I have a 3D scatter which wanna plot using Plotly in python. The problem is size of the dataframe is too large and I want to use webgl to plot the graph. As I know plotly has go.Scatter3d function to plot scatters. Also, there is a go.Scattergl to plot large datasets. However, U can't find something like go.Scatter3Dgl. What should I do?
I believe 3D scatter plots use webgl by default. If you inspect a scatter_3d you'll find that it is in a class="gl-container". Likewise a regular Scatter is in a class="main-svg and a Scattergl is in a class="gl-container".
From plotly:
Note: It is important to note that any figures containing WebGL traces
(i.e. of type scattergl, heatmapgl, contourgl, scatter3d, surface,
mesh3d, scatterpolargl, cone, streamtube, splom, or parcoords) that
are exported in a vector format will include encapsulated rasters,
instead of vectors, for some parts of the image.
Related
I have generated a series of 2D plots using matplotlib.pyplot. I want to change the perspective of each 2D plot to make them look more "3D" (from the rectangular shape to parallelogram shape) and stack them together by hand, which will look something like this:
If there are texts present in the 2D plot (e.g. labels, title, legend), I want them to be rotated together with the plot. The reason I don't want to use mplot3d is that it doesn't support some advanced functions that is used in my 2D plots.
This has already been asked before for 3D plots: how to set "camera position" for 3d plots using python/matplotlib?, but the ax.view_init is only implemented for 3D plots. I wonder if there is a way to also change the camera angle for a 2D plot. If not, are there any tools that can do this task?
I have the recurrent issue of having matplotlib bar graphs containing too many categorical values in the X axis. Resize a figure automatically in matplotlib and Python matplotlib multiple bars does not make the trick because my x values are not x. I am having the idea of splitting the graph into two graphs when it get past a certain amount of data point in the graph. I cannot find anything about in the matplotlib document, nor anywhere.
Is there a matplotlib tool to do that? or i would need to write an algorithm that detects the length of the dataset?
I Posted this question about 3D plots of data frames:
3D plot of 2d Pandas data frame
and the user referred me very very helfully to this:
Plotting Pandas Crosstab Dataframe into 3D bar chart
It use useful and the code worked in principle, but it lookes like a mess (see image below) for several reasons:
I have huge number of values to plot (470 or so, along the y-axis) so perhaps a bar chart is not the best way (I am going for a histogram kind of look, so I assumed very narrow bars would be suitable)
my counts (z axis) do not give almost any information, because the differences I need to see are from 100 to the max value
how can I make the 3D plot that shows up interactive? (being able to rotate etc) - I have seen it done in blogs/videos but sure if it's something on Tools -> Preferences that I can't find
So re: the second issue, simple enough, I tried to just change the limits of the zbar as I would for a 2D Plot, by incorporating:
ax.set_zlim([110,150])
just before the axis labels, but obviously this is the wrong way:
SO do I have to limit the values from the original data set (i.e. filter out <110), or is there a way to do this from the plot?
I am trying to generate a contour graph in terms of three parameters (say x, y, z). These parameters come from a data table of more than 5000 values.I need the graphics to look like the figures shown below.
Contour plots are most easily made using matplotlib's contour.
There's also a corresponding contourf function that provides filled contours. Anyway, what you uploaded looks more like matplotlib's pcolor or pcolormesh, as they draw colored pixels instead of isovalue lines.
Here's a nice comparison of both if you need to choose.
Edit: For (x,y,z) points that are not distributed on a grid (i.e. come from random samples), a working solution seems to be a combination of binned_statistic_2d and then either plt.pcolor or plt.contour.
Is there any (simple or complex) way to recreate this plot in matplotlib?
I've tried plotting it using a scatter plot with two different x-values, while adding a small random number to it, but obviously it didn't produce the nice "ordered" effect seen above.
There's a package built on top of matplotlib called beeswarm that positions the points as requested.