plot histogram in seaborn
sns.distplot(gapminder['lifeExp'], kde=False, color='red', bins=100)
plt.title('Life Expectancy', fontsize=18)
plt.xlabel('Life Exp (years)', fontsize=16)
plt.ylabel('Frequency', fontsize=16)plot histogram in seaborn
sns.distplot(gapminder['lifeExp'], kde=False, color='red', bins=100)
plt.title('Life Expectancy', fontsize=18)
plt.xlabel('Life Exp (years)', fontsize=16)
plt.ylabel('Frequency', fontsize=16)plot distribution seaborn
x = np.random.normal(size=100)
sns.distplot(x);scatter density plot seaborn
>>> iris = sns.load_dataset("iris")
>>> g = sns.jointplot("sepal_width", "petal_length", data=iris,
... kind="kde", space=0, color="g")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')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()Copyright © 2021 Codeinu
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