which plots are good for bivariate distribution seaborn code example

Example 1: plot distribution seaborn

x = np.random.normal(size=100)
sns.distplot(x);

Example 2: scatter density plot seaborn

>>> iris = sns.load_dataset("iris")
>>> g = sns.jointplot("sepal_width", "petal_length", data=iris,
...                   kind="kde", space=0, color="g")

Example 3: 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')

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')