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The term "heatmap" usually refers to a Cartesian plot with data visualized as colored rectangular tiles, which is the subject of this page. It is also sometimes used to refer to actual maps with density data displayed as color intensity.
Plotly supports two different types of colored-tile heatmaps:
- Matrix Heatmaps accept a 2-dimensional matrix or array of data and visualizes it directly. This type of heatmap is the subject of this page.
- Density Heatmaps accept data as a list and visualizes aggregated quantities like counts or sums of this data. Please refer to the 2D Histogram documentation for this kind of figure.
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.
The px.imshow() function can be used to display heatmaps (as well as full-color images, as its name suggests). It accepts both array-like objects like lists of lists and numpy or xarray arrays, as well as supported DataFrame objects.
For more examples using
px.imshow, including examples of faceting and animations, as well as full-color image display, see the theimshowdocumentation page.
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()New in v5.5
You can add the values to the figure as text using the text_auto argument. Setting it to True will display the values on the bars, and setting it to a d3-format formatting string will control the output format.
import plotly.express as px
z = [[.1, .3, .5, .7, .9],
[1, .8, .6, .4, .2],
[.2, 0, .5, .7, .9],
[.9, .8, .4, .2, 0],
[.3, .4, .5, .7, 1]]
fig = px.imshow(z, text_auto=True)
fig.show()Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.
Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.
from IPython.display import IFrame
snippet_url = 'https://python-docs-dash-snippets.herokuapp.com/python-docs-dash-snippets/'
IFrame(snippet_url + 'heatmaps', width='100%', height=1200)Sign up for Dash Club → Free cheat sheets plus updates from Chris Parmer and Adam Schroeder delivered to your inbox every two months. Includes tips and tricks, community apps, and deep dives into the Dash architecture. Join now.
By default, px.imshow() produces heatmaps with square tiles, but setting the aspect argument to "auto" will instead fill the plotting area with the heatmap, using non-square tiles.
import plotly.express as px
z = [[.1, .3, .5, .7, .9],
[1, .8, .6, .4, .2],
[.2, 0, .5, .7, .9],
[.9, .8, .4, .2, 0],
[.3, .4, .5, .7, 1]]
fig = px.imshow(z, text_auto=True, aspect="auto")
fig.show()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()xarrays are labeled arrays (with labeled axes and coordinates). If you pass an xarray image to px.imshow, its axes labels and coordinates will be used for axis titles. If you don't want this behavior, you can pass img.values which is a NumPy array if img is an xarray. Alternatively, you can override axis titles hover labels and colorbar title using the labels attribute, as above.
import plotly.express as px
import xarray as xr
# Load xarray from dataset included in the xarray tutorial
airtemps = xr.tutorial.open_dataset('air_temperature').air.sel(lon=250.0)
fig = px.imshow(airtemps.T, color_continuous_scale='RdBu_r', origin='lower')
fig.show()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()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()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()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=dict(text='GitHub commits per day'),
xaxis_nticks=36)
fig.show()In this example we add text to heatmap points using texttemplate. We use the values from the text attribute for the text. We also adjust the font size using textfont.
import plotly.graph_objects as go
fig = go.Figure(data=go.Heatmap(
z=[[1, 20, 30],
[20, 1, 60],
[30, 60, 1]],
text=[['one', 'twenty', 'thirty'],
['twenty', 'one', 'sixty'],
['thirty', 'sixty', 'one']],
texttemplate="%{text}",
textfont={"size":20}))
fig.show()Arrays of rasterized values build by datashader can be visualized using plotly's heatmaps, as shown in the plotly and datashader tutorial.
See function reference for px.(imshow) or https://plotly.com/python/reference/heatmap/ for more information and chart attribute options!