Interactive Heatmap in Python (using hvPlot and Bokeh)

Renesh Bedre 1 minute read

In this article, you will learn how to create an interactive heatmap from pandas DataFrame using the hvPlot Python package.

You need to install the holoviews, hvPlot, and bioinfokit Python package for creating heatmaps. Check how to install Python packages

Get dataset for heatmap

Load gene expression dataset for plotting heatmap,

Note: If you have your own dataset, you should import it as a pandas DataFrame. Learn how to import data using pandas

from bioinfokit import analys
import holoviews as hv 
import hvplot.pandas # use hvplot directly with pandas
hv.extension('bokeh')  # to generate interactive plots 

# load example gene expression dataset as pandas dataframe
df = analys.get_data('hmap').data
df.head(2)
    Gene         A         B         C        D        E         F
0  B-CHI  4.505700  3.260360 -1.249400  8.89807  8.05955 -0.842803
1   CTL2  3.508560  1.660790 -1.856680 -2.57336 -1.37370  1.196000

# set gene names as index
df = df.set_index(df.columns[0])
df.head(2)
              A         B         C        D        E         F
Gene
B-CHI  4.505700  3.260360 -1.249400  8.89807  8.05955 -0.842803
CTL2   3.508560  1.660790 -1.856680 -2.57336 -1.37370  1.196000

Generate interactive heatmap

Create a basic interactive heatmap using hvplot.heatmap() function,

hm=df.hvplot.heatmap(cmap='seismic', width=300, height=500)
hv.save(hm, 'heatmap.html')

Get more in-built colormaps here

Change the position of X axis ticks label,

hm=df.hvplot.heatmap(cmap='RdYlGn', xaxis='top', width=300, height=400)
hv.save(hm, 'heatmap.html')

Add title to the heatmap,

hm=df.hvplot.heatmap(cmap='BrBG', xaxis='top', title='Gene expression heatmap', 
                     width=300, height=400)
hv.save(hm, 'heatmap.html')

Remove colorbar from the heatmap,

hm=df.hvplot.heatmap(cmap='PRGn', xaxis='top', title='Gene expression heatmap', 
                     colorbar=False, width=300, height=400)
hv.save(hm, 'heatmap.html')