How to Create a Seaborn Correlation Heatmap in Python?

5 Jan 2025 | 3 min read

Visualizing the connections between variables is crucial for understanding complex datasets. Seaborn's correlation heatmaps offer an elegant way to reveal these patterns, empowering you to identify strongly correlated features and guiding feature engineering and model selection. It uncovers hidden relationships that might otherwise go unnoticed. It builds communication between correlation patterns effectively through intuitive visual representation.

Here's a step-by-step guide to creating correlation heatmaps in Python using Seaborn.

Understanding Correlation Heatmap

Firstly, we need to know what correlation heatmap is.

When it comes to exploring the relationships between variables in datasets, correlation heatmaps are an incredibly useful visualisation tool. With the help of Seaborn, a widely used Python data visualisation library, it's easy to create correlation heatmaps that offer valuable insights into data patterns. These heatmaps can help you identify strong correlations between different variables, spot outliers, and gain a deeper understanding of your data set.

A correlation heatmap is a heatmap that suggests a 2D correlation matrix among two discrete dimensions, using coloured cells to symbolise statistics from a monochromatic scale usually. The values of the primary size appear because the rows of the table at the same time as of the second one measurement as a column. The colour of the cell is proportional to the wide variety of measurements that healthy the dimensional fee. This makes correlation heatmaps ideal for information evaluation since they make patterns without problems readable and highlight the variations and variations inside identical information. A correlation heatmap, like an everyday heatmap, is assisted by a colour bar, making records without problems readable and understandable.

Before proceeding, we need to install the seaborn library in Python using the pip command.

Then, we will import all the necessary libraries for creating a correlation heatmap.

Syntax of heatmap( ) function

Here, the data is the data from which the correlation heatmap will be made. It must be passed from the corr( ) function to get the correlation of the data. The rest of the attributes are optional while creating a correlation heatmap.

Now, we will create correlation heatmaps with different data in various examples:

1. Correlation Heatmap of Random Data

Output:

How to Create a Seaborn Correlation Heatmap in Python

This code makes a correlation heatmap from random data. We used the magma colormap.

2. Correlation Heatmap of Diamonds Dataset

Output:

We imported the diamonds dataset from the sns.load_dataset( ) function. As this dataset has some string records, we must select only the numerical values as the heatmap contains only numbers. For this, we used the select_dtypes( ) function. Then, made the heatmap using the plasma colormap.

Conclusion

A simple, however effective technique for visualising the correlations among variables in a dataset is to use Python to create a Seaborn correlation heatmap. You can assist with facts evaluation and selection-making methods by using Seaborn's functionalities to obtain insights into the route and power of correlations.

Try out diverse datasets and customise Seaborn's services to check out correlations and enhance your information visualisation abilities!

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