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Plotting Heat Maps in Python Using Plotly: A Comprehensive Guide

Creating Heatmap using Plotly in Python: A Comprehensive Guide

Are you interested in visualizing 2D matrices or random data using heat maps in Python? Plotly is a powerful and versatile library that offers multiple functions to create heat maps for data analysis.

In this article, we will discuss two different functions of Plotly to create a heat map using imshow() and Heatmap() functions respectively. We will also explain how to customize them by using various parameters.

Creating Heatmap using imshow() Function of Plotly in Python

The imshow() function of Plotly is used to display a 2D matrix as a heat map. It is a simple and easy-to-use function that requires only a 2D matrix as input.

To start, let’s import Plotly and Numpy library in Python. Then, we can create a random 2D matrix using the random() function of Numpy library.

import plotly.express as px

import numpy as np

z = np.random.rand(10, 10) # Creating a random 2D Matrix of size 10×10

Now, we can create a heat map using the imshow() function by passing the 2D matrix as an argument. We can also customize the plot by adding a title, a color continuous scale, and changing the subplot layout.

fig = px.imshow(z,

color_continuous_scale=’Blues’,

title=”2D Matrix Heatmap”,

facet_col_spacing=0.05)

fig.update_layout(width=600, height=400) # Changing subplot layout

fig.show()

In the above code, we used the ‘Blues’ color continuous scale to change the color intensity of the plot. The title can be changed as per the requirement.

We also changed the subplot layout using ‘facet_col_spacing’ parameter to increase or decrease the gap between two subplots.

Creating Heatmap using Heatmap() Function of Plotly in Python

The Heatmap() function of Plotly is used to plot random data as a heat map. It requires x, y, and z-axis values as input.

To start, let’s import Plotly and Numpy library in Python. Then, we can create the x, y, and z-axis values using the random() function of Numpy library.

import plotly.graph_objs as go

import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # Creating x-axis values

y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # Creating y-axis values

z = np.random.rand(10, 10) # Creating a random 2D Matrix of size 10×10

Now, we can create a heat map using the Heatmap() function by passing the x, y, and z-axis values as arguments. We can also customize the plot by changing the color scale, opacity, and hiding hover information and color bar.

fig = go.Figure(go.Heatmap(

x=x, y=y, z=z,

colorscale=’Viridis’,

opacity=0.8,

hoverinfo=’skip’,

showscale=False

))

fig.update_layout(title=”Random Data Heatmap”) # Adding a title to the plot

fig.show()

In the above code, we used the ‘Viridis’ color continuous scale to change the color intensity of the plot. We also changed the opacity value to make the plot more transparent.

We can hide the hover information by using ‘hoverinfo’ parameter with value ‘skip’. We also removed the color bar using ‘showscale’ parameter with value ‘False’.

Conclusion

In this article, we have discussed two different functions of Plotly to create a heat map using imshow() and Heatmap() functions. We have also explained how to customize them by using various parameters.

By using these functions, we can easily visualize our data in the form of heat maps and analyze it in a better way. This article has discussed two different functions of Plotly to create heat maps in Python using imshow() and Heatmap() functions.

We have explained how to customize them by using various parameters. By utilizing these functions, we can easily represent data as heat maps and analyze it for better insights.

As a final thought, heat maps are a valuable tool in data visualization, and learning how to create and customize them is essential for anyone working with data in Python.

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