Heatmaps
Heatmaps
A 2D graph of matrix data and this can be tweaked using seaborn library.
The output of simple corr() can be plotted as a heatmap to understand, visually, how each column of the dataset are correlated to each other column.
annot=True:
To display the correlation values in the boxes
cmap:
Refers to the color map of the color bar
cbar=False:
Used to turn off the color bar
square=True:
makes all the boxes as squares so that the over all shape looks like a square.
linewidth and linecolor:
Used to manipulate the lines in between the grids
fmt:
Used to change the format of text in each box.
From the doc:
corr():
Compute pairwise correlation of columns, excluding NA/null values
Parameters
----------
method : {'pearson', 'kendall', 'spearman'}
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
great
ReplyDeleteThe article provides a clear introduction to heatmaps as a way of representing matrix data visually. The explanation of using a correlation matrix to understand relationships between columns makes the example particularly useful for data exploration, where visual patterns can make relationships easier to identify.
ReplyDeleteThe discussion of annot=True, cmap, cbar=False, square=True, linewidth, linecolor, and fmt is useful because it shows how a Seaborn heatmap can be customized beyond its default appearance. These parameters provide a practical foundation for learners working through a Seaborn Course.
The article also demonstrates how correlation results can be transformed into an accessible visual representation instead of being examined only as numerical output. This makes heatmaps a useful technique to explore as part of broader Data Visualization Training workflows in Python.
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