Visualize correlation matrices

The correlation matrix generated in the previous exercise can be plotted using a heatmap. To do so, you can leverage the heatmap() function from the seaborn library which contains several arguments to tailor the look of your heatmap.

df_corr = df.corr()

sns.heatmap(df_corr)
plt.xticks(rotation=90)
plt.yticks(rotation=0)

You can use the .xticks() and .yticks() methods to rotate the axis labels so they don't overlap.

To learn about the arguments to the heatmap() function, refer to this page.

Bu egzersiz

Visualizing Time Series Data in Python

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

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

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import seaborn library
import ____ as ____

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

# Customize the heatmap of the corr_meat correlation matrix
____(corr_meat,
            annot=True,
            linewidths=0.4,
            annot_kws={"size": 10})

plt.xticks(rotation=90)
plt.yticks(rotation=0) 
plt.show()

Kodu Düzenle ve Çalıştır