Clustered heatmaps

Heatmaps are extremely useful to visualize a correlation matrix, but clustermaps are better. A Clustermap allows to uncover structure in a correlation matrix by producing a hierarchically-clustered heatmap:

df_corr = df.corr()

fig = sns.clustermap(df_corr)
plt.setp(fig.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
plt.setp(fig.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)

To prevent overlapping of axis labels, you can reference the Axes from the underlying fig object and specify the rotation. You can learn about the arguments to the clustermap() function here.

This exercise is part of the course

Visualizing Time Series Data in Python

View Course

Exercise instructions

Import seaborn as sns.
Compute the correlation between all columns in the meat DataFrame using the Pearson method and assign the results to a new variable called corr_meat.
Plot the clustermap of corr_meat.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import seaborn library
____

# Get correlation matrix of the meat DataFrame
corr_meat = ____(____)

# Customize the heatmap of the corr_meat correlation matrix and rotate the x-axis labels
fig = ____(corr_meat,
                     row_cluster=True,
                     col_cluster=True,
                     figsize=(10, 10))

plt.setp(fig.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
plt.setp(fig.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
plt.show()

Edit and Run Code