# Create arrays for the features and the response variable y = df['party'].values X = df.drop('party', axis=1).values
# Create arrays for the features and the response variable y = df['party'].values X = df.drop('party', axis=1).values
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LinkedIn![mean =[0,0] covariance = [[1,0],[0,100]] ds = np.random.multivariate_normal(mean,covariance,500) dframe = pd.DataFrame(ds, columns=['col1', 'col2']) fig = sns.kdeplot(dframe).get_figure() fig.savefig('kde1.png')](/answers/42345/mean+%3D%5B0%2C0%5D+covariance+%3D+%5B%5B1%2C0%5D%2C%5B0%2C100%5D%5D++ds+%3D+np.random.multivariate_normal%28mean%2Ccovariance%2C500%29++dframe+%3D+pd.DataFrame%28ds%2C+columns%3D%5B%27col1%27%2C+%27col2%27%5D%29++fig+%3D+sns.kdeplot%28dframe%29.get_figure%28%29+fig.savefig%28%27kde1.png%27%29){kind=link}