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FAQ.html
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<!DOCTYPE html>
<html data-content_root="./" lang="en">
<head>
<meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/><meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>FAQ — Datashader v0.16.3</title>
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<li class="toctree-l2"><a class="reference internal" href="user_guide/Inspection_Reductions.html">Inspection Reductions</a></li>
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<li class="toctree-l2"><a class="reference external" href="https://examples.pyviz.org/census/census.html">Census</a></li>
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<section id="faq">
<h1>FAQ<a class="headerlink" href="#faq" title="Link to this heading">#</a></h1>
<section id="when-should-i-use-datashader">
<h2>When should I use Datashader?<a class="headerlink" href="#when-should-i-use-datashader" title="Link to this heading">#</a></h2>
<p>Datashader is designed for working with large datasets, for
cases where it is most crucial to faithfully represent the
<em>distribution</em> of your data. Datashader can work easily with
extremely large datasets, generating a fixed-size data structure
(regardless of the original number of records) that gets transferred to
your local browser for display. If you ever find yourself subsampling
your data just so that you can plot it feasibly, or if you are forced
for practical reasons to iterate over chunks of it rather than looking
at all of it at once, then Datashader can probably help you.</p>
</section>
<section id="when-should-i-not-use-datashader">
<h2>When should I <em>not</em> use Datashader?<a class="headerlink" href="#when-should-i-not-use-datashader" title="Link to this heading">#</a></h2>
<p>If you have a very small number of data points (in the hundreds
or thousands) or curves (in the tens or several tens, each with
hundreds or thousands of points), then conventional plotting packages
like <a class="reference external" href="https://bokeh.pydata.org">Bokeh</a> may be more suitable. With conventional browser-based
packages, all of the data points are passed directly to the browser for
display, allowing specific interaction with each curve or point,
including display of metadata, linking to sources, etc. This approach
offers the most flexibility <em>per point</em> or <em>per curve</em>, but rapidly
runs into limitations on how much data can be processed by the browser,
and how much can be displayed on screen and resolved by the human
visual system. If you are not having such problems, i.e., your data is
easily handled by your plotting infrastructure and you can easily see
and work with all your data onscreen already, then you probably don’t
need Datashader.</p>
</section>
<section id="is-datashader-part-of-bokeh-or-holoviews">
<h2>Is Datashader part of Bokeh or HoloViews?<a class="headerlink" href="#is-datashader-part-of-bokeh-or-holoviews" title="Link to this heading">#</a></h2>
<p>Datashader is an independent project, focusing on generating
aggregate arrays and representations of them as images. Bokeh
and HoloViews are complementary projects to Datashader, focusing on
building browser-based visualizations and dashboards. These and other
plotting packages can display images rendered by Datashader, providing
axes, interactive zooming and panning, selection, legends, hover
information, and so on. Sample code is provided with Datashader for
demonstrating how to use HoloViews (supporting Bokeh, Matplotlib, and
Plotly) or native Matplotlib to provide such features. The library can
also be used separately, without any
external plotting packages, generating images that can be displayed
directly or saved to disk, or generating aggregate arrays suitable
for further analysis.</p>
</section>
<section id="what-s-the-easiest-way-to-use-datashader-interactively">
<h2>What’s the easiest way to use Datashader interactively?<a class="headerlink" href="#what-s-the-easiest-way-to-use-datashader-interactively" title="Link to this heading">#</a></h2>
<p><a class="reference external" href="https://holoviews.org">HoloViews</a>. HoloViews uses Bokeh or Plotly behind
the scenes, but it offers a higher level API that is well suited to
the sorts of magic that allow interactive use of Datashader. For a
given dataset, HoloViews can easily construct either a raw Bokeh/Plotly plot
or a Bokeh/Plotly plot with server-side rendering from Datashader, hiding
nearly all of the complexity involved.</p>
</section>
<section id="how-can-i-get-legends-and-colorbars-for-my-datashader-plot">
<h2>How can I get legends and colorbars for my Datashader plot?<a class="headerlink" href="#how-can-i-get-legends-and-colorbars-for-my-datashader-plot" title="Link to this heading">#</a></h2>
<p>When used as a standalone library, Datashader can only generate images or bare arrays; it does not have any concept of axes, legends, or colorbars. But Datashader is designed to work well as a rendering engine for other plotting libraries that do offer those features. For the specific case of colorbars, just ensure that you are letting the separate plotting library do the colormapping, not Datashader, and you should be able to get full support for colorbars. That is, use Datashader to aggregate the image into a fixed array of values, and then use Bokeh, Plotly, or Holoviews to colormap and render the array as pixels, which will allow the plotting library to construct a suitable colormap. For instance, to get a colorbar in HoloViews, use <code class="docutils literal notranslate"><span class="pre">rasterize()</span></code> to invoke datashader on the data and generate an array of values; do not use <code class="docutils literal notranslate"><span class="pre">datashade()</span></code>, which would <code class="docutils literal notranslate"><span class="pre">rasterize()</span></code> while also calling <code class="docutils literal notranslate"><span class="pre">shade()</span></code> to generate RGB pixel values, at which point the plotting library would not be able to report the mapping from value to color, as it only would have seen the final RGB values. Of course, if you let the plotting library do the colormapping, you will no longer be able to use Datashader-specific features like histogram equalization, which would then need to be implemented by the plotting library (as now done for Bokeh) if you want to have colorbars for such cases.</p>
</section>
<section id="what-data-libraries-can-i-use-with-datashader">
<h2>What data libraries can I use with Datashader?<a class="headerlink" href="#what-data-libraries-can-i-use-with-datashader" title="Link to this heading">#</a></h2>
<p>See the <a class="reference internal" href="user_guide/Performance.html#Data-objects"><span class="std std-ref">Performance user guide</span></a> for the available options for working with columnar/multidimensional/ragged data on single-core/multi-core/distributed/CPU/GPU resources in or out of core for each available glyph.</p>
</section>
<div id="scroller-right">This web page was generated from a Jupyter notebook and not all interactivity will work on this website. <a href="https://raw.githubusercontent.com/holoviz/datashader/main/examples/FAQ.ipynb">Right click to download and run locally</a> for full Python-backed interactivity.</div><hr class="docutils"/>
<p><a class="reference external" href="https://raw.githubusercontent.com/holoviz/datashader/main/examples/FAQ.ipynb">Right click to download this notebook from GitHub.</a></p>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#when-should-i-use-datashader">When should I use Datashader?</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-can-i-get-legends-and-colorbars-for-my-datashader-plot">How can I get legends and colorbars for my Datashader plot?</a></li>
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