Uploaded June 2022 | Updated September 2026, 2 weeks ago
Let’s make heatmaps of correlation matrix with Python and Seaborn better. Half of a heatmap ıs useless. Anythıng dıstractıng should be oblıterated from a plot. Duplıcate cells of a heatmap are certaınly such a case as they appear to hold valuable ınformatıon.
This video demonstrates how to get rid of that clutter. To make this demonstration as simple as possible, I used two Jupyter notebooks. One left side I am showing a standard way how to build a Seaborn heatmap in a standard way.
One disadvantage on the left example that the generated heatmap represents duplicated values of calculated correlation coefficients in the perspective of diagonal line. You will see the view of this in the intro of this video.
On the right side, there is the improved technique how to visualize a heatmap of correlation matrixes. But without duplicated values we had on the left examples! For this improvement we used two additional lines of Python codes which we used to set-up a mask needed for this improvement.
1. numpy.triu_indices_from: Return the indices for the upper-triangle of arr. (numpy.org/doc/stable/reference/generated/numpy.triu_indices_from.html)
2. numpy.zeros_like: Return an array of zeros with the same shape and type as a given array (numpy.org/doc/stable/reference/generated/numpy.zeros_like.html).
Example Notebooks used in this video: github.com/vb100/corr_matrix_sns_experiment
The official Seaborn documentation: seaborn.pydata.org/tutorial/aesthetics.html
Generate correlation matrix with Pandas - pd.corr(): pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html
I hope this way to display heatmaps in Python codes will help for data scientists and data analytics in their daily work with real world project!
#heatmap #seaborn #correlationmatrix
Let’s make heatmaps of correlation matrix with Python and Seaborn better. Half of a heatmap ıs useless. Anythıng dıstractıng should be oblıterated from a plot. Duplıcate cells of a heatmap are certaınly such a case as they appear to hold valuable ınformatıon.
This video demonstrates how to get rid of that clutter. To make this demonstration as simple as possible, I used two Jupyter notebooks. One left side I am showing a standard way how to build a Seaborn heatmap in a standard way.
One disadvantage on the left example that the generated heatmap represents duplicated values of calculated correlation coefficients in the perspective of diagonal line. You will see the view of this in the intro of this video.
On the right side, there is the improved technique how to visualize a heatmap of correlation matrixes. But without duplicated values we had on the left examples! For this improvement we used two additional lines of Python codes which we used to set-up a mask needed for this improvement.
1. numpy.triu_indices_from: Return the indices for the upper-triangle of arr. (numpy.org/doc/stable/reference/generated/numpy.triu_indices_from.html)
2. numpy.zeros_like: Return an array of zeros with the same shape and type as a given array (numpy.org/doc/stable/reference/generated/numpy.zeros_like.html).
Example Notebooks used in this video: github.com/vb100/corr_matrix_sns_experiment
The official Seaborn documentation: seaborn.pydata.org/tutorial/aesthetics.html
Generate correlation matrix with Pandas - pd.corr(): pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html
I hope this way to display heatmaps in Python codes will help for data scientists and data analytics in their daily work with real world project!
#heatmap #seaborn #correlationmatrix
