i&gt;blocking&lt;/i&gt; it just for the rendering of temporary results.</text></item><item><author>Dowwie</author><text>Check out plotly for general graphing use and bokeh for heavy loads.&lt;p&gt;An exhaustive list of dataviz libraries is maintained at &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;fasouto&amp;#x2F;awesome-dataviz&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;fasouto&amp;#x2F;awesome-dataviz&lt;/a&gt;&lt;p&gt;For Python:&lt;p&gt;&lt;pre&gt;&lt;code&gt;  * altair - Declarative statistical visualizations, based on Vega-lite.
  * bokeh - Interactive Web Plotting for Python.
  * diagram - Text mode diagrams using UTF-8 characters
  * ggplot - plotting system based on R&amp;#x27;s ggplot2.
  * glumpy - OpenGL scientific visualizations library.
  * holoviews - Complex and declarative visualizations from annotated data.
  * matplotlib - 2D plotting library.
  * missingno - provides flexible toolset of data-visualization utilities that allows quick visual summary of the completeness of your dataset, based on matplotlib.
  * plotly - Interactive web based visualization built on top of plotly.js
  * plotnine - A grammar of graphics for Python
  * pygal - A dynamic SVG charting library.
  * PyQtGraph - Interactive and realtime 2D&amp;#x2F;3D&amp;#x2F;Image plotting and science&amp;#x2F;engineering widgets.
  * seaborn - A library for making attractive and informative statistical graphics.
  * toyplot - The kid-sized plotting toolkit for Python with grownup-sized goals.
  * Veusz - https:&amp;#x2F;&amp;#x2F;veusz.github.io
  * VisPy - High-performance scientific visualization based on OpenGL.&lt;/code&gt;&lt;/pre&gt;</text></item></parent_chain></comment><story><title>Python Graph Gallery</title><url>https://python-graph-gallery.com/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>edraferi</author><text>Bokeh supports streaming data to existing graphs. This lets you define a graph, display it in a browser, and then push updates from your code. This is a lot lighter than doing a full draw in matplotlib, plus works over the web.&lt;p&gt;Here&amp;#x27;s an intro blog post from 2013 [0] and a current tutorial [1]. This functionality depends on Bokeh Server, docs here [2]&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.anaconda.com&amp;#x2F;developer-blog&amp;#x2F;painless-streaming-plots-bokeh&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.anaconda.com&amp;#x2F;developer-blog&amp;#x2F;painless-streaming-p...&lt;/a&gt;&lt;p&gt;[1] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;nbviewer.jupyter.org&amp;#x2F;github&amp;#x2F;bokeh&amp;#x2F;bokeh-notebooks&amp;#x2F;blob&amp;#x2F;master&amp;#x2F;tutorial&amp;#x2F;11%20-%20Running%20Bokeh%20Applictions.ipynb#Streaming-Data&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;nbviewer.jupyter.org&amp;#x2F;github&amp;#x2F;bokeh&amp;#x2F;bokeh-notebooks&amp;#x2F;blo...&lt;/a&gt;&lt;p&gt;[2] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;bokeh.pydata.org&amp;#x2F;en&amp;#x2F;latest&amp;#x2F;docs&amp;#x2F;user_guide&amp;#x2F;server.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;bokeh.pydata.org&amp;#x2F;en&amp;#x2F;latest&amp;#x2F;docs&amp;#x2F;user_guide&amp;#x2F;server.ht...&lt;/a&gt;</text><parent_chain><item><author>radarsat1</author><text>Does anyone know of a good &lt;i&gt;non-blocking&lt;/i&gt; plotting library for Python?&lt;p&gt;I find that for some applications matplotlib really slows down my loops, but there are times that I really need to visualize the in-progress computations. While some other libraries might be faster, what I really want is simply to send instructions and data to a separate process that is performing the plotting.  I&amp;#x27;d like it to completely render a frame and simply skip instructions for following frames until it&amp;#x27;s done rendering, avoid buffering up every single frame that is sent to it, and have it plot the next available frame when it&amp;#x27;s done.  I have yet to see a library that functions this way.  Basically I want to be able to easily visualize an on-going computation without &lt;i&gt;blocking&lt;/i&gt; it just for the rendering of temporary results.</text></item><item><author>Dowwie</author><text>Check out plotly for general graphing use and bokeh for heavy loads.&lt;p&gt;An exhaustive list of dataviz libraries is maintained at &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;fasouto&amp;#x2F;awesome-dataviz&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;fasouto&amp;#x2F;awesome-dataviz&lt;/a&gt;&lt;p&gt;For Python:&lt;p&gt;&lt;pre&gt;&lt;code&gt;  * altair - Declarative statistical visualizations, based on Vega-lite.
  * bokeh - Interactive Web Plotting for Python.
  * diagram - Text mode diagrams using UTF-8 characters
  * ggplot - plotting system based on R&amp;#x27;s ggplot2.
  * glumpy - OpenGL scientific visualizations library.
  * holoviews - Complex and declarative visualizations from annotated data.
  * matplotlib - 2D plotting library.
  * missingno - provides flexible toolset of data-visualization utilities that allows quick visual summary of the completeness of your dataset, based on matplotlib.
  * plotly - Interactive web based visualization built on top of plotly.js
  * plotnine - A grammar of graphics for Python
  * pygal - A dynamic SVG charting library.
  * PyQtGraph - Interactive and realtime 2D&amp;#x2F;3D&amp;#x2F;Image plotting and science&amp;#x2F;engineering widgets.
  * seaborn - A library for making attractive and informative statistical graphics.
  * toyplot - The kid-sized plotting toolkit for Python with grownup-sized goals.
  * Veusz - https:&amp;#x2F;&amp;#x2F;veusz.github.io
  * VisPy - High-performance scientific visualization based on OpenGL.&lt;/code&gt;&lt;/pre&gt;</text></item></parent_chain></comment><story><title>Python Graph Gallery</title><url>https://python-graph-gallery.com/</url></story>