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How to make 2D Histograms in Python with Plotly.
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2D Histograms
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Python 2D Histograms | plotly

2D Histogram of a Bivariate Normal Distribution

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

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()

2D Histogram Overlaid with a Scatter Chart

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()

Reference

See https://plot.ly/python/reference/#histogram2d for more information and chart attribute options!