.Histogram2d()

The .Histogram2d() method in Plotly’s graph_objects module creates a 2D histogram to display the joint distribution of two variables. It uses a grid where color intensity represents the count or aggregated values within each cell, making it useful for visualizing relationships and density in bivariate data.

Syntax

plotly.graph_objects.Histogram2d(x=None, y=None, nbinsx=None, nbinsy=None, colorscale=None, ...)

• x: Input data for the x-axis, representing the values plotted along the horizontal axis.
• y: Input data for the y-axis, representing the values plotted along the vertical axis.
• nbinsx (Optional): The number of bins (intervals) to use for the x-axis. If not specified (None), Plotly automatically calculates an appropriate number of bins based on the data.
• nbinsy (Optional): The number of bins (intervals) to use for the y-axis. If not specified (None), Plotly automatically calculates an appropriate number of bins based on the data.
• colorscale (Optional): Defines the color scale for bin intensities (e.g., 'Viridis', 'Blues'). You can also define custom color scales.

Note: The ellipsis in the syntax (…) indicates that there are additional optional parameters beyond those listed here to customize the scatter plot on polar axes.

Example

The following example showcases the use of the .Histogram2d():

import plotly.graph_objects as go

# Sample data
x = [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 8, 9, 9, 10, 10]
y = [10, 9, 8, 7, 6, 6, 5, 5, 4, 3, 2, 2, 1, 1, 0]

# Create the 2D histogram
fig = go.Figure(
    data=go.Histogram2d(
        x=x,
        y=y,
        colorscale='Blues',  # Color scale
        colorbar=dict(title="Frequency"),  # Customize the color bar
    )
)

# Add layout details
fig.update_layout(
    title="Example of a 2D Histogram",
    xaxis_title="X Axis",
    yaxis_title="Y Axis",
    template="plotly_white"  # Use a clean template
)

# Display the figure
fig.show()

This example demonstrates how to use plotly.graph_objects.Histogram2d to create a two-dimensional histogram and visualize it as a heatmap.

The above code generates the following output: