heatmaps.md

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Heatmap with plotly.express and px.imshow

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. With px.imshow, each value of the input array or data frame is represented as a heatmap pixel.

For more examples using px.imshow, see the tutorial on displaying image data with plotly.

import plotly.express as px

fig = px.imshow([[1, 20, 30],
                 [20, 1, 60],
                 [30, 60, 1]])
fig.show()

import plotly.express as px

df = px.data.medals_wide(indexed=True)
fig = px.imshow(df)
fig.show()

Customizing the axes and labels on a heatmap

You can use the x, y and labels arguments to customize the display of a heatmap, and use .update_xaxes() to move the x axis tick labels to the top:

import plotly.express as px
data=[[1, 25, 30, 50, 1], [20, 1, 60, 80, 30], [30, 60, 1, 5, 20]]
fig = px.imshow(data,
                labels=dict(x="Day of Week", y="Time of Day", color="Productivity"),
                x=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
                y=['Morning', 'Afternoon', 'Evening']
               )
fig.update_xaxes(side="top")
fig.show()

Basic Heatmap with plotly.graph_objects

If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Heatmap class from plotly.graph_objects.

import plotly.graph_objects as go

fig = go.Figure(data=go.Heatmap(
                    z=[[1, 20, 30],
                      [20, 1, 60],
                      [30, 60, 1]]))
fig.show()

Heatmap with Categorical Axis Labels

In this example we also show how to ignore hovertext when we have missing values in the data by setting the hoverongaps to False.

import plotly.graph_objects as go

fig = go.Figure(data=go.Heatmap(
                   z=[[1, None, 30, 50, 1], [20, 1, 60, 80, 30], [30, 60, 1, -10, 20]],
                   x=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
                   y=['Morning', 'Afternoon', 'Evening'],
                   hoverongaps = False))
fig.show()

Heatmap with Unequal Block Sizes

import plotly.graph_objects as go
import numpy as np

# Build the rectangles as a heatmap
# specify the edges of the heatmap squares
phi = (1 + np.sqrt(5) )/2. # golden ratio
xe = [0, 1, 1+(1/(phi**4)), 1+(1/(phi**3)), phi]
ye = [0, 1/(phi**3), 1/phi**3+1/phi**4, 1/(phi**2), 1]

z = [ [13,3,3,5],
      [13,2,1,5],
      [13,10,11,12],
      [13,8,8,8]
    ]

fig = go.Figure(data=go.Heatmap(
          x = np.sort(xe),
          y = np.sort(ye),
          z = z,
          type = 'heatmap',
          colorscale = 'Viridis'))

# Add spiral line plot

def spiral(th):
    a = 1.120529
    b = 0.306349
    r = a*np.exp(-b*th)
    return (r*np.cos(th), r*np.sin(th))

theta = np.linspace(-np.pi/13,4*np.pi,1000); # angle
(x,y) = spiral(theta)

fig.add_trace(go.Scatter(x= -x+x[0], y= y-y[0],
     line =dict(color='white',width=3)))

axis_template = dict(range = [0,1.6], autorange = False,
             showgrid = False, zeroline = False,
             linecolor = 'black', showticklabels = False,
             ticks = '' )

fig.update_layout(margin = dict(t=200,r=200,b=200,l=200),
    xaxis = axis_template,
    yaxis = axis_template,
    showlegend = False,
    width = 700, height = 700,
    autosize = False )

fig.show()

Heatmap with Datetime Axis

import plotly.graph_objects as go
import datetime
import numpy as np
np.random.seed(1)

programmers = ['Alex','Nicole','Sara','Etienne','Chelsea','Jody','Marianne']

base = datetime.datetime.today()
dates = base - np.arange(180) * datetime.timedelta(days=1)
z = np.random.poisson(size=(len(programmers), len(dates)))

fig = go.Figure(data=go.Heatmap(
        z=z,
        x=dates,
        y=programmers,
        colorscale='Viridis'))

fig.update_layout(
    title='GitHub commits per day',
    xaxis_nticks=36)

fig.show()

Heatmap and datashader

Arrays of rasterized values build by datashader can be visualized using plotly's heatmaps, as shown in the plotly and datashader tutorial.

Reference

See https://plotly.com/python/reference/heatmap/ for more information and chart attribute options!