Heatmap#
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import hvplot.pandas # noqa
heatmap can be data has two categorical axes. Data can either be pre-computed into a matrix, or it can be 1d and the aggregation will be computed when rendering.
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 |
In the first example, we’ll make a sea surface temperature calendar of sorts:
df.hvplot.heatmap(x='time.month', y='time.day', C='temperature',
height=500, width=500, colorbar=False)
WARNING:param.HeatMapPlot01568: HeatMap element index is not unique, ensure you aggregate the data before displaying it, e.g. using heatmap.aggregate(function=np.mean). Duplicate index values have been dropped.
If the value for each section of the heatmap is pre-computed, then use x='index' and y='columns' to plot those values. Note to see how to make this same plot in bokeh, see the bokeh docs.
from bokeh.sampledata.unemployment1948 import data
data = data.set_index('Year').drop('Annual', axis=1).transpose()
data.head()
| Year | 1948 | 1949 | 1950 | 1951 | 1952 | 1953 | 1954 | 1955 | 1956 | 1957 | ... | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jan | 4.0 | 5.0 | 7.6 | 4.4 | 3.7 | 3.4 | 5.7 | 5.8 | 4.7 | 4.9 | ... | 5.0 | 5.4 | 8.5 | 10.6 | 9.8 | 8.8 | 8.5 | 7.0 | 6.1 | 5.3 |
| Feb | 4.7 | 5.8 | 7.9 | 4.2 | 3.8 | 3.2 | 6.3 | 5.7 | 4.8 | 4.7 | ... | 4.9 | 5.2 | 8.9 | 10.4 | 9.5 | 8.7 | 8.1 | 7.0 | 5.8 | 5.2 |
| Mar | 4.5 | 5.6 | 7.1 | 3.8 | 3.3 | 2.9 | 6.4 | 5.2 | 4.7 | 4.3 | ... | 4.5 | 5.2 | 9.0 | 10.2 | 9.2 | 8.4 | 7.6 | 6.8 | 5.6 | 5.1 |
| Apr | 4.0 | 5.4 | 6.0 | 3.2 | 3.0 | 2.8 | 6.1 | 4.9 | 4.1 | 4.0 | ... | 4.3 | 4.8 | 8.6 | 9.5 | 8.7 | 7.7 | 7.1 | 5.9 | 5.1 | 4.7 |
| May | 3.4 | 5.7 | 5.3 | 2.9 | 2.9 | 2.5 | 5.7 | 4.2 | 4.2 | 3.9 | ... | 4.3 | 5.2 | 9.1 | 9.3 | 8.7 | 7.9 | 7.3 | 6.1 | 5.3 | 4.5 |
5 rows × 69 columns
data.hvplot.heatmap(
x='columns',
y='index',
title='US Unemployment 1948—2016',
cmap=["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"],
xaxis='top',
rot=70,
width=800, height=300).opts(
toolbar=None,
fontsize={'title': 10, 'xticks': 5, 'yticks': 5}
)
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Download this notebook from GitHub (right-click to download).