A bivariate plot uses nested contours (or contours combined with color) to indicate regions of higher local density. Bivariate plots provide a convenient way to visualize a 2D distribution of values as a Kernel Density Estimate (KDE) and therefore provide a 2D
(
self, x=None, y=None, colorbar=True, bandwidth=None, cut=3, filled=False, levels=10, **kwds
)
| 740 | ) |
| 741 | |
| 742 | def bivariate( |
| 743 | self, x=None, y=None, colorbar=True, bandwidth=None, cut=3, filled=False, levels=10, **kwds |
| 744 | ): |
| 745 | """ |
| 746 | A bivariate plot uses nested contours (or contours combined with color) |
| 747 | to indicate regions of higher local density. |
| 748 | |
| 749 | Bivariate plots provide a convenient way to visualize a 2D distribution |
| 750 | of values as a Kernel Density Estimate (KDE) and therefore provide a 2D |
| 751 | extension to :py:meth:`~.kde`. KDE is a non-parametric way to estimate |
| 752 | the probability density function of a random variable. |
| 753 | |
| 754 | The KDE works by placing a Gaussian kernel at each sample with the supplied |
| 755 | bandwidth, which are then summed to produce the density estimate. By default |
| 756 | the bandwidth is determined using the Scott's method, which usually produces |
| 757 | good results, but it may be overridden by an explicit value. |
| 758 | |
| 759 | Bivariate plots can be a useful alternative to scatter plots, if the data |
| 760 | are too dense to plot each point individually. |
| 761 | |
| 762 | Reference: https://hvplot.holoviz.org/ref/api/manual/hvplot.hvPlot.bivariate.html |
| 763 | |
| 764 | Plotting options: https://hvplot.holoviz.org/ref/plotting_options/index.html |
| 765 | |
| 766 | Parameters |
| 767 | ---------- |
| 768 | x : string, optional |
| 769 | Field name to draw x-positions from. If not specified, the index is used. |
| 770 | y : string, optional |
| 771 | Field name to draw y-positions from |
| 772 | colorbar : boolean |
| 773 | Whether to display a colorbar |
| 774 | bandwidth : float, optional |
| 775 | Allows supplying explicit bandwidth value of the kernel for the |
| 776 | density estimate, rather than relying on Scott. Higher value |
| 777 | yields smoother contours. Default is None. |
| 778 | cut : float, optional |
| 779 | Draw the estimate to cut * bw from the extreme data points. Default is 3. |
| 780 | filled : bool, optional |
| 781 | If True the contours will be filled. Default is False. |
| 782 | levels : int or list, optional |
| 783 | The number of contour lines to draw or a list of scalar values used |
| 784 | to specify the contour levels. Default is 10. |
| 785 | |
| 786 | **kwds : optional |
| 787 | Additional keywords arguments are documented in :ref:`plot-options`. |
| 788 | Run ``hvplot.help('bivariate')`` for the full method documentation. |
| 789 | |
| 790 | Returns |
| 791 | ------- |
| 792 | :class:`holoviews:holoviews.element.Bivariate` / Panel object |
| 793 | You can `print` the object to study its composition and run: |
| 794 | |
| 795 | .. code-block:: |
| 796 | |
| 797 | import holoviews as hv |
| 798 | hv.help(the_holoviews_object) |
| 799 |
no outgoing calls