A heatmap is used to visualize the relationship between the features to analyze correlation, variance, anomalies, and various other patterns between features in a dataset. In this article, I’ll walk you through a tutorial on how to visualize a heatmap using Python.
What is a Heatmap?
Heatmaps are used to visualize data in a two-dimensional format as a coloured map so that different colour variations represent different patterns between features. A heatmap is a method of data visualization that plots data by replacing numbers with colours, making it easy for humans to understand patterns between different entities in the dataset.
Every visualization technique that we use in data science has a purpose. For example, histograms are used to understand the distribution of the dataset, pie charts are used to understand the composition of categorical features. So just like other visualization techniques, the heatmaps are used to understand the correlation between the features.
How to Analyze a Heatmap?
A heatmap visualizes the relationship between features as a colour palette, so you must be confused about how to analyze a heatmap. When analyzing a heatmap, always remember that dark shades represent a high degree of linear relationship between features and light shades represent a low degree of linear relationship between features.
I hope you now have understood what is a heatmap and how to analyze it. In the section below, I will take you through a tutorial on how to visualize a heatmap using Python.
Heatmap using Python
In Data Science, a heatmap is used to understand the relationship between different features in a dataset. It represents numbers in the form of a coloured pallet such that darker shades represent a high degree of relationship between the features and the lighter shades represent a low degree of relationship between the features. Now let’s see how to visualize a heatmap using Python.
There are many libraries in Python to visualize a heatmap but the simplest way is by using the Seaborn library in Python. Below is how to visualize a heatmap using Python:
You can see in the figure above that it represents different colour variations. We can also plot the degree of relationship (which is between -1 and 1) on the heatmap:
sns.heatmap(correlations, cmap="coolwarm", annot=True) plt.show()
So this is how we can visualize a heatmap using Python. Heatmaps are a great tool for understanding the relationships between the features in a dataset by visualizing the features in the form of a colour palette where darker colours represent a high degree of relationships and vice-versa. I hope you liked this article on how to visualize a heatmap using Python. Feel free to ask your valuable questions in the comments section below.