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.

Deze oefening maakt deel uit van de cursus

Visualizing Time Series Data in Python

Cursus bekijken

Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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()

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