Errorbars#
import hvplot.pandas # noqa
errorbars are most helpful when overlaid with other plot types. To do this we’ll use the * operator.
from bokeh.sampledata import perceptions
data = perceptions.numberly.describe().transpose().sort_values('mean')
data
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| A couple | 46.0 | 2.326087 | 1.033964 | 2.00 | 2.0000 | 2.000 | 2.000 | 8.0 |
| A few | 46.0 | 3.608696 | 1.357747 | 2.00 | 3.0000 | 3.000 | 4.000 | 10.0 |
| Some | 46.0 | 4.739130 | 2.123255 | 2.00 | 3.2500 | 4.500 | 5.000 | 15.0 |
| Fractions of | 46.0 | 5.109783 | 16.847218 | 0.01 | 0.1625 | 0.365 | 0.875 | 100.0 |
| Several | 46.0 | 6.304348 | 2.096235 | 2.00 | 5.0000 | 7.000 | 7.750 | 10.0 |
| Dozens | 46.0 | 35.130435 | 17.119980 | 12.00 | 24.0000 | 36.000 | 36.000 | 120.0 |
| Many | 46.0 | 44.065217 | 145.464223 | 5.00 | 9.2500 | 20.000 | 25.000 | 1000.0 |
| A lot | 46.0 | 85.065217 | 431.917451 | 7.00 | 10.0000 | 14.500 | 22.250 | 2948.0 |
| Hundreds of | 46.0 | 400.804348 | 424.387015 | 100.00 | 212.5000 | 300.000 | 475.000 | 3000.0 |
| Scores of | 46.0 | 3017.456522 | 15266.869723 | 3.00 | 40.0000 | 80.000 | 200.000 | 100000.0 |
First we’ll use just one column to set the size of the errorbars around ‘mean’.
data.hvplot.scatter(y='mean', ylabel='amount') * data.hvplot.errorbars(y='mean', yerr1='std')
Then we’ll use two columns. Remember that these are not the absolute placements of the ends of the bars, but the distance from the center.
data['yerr1'] = data['mean'] - data['min']
data['yerr2'] = data['max'] - data['mean']
data.hvplot.scatter(y='mean', ylabel='amount') * data.hvplot.errorbars(y='mean', yerr1='yerr1', yerr2='yerr2')
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