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2D Histograms or Density Heatmaps¶

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.

Density Heatmaps with Plotly Express¶

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.

In [1]:

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.

In [2]:

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, rug, and heatmap.

In [3]:

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()

New in 7.1

marginal_x="heatmap" and marginal_y="heatmap" draw the margin as a single-row or single-column heatmap strip. Each strip uses the same aggregate and the same color scale as the main plot. See Marginal Plots for more detail.

In [4]:

import plotly.express as px
df = px.data.tips()

fig = px.density_heatmap(df, x="total_bill", y="tip", marginal_x="heatmap", marginal_y="heatmap")
fig.show()

Density heatmaps can also be faceted:

In [5]:

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()

Displaying Text¶

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.

In [6]:

import plotly.express as px
df = px.data.tips()

fig = px.density_heatmap(df, x="total_bill", y="tip", text_auto=True)
fig.show()

Other aggregation functions than `count`¶

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.

In [7]:

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()

2D Histograms with Graph Objects¶

To build this kind of figure using graph objects without using Plotly Express, we can use the go.Histogram2d class.

2D Histogram of a Bivariate Normal Distribution¶

In [8]:

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()

2D Histogram Binning and Styling Options¶

In [9]:

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()

Sharing bin settings between 2D Histograms¶

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.

In [10]:

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()

2D Histogram Overlaid with a Scatter Chart¶

In [11]:

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()

Text on 2D Histogram Points¶

In this example we add text to 2D Histogram points. We use the values from the z attribute for the text.

In [12]:

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()

Reference¶

See https://plotly.com/python/reference/histogram2d/ for more information and chart attribute options!

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter

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