import matplotlib.pyplot as plt
import seaborn as sns
figure = plt.figure(figsize=(12, 6))
sns.heatmap(train_data.corr(), annot=True,cmap=plt.cm.cool)
plt.tight_layout()
plt.xlabel('Corr')
plt.show()import seaborn as sns
%matplotlib inline
# calculate the correlation matrix
corr = auto_df.corr()
# plot the heatmap
sns.heatmap(corr,
xticklabels=corr.columns,
yticklabels=corr.columns)
import matplotlib.pyplot as plt
import seaborn as sns
figure = plt.figure(figsize=(12, 6))
sns.heatmap(df.corr(), annot=True,cmap=plt.cm.cool)
plt.tight_layout()
plt.xlabel('Corr')
plt.show()sns.heatmap(table2,annot=True,cmap='Blues', fmt='g')