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Create Heatmaps using graph_objects class in Plotly

Last Updated : 05 Sep, 2020

A Plotly is a Python library that is used to design graphs, especially interactive graphs. It can plot various graphs and charts like histogram, barplot, boxplot, spreadplot, and many more. It is mainly used in data analysis as well as financial analysis. plotly is an interactive visualization library.

A heatmap is a two-dimensional graphical representation of data where the individual values that are contained in a matrix are represented as colors.

Syntax: plotly.graph_objects.Heatmap(arg=None, autocolorscale=None, colorbar=None, colorscale=None, x=None, y=None, z=None, **kwargs)

Parameters:

arg: dict of properties compatible with this constructor

autocolorscale: Determines whether the colorscale is a default palette (autocolorscale: true) or the palette determined by colorscale. 

colorbar: plotly.graph_objects.heatmap.ColorBar instance or dict with compatible properties

colorscale: Sets the colorscale. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string.

x: Sets the x coordinates.

y: Sets the y coordinates.

z: Sets the z coordinates.

Example:

import plotly.graph_objects as go

feature_x = np.arange(0, 50, 2) 
feature_y = np.arange(0, 50, 3) 

# Creating 2-D grid of features 
[X, Y] = np.meshgrid(feature_x, feature_y) 

Z = np.cos(X / 2) + np.sin(Y / 4) 

fig = go.Figure(data =
     go.Heatmap(x = feature_x, y = feature_y, z = Z,))

fig.show() 

Output:

In plotly, it is possible to show the unequal sizes of the blocks as to make the data differentiate between each other to make it more clear while presenting as the data has different height and width compared to each other.

Example:

import plotly.graph_objects as go

feature_x = np.arange(0, 50, 2)
feature_y = np.arange(0, 50, 3)

# Creating 2-D grid of features
[X, Y] = np.meshgrid(feature_x, feature_y)

Z = np.cos(X / 2) + np.sin(Y / 4)

fig = go.Figure(data=go.Heatmap(
  x=feature_x, y=feature_y, z=Z,))

fig.update_layout(
    margin=dict(t=200, r=200, b=200, l=200),
    showlegend=False,
    width=700, height=700,
    autosize=False)


fig.show()

Output:

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