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  3. Visualizing Time Series Data in Python

Exercise

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.

Instructions

100 XP
  • 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.