Kde#
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import hvplot.pandas # noqa
Kernel density estimate (kde) provides a mechanism for showing the distribution and spread of the data. In hvplot the method is exposed both as kde and density.
from bokeh.sampledata import sea_surface_temperature as sst
df = sst.sea_surface_temperature
df.tail()
| temperature | |
|---|---|
| time | |
| 2017-03-21 22:00:00+00:00 | 4.000 |
| 2017-03-21 22:30:00+00:00 | 3.975 |
| 2017-03-21 23:00:00+00:00 | 4.017 |
| 2017-03-21 23:30:00+00:00 | 4.121 |
| 2017-03-22 00:00:00+00:00 | 4.316 |
df.hvplot.kde()
There are many options exposed and explorable using tab completion. In this case we’ll create a colormap that spans the rainbow and divide the temperature by month.
import numpy as np
import colorcet as cc
categorical_cmap = [cc.rainbow[int(i)] for i in np.linspace(0, 255, 12)]
df.hvplot.kde(by='time.month', cmap=categorical_cmap, legend='top', height=400)
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Download this notebook from GitHub (right-click to download).