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A 2D histogram, also known as a density heatmap, is the 2-dimensional generalization of a histogram which resembles a heatmap but is computed by grouping a set of points specified by their x and y coordinates into bins, and applying an aggregation function such as count or sum (if z is provided) to compute the color of the tile representing the bin. This kind of visualization (and the related 2D histogram contour, or density contour) is often used to manage over-plotting, or situations where showing large data sets as scatter plots would result in points overlapping each other and hiding patterns. For data sets of more than a few thousand points, a better approach than the ones listed here would be to use Plotly with Datashader to precompute the aggregations before displaying the data with Plotly.
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. The Plotly Express function density_heatmap() can be used to produce density heatmaps.
import plotly.express as px
df = px.data.tips()
fig = px.density_heatmap(df, x="total_bill", y="tip")
fig.show()The number of bins can be controlled with nbinsx and nbinsy and the color scale with color_continuous_scale.
import plotly.express as px
df = px.data.tips()
fig = px.density_heatmap(df, x="total_bill", y="tip", nbinsx=20, nbinsy=20, color_continuous_scale="Viridis")
fig.show()Marginal plots can be added to visualize the 1-dimensional distributions of the two variables. Here we use a marginal histogram. Other allowable values are violin, box and rug.
import plotly.express as px
df = px.data.tips()
fig = px.density_heatmap(df, x="total_bill", y="tip", marginal_x="histogram", marginal_y="histogram")
fig.show()Density heatmaps can also be faceted:
import plotly.express as px
df = px.data.tips()
fig = px.density_heatmap(df, x="total_bill", y="tip", facet_row="sex", facet_col="smoker")
fig.show()New in v5.5
You can add the z values 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
df = px.data.tips()
fig = px.density_heatmap(df, x="total_bill", y="tip", text_auto=True)
fig.show()By passing in a z value and a histfunc, density heatmaps can perform basic aggregation operations. Here we show average Sepal Length grouped by Petal Length and Petal Width for the Iris dataset.
import plotly.express as px
df = px.data.iris()
fig = px.density_heatmap(df, x="petal_length", y="petal_width", z="sepal_length", histfunc="avg")
fig.show()To build this kind of figure using graph objects without using Plotly Express, we can use the go.Histogram2d class.
import plotly.graph_objects as go
import numpy as np
np.random.seed(1)
x = np.random.randn(500)
y = np.random.randn(500)+1
fig = go.Figure(go.Histogram2d(
x=x,
y=y
))
fig.show()import plotly.graph_objects as go
import numpy as np
x = np.random.randn(500)
y = np.random.randn(500)+1
fig = go.Figure(go.Histogram2d(x=x, y=y, histnorm='probability',
autobinx=False,
xbins=dict(start=-3, end=3, size=0.1),
autobiny=False,
ybins=dict(start=-2.5, end=4, size=0.1),
colorscale=[[0, 'rgb(12,51,131)'], [0.25, 'rgb(10,136,186)'], [0.5, 'rgb(242,211,56)'], [0.75, 'rgb(242,143,56)'], [1, 'rgb(217,30,30)']]
))
fig.show()This example shows how to use bingroup attribute to have a compatible bin settings for both histograms. To define start, end and size value of x-axis and y-axis separately, set ybins and xbins.
import plotly.graph_objects as go
from plotly.subplots import make_subplots
fig = make_subplots(2,2)
fig.add_trace(go.Histogram2d(
x = [ 1, 2, 2, 3, 4 ],
y = [ 1, 2, 2, 3, 4 ],
coloraxis = "coloraxis",
xbins = {'start':1, 'size':1}), 1,1)
fig.add_trace(go.Histogram2d(
x = [ 4, 5, 5, 5, 6 ],
y = [ 4, 5, 5, 5, 6 ],
coloraxis = "coloraxis",
ybins = {'start': 3, 'size': 1}),1,2)
fig.add_trace(go.Histogram2d(
x = [ 1, 2, 2, 3, 4 ],
y = [ 1, 2, 2, 3, 4 ],
bingroup = 1,
coloraxis = "coloraxis",
xbins = {'start':1, 'size':1}), 2,1)
fig.add_trace(go.Histogram2d(
x = [ 4, 5, 5, 5, 6 ],
y = [ 4, 5, 5, 5, 6 ],
bingroup = 1,
coloraxis = "coloraxis",
ybins = {'start': 3, 'size': 1}),2,2)
fig.show()import plotly.graph_objects as go
import numpy as np
x0 = np.random.randn(100)/5. + 0.5 # 5. enforces float division
y0 = np.random.randn(100)/5. + 0.5
x1 = np.random.rand(50)
y1 = np.random.rand(50) + 1.0
x = np.concatenate([x0, x1])
y = np.concatenate([y0, y1])
fig = go.Figure()
fig.add_trace(go.Scatter(
x=x0,
y=y0,
mode='markers',
showlegend=False,
marker=dict(
symbol='x',
opacity=0.7,
color='white',
size=8,
line=dict(width=1),
)
))
fig.add_trace(go.Scatter(
x=x1,
y=y1,
mode='markers',
showlegend=False,
marker=dict(
symbol='circle',
opacity=0.7,
color='white',
size=8,
line=dict(width=1),
)
))
fig.add_trace(go.Histogram2d(
x=x,
y=y,
colorscale='YlGnBu',
zmax=10,
nbinsx=14,
nbinsy=14,
zauto=False,
))
fig.update_layout(
xaxis=dict( ticks='', showgrid=False, zeroline=False, nticks=20 ),
yaxis=dict( ticks='', showgrid=False, zeroline=False, nticks=20 ),
autosize=False,
height=550,
width=550,
hovermode='closest',
)
fig.show()In this example we add text to 2D Histogram points. We use the values from the z attribute for the text.
import plotly.graph_objects as go
from plotly import data
df = data.tips()
fig = go.Figure(go.Histogram2d(
x=df.total_bill,
y=df.tip,
texttemplate= "%{z}"
))
fig.show()See https://plotly.com/python/reference/histogram2d/ for more information and chart attribute options!