Python Matplotlib Make 3D plot interactive in Jupyter Notebook

Question

I use Jupyter Notebook to make analysis of datasets  There are a lot of plots in the notebook  and some of them are 3d plots   I m wondering if it is possible to make the 3d plot interactive  so I can later play with it in more details  Maybe we can add a button on it  Clicking it can pop out a 3d plot and people can zoom  pan  rotate etc   My thougths  1  matplotlib   qt This does not fit my case  because I need to continue plot after the 3d plot   qt will interfere with later plots  2  mpld3 mpld3 is almost ideal in my case  no need to rewrite anything  compatible with matplotlib  However  it only support 2D plot  And I didn t see any plan working on 3D  https   github com mpld3 mpld3 issues 223   3  bokeh   visjs Didn t find any actualy example of 3d plot in bokeh gallery  I only find https   demo bokeh org surface3d  which uses visjs  4  Javascript 3D plot  Since what I need is just line and surce  is it possible to pass the data to js plot using js in the browser to make it interacive   Then we may need to add 3d axis as well   This may be similar to visjs  and mpld3

User · Answer

For 3-D visualization pythreejs is the best way to go probably in the notebook. It leverages the interactive widget infrastructure of the notebook, so connection between the JS and python is seamless.

A more advanced library is bqplot which is a d3-based interactive viz library for the iPython notebook, but it only does 2D

User · Answer

There is a new library called ipyvolume that may do what you want  the documentation shows live demos  The current version doesn t do meshes and lines  but master from the git repo does  as will version 0 4    Disclaimer  I m the author

User · Answer

A solution I came up with is to use a vis js instance in an iframe  This shows an interactive 3D plot inside a notebook  which still works in nbviewer  The visjs code is borrowed from the example code on the 3D graph page  A small notebook to illustrate this  demo  The code itself   from IPython core display import display  HTML import json  def plot3D X  Y  Z  height 600  xlabel    X   ylabel    Y   zlabel    Z   initialCamera   None        options              width    100             style    surface            showPerspective   True           showGrid   True           showShadow   False           keepAspectRatio   True           height   str height     px             if initialCamera          options  cameraPosition     initialCamera      data       x   X y x    y   Y y x    z   Z y x   for y in range X shape 0   for x in range X shape 1         visCode   r            lt link href  https   cdnjs cloudflare com ajax libs vis 4 21 0 vis min css  type  text css  rel  stylesheet    gt          lt script src  https   cdnjs cloudflare com ajax libs vis 4 21 0 vis min js  gt  lt  script gt          lt div id  pos  style  top 0px left 0px position absolute   gt  lt  div gt          lt div id  visualization  gt  lt  div gt          lt script type  text javascript  gt          var data   new vis DataSet            data add       json dumps data                  var options         json dumps options                 var container   document getElementById  visualization            var graph3d   new vis Graph3d container  data  options           graph3d on  cameraPositionChange   function evt                        elem   document getElementById  pos                elem innerHTML    H      evt horizontal     lt br gt V      evt vertical     lt br gt D      evt distance                      lt  script gt              htmlCode     lt iframe srcdoc    visCode    width  100   height      str height     px  style  border 0   scrolling  no  gt   lt  iframe gt       display HTML htmlCode

User · Answer

try     matplotlib notebook  see jakevdp reply  here  EDIT for JupyterLab users   Follow the instructions to install jupyter-matplotlib   Then the magic command above is no longer needed  as in the example     Enabling the  widget  backend    This requires jupyter-matplotlib a k a  ipympl    ipympl can be install via pip or conda   matplotlib widget   aka import ipympl  import matplotlib pyplot as plt  plt plot  0  1  2  2   plt show     Finally  note Maarten Breddels  reply  IMHO ipyvolume is indeed very impressive  and useful

User · Answer

You may go with Plotly library  It can render interactive 3D plots directly in Jupyter Notebooks   To do so you first need to install Plotly by running   pip install plotly   You might also want to upgrade the library by running     pip install plotly --upgrade   After that in you Jupyter Notebook you may write something like     Import dependencies import plotly import plotly graph objs as go    Configure Plotly to be rendered inline in the notebook  plotly offline init notebook mode      Configure the trace  trace   go Scatter3d      x  1  2  3       lt -- Put your data instead     y  4  5  6       lt -- Put your data instead     z  7  8  9       lt -- Put your data instead     mode  markers       marker            size   10           opacity   0 8             Configure the layout  layout   go Layout      margin   l   0   r   0   b   0   t   0     data    trace   plot figure   go Figure data data  layout layout     Render the plot  plotly offline iplot plot figure    As a result the following chart will be plotted for you in Jupyter Notebook and you ll be able to interact with it  Of course you will need to provide your specific data instead of suggeseted one

User · Answer

Plotly is missing in this list  I ve linked the python binding page  It definitively has animated and interative 3D Charts  And since it is Open Source most of that is available offline  Of course it is working with Jupyter

[python] Python & Matplotlib: Make 3D plot interactive in Jupyter Notebook

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