scatter density plot seaborn
>>> iris = sns.load_dataset("iris")
>>> g = sns.jointplot("sepal_width", "petal_length", data=iris,
... kind="kde", space=0, color="g")
scatter density plot seaborn
>>> iris = sns.load_dataset("iris")
>>> g = sns.jointplot("sepal_width", "petal_length", data=iris,
... kind="kde", space=0, color="g")
multiple categories on distplot
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
import seaborn as sns
iris = load_iris()
iris = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
columns=iris['feature_names'] + ['target'])
# Sort the dataframe by target
target_0 = iris.loc[iris['target'] == 0]
target_1 = iris.loc[iris['target'] == 1]
target_2 = iris.loc[iris['target'] == 2]
sns.distplot(target_0[['sepal length (cm)']], hist=False, rug=True)
sns.distplot(target_1[['sepal length (cm)']], hist=False, rug=True)
sns.distplot(target_2[['sepal length (cm)']], hist=False, rug=True)
sns.plt.show()
multiple categories on distploy
sns.pairplot(iris, hue="species", height=2.5);
distplot for 2 columns
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
import seaborn as sns
iris = load_iris()
iris = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
columns=iris['feature_names'] + ['target'])
# Sort the dataframe by target
target_0 = iris.loc[iris['target'] == 0]
target_1 = iris.loc[iris['target'] == 1]
target_2 = iris.loc[iris['target'] == 2]
sns.distplot(target_0[['sepal length (cm)']], hist=False, rug=True)
sns.distplot(target_1[['sepal length (cm)']], hist=False, rug=True)
sns.distplot(target_2[['sepal length (cm)']], hist=False, rug=True)
sns.plt.show()
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